CRD 37 Agricolae

User Manual: CRD-37

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Package ‘agricolae’
September 12, 2017
Type Package
Title Statistical Procedures for Agricultural Research
Version 1.2-8
Date 2017-09-12
Author Felipe de Mendiburu
Maintainer Felipe de Mendiburu <fmendiburu@lamolina.edu.pe>
Imports klaR, MASS, nlme, cluster, spdep, AlgDesign, graphics
Description Original idea was presented in the thesis ``A statistical analysis tool for agricultural re-
search'' to obtain the degree of Master on science, National Engineering University (UNI), Lima-
Peru. Some experimental data for the examples come from the CIP and others research. Agrico-
lae offers extensive functionality on experimental design especially for agricul-
tural and plant breeding experiments, which can also be useful for other purposes. It sup-
ports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-
Latin Squares, augmented block, factorial, split and strip plot designs. There are also vari-
ous analysis facilities for experimental data, e.g. treatment comparison procedures and sev-
eral non-parametric tests comparison, biodiversity indexes and consensus cluster.
License GPL
URL http://tarwi.lamolina.edu.pe/~fmendiburu
NeedsCompilation no
Depends R (>= 2.10)
Repository CRAN
Date/Publication 2017-09-12 20:38:31 UTC
Rtopics documented:
agricolae-package...................................... 4
AMMI............................................ 5
AMMI.contour ....................................... 7
audpc ............................................ 8
audps ............................................ 10
bar.err ............................................ 11
1
2Rtopics documented:
bar.group .......................................... 13
BIB.test ........................................... 14
carolina ........................................... 16
Chz2006........................................... 17
CIC ............................................. 18
clay ............................................. 19
ComasOxapampa...................................... 20
consensus .......................................... 21
corn ............................................. 22
correl ............................................ 23
correlation.......................................... 24
cotton ............................................ 26
cv.model........................................... 27
cv.similarity......................................... 28
DAU.test .......................................... 29
DC.............................................. 30
delete.na........................................... 31
design.ab .......................................... 32
design.alpha......................................... 34
design.bib.......................................... 35
design.crd.......................................... 37
design.cyclic ........................................ 38
design.dau.......................................... 40
design.graeco ........................................ 41
design.lattice ........................................ 43
design.lsd .......................................... 44
design.rcbd ......................................... 45
design.split ......................................... 47
design.strip ......................................... 48
design.youden........................................ 49
diffograph.......................................... 51
disease............................................ 52
duncan.test ......................................... 53
durbin.test.......................................... 54
friedman........................................... 56
frijol............................................. 57
genxenv ........................................... 58
Glycoalkaloids ....................................... 59
graph.freq.......................................... 59
grass............................................. 61
greenhouse ......................................... 62
growth............................................ 63
haynes............................................ 64
Hco2006........................................... 65
hcut ............................................. 66
heterosis........................................... 67
hgroups ........................................... 68
HSD.test........................................... 69
Rtopics documented: 3
huasahuasi.......................................... 70
index.AMMI ........................................ 72
index.bio .......................................... 73
index.smith ......................................... 74
intervals.freq ........................................ 75
join.freq........................................... 76
kendall............................................ 77
kruskal............................................ 78
kurtosis ........................................... 79
lastC............................................. 80
lateblight .......................................... 80
lineXtester.......................................... 83
LSD.test........................................... 85
LxT ............................................. 86
markers ........................................... 87
Median.test ......................................... 88
melon ............................................ 89
montecarlo ......................................... 90
natives............................................ 91
nonadditivity ........................................ 92
normal.freq ......................................... 93
ogive.freq .......................................... 94
order.group ......................................... 95
orderPvalue ......................................... 96
pamCIP ........................................... 97
paracsho........................................... 98
path.analysis......................................... 99
PBIB.test ..........................................100
plot.AMMI .........................................102
plot.graph.freq........................................104
plot.group..........................................106
plots.............................................107
plrv .............................................108
polygon.freq.........................................109
potato ............................................110
ralstonia...........................................110
reg.homog..........................................111
REGW.test .........................................113
resampling.cv........................................114
resampling.model......................................115
rice .............................................117
RioChillon..........................................118
scheffe.test .........................................119
similarity ..........................................120
simulation.model ......................................121
sinRepAmmi ........................................122
skewness ..........................................123
SNK.test...........................................124
4agricolae-package
soil..............................................125
sp.plot............................................127
ssp.plot ...........................................128
stability.nonpar .......................................129
stability.par .........................................130
stat.freq ...........................................132
strip.plot...........................................133
sturges.freq .........................................134
summary.graph.freq.....................................135
sweetpotato .........................................136
table.freq ..........................................137
tapply.stat ..........................................138
vark .............................................139
waerden.test.........................................140
waller ............................................141
waller.test ..........................................142
weatherSeverity.......................................144
wilt .............................................145
yacon ............................................147
zigzag............................................148
Index 150
agricolae-package Statistical Procedures for Agricultural Research
Description
This package contains functionality for the Statistical Analysis of experimental designs applied
specially for field experiments in agriculture and plant breeding.
Details
Package: agricolae
Type: Package
Version: 1.2-8
Date: 2017-09-12
License: GPL
Planning of field experiments: lattice, factorial, RCBD, CRD, Latin Square, Youden, Graeco, BIB,
Alpha design, Cyclic, augmented block, split and strip plot Designs. Comparison of multi-location
trials: AMMI, Index AMMI Stability (biplot, triplot), comparison between treatments: LSD, Bon-
ferroni and other p-adjust, HSD, Waller, Student Newman Keuls SNK, Duncan, REGW, Scheffe;
Non parametric tests: Kruskal, Friedman, Durbin, Van Der Waerden, Median. Analysis of genetic
experiments: North Carolina designs, LinexTester, Balanced Incomplete Block, Strip plot, Split-
Plot, Partially Balanced Incomplete Block, analysis Mother and baby trials (see data RioChillon).
AMMI 5
Resampling and simulation: resampling.model, simulation.model, montecarlo, lateblight Simulator
for potato. Ecology: Biodiversity Index, Path Analysis. Soil Uniformity: Smith’s Index. Cluster
Analysis: Consensus Cluster. Descriptive statistics utilities: *.freq
Author(s)
Felipe de Mendiburu Statistical Engineer Master in Systems Engineering Professor of Applied
Statistics
Maintainer: Felipe de Mendiburu <fmendiburu@lamolina.edu.pe>
References
De Mendiburu, Felipe (2009). Una herramienta de analisis estadistico para la investigacion agricola.
Tesis. Universidad Nacional de Ingenieria (UNI-PERU).
Universidad Nacional Agraria La Molina, Lima-PERU. Facultad de Economia y Planificacion De-
partamento Academico de Estadistica e Informatica
AMMI AMMI Analysis
Description
Additive Main Effects and Multiplicative Interaction Models (AMMI) are widely used to analyze
main effects and genotype by environment (GEN, ENV) interactions in multilocation variety trials.
Furthermore, this function generates data to biplot, triplot graphs and analysis.
Usage
AMMI(ENV, GEN, REP, Y, MSE = 0,console=FALSE,PC=FALSE)
Arguments
ENV Environment
GEN Genotype
REP Replication
YResponse
MSE Mean Square Error
console ouput TRUE or FALSE
PC Principal components ouput TRUE or FALSE
Details
additional graphics see help(plot.AMMI).
6AMMI
Value
ANOVA analysis of variance general
genXenv class by, genopyte and environment
analysis analysis of variance principal components
means average genotype and environment
biplot data to produce graphics
PC class princomp
Author(s)
F. de Mendiburu
References
Crossa, J. 1990. Statistical analysis of multilocation trials. Advances in Agronomy 44:55-85
See Also
lineXtester,plot.AMMI
Examples
# Full replications
library(agricolae)
# Example 1
data(plrv)
model<- with(plrv,AMMI(Locality, Genotype, Rep, Yield, console=FALSE))
model$ANOVA
# see help(plot.AMMI)
# biplot
plot(model)
# triplot PC 1,2,3
plot(model, type=2, number=TRUE)
# biplot PC1 vs Yield
plot(model, first=0,second=1, number=TRUE)
# Example 2
data(CIC)
data1<-CIC$comas[,c(1,6,7,17,18)]
data2<-CIC$oxapampa[,c(1,6,7,19,20)]
cic <- rbind(data1,data2)
model<-with(cic,AMMI(Locality, Genotype, Rep, relative))
model$ANOVA
plot(model,0,1,angle=20,ecol="brown")
# Example 3
# Only means. Mean square error is well-known.
data(sinRepAmmi)
REP <- 3
MSerror <- 93.24224
#startgraph
model<-with(sinRepAmmi,AMMI(ENV, GEN, REP, YLD, MSerror,PC=TRUE))
AMMI.contour 7
# print anova
print(model$ANOVA,na.print = "")
# Biplot with the one restored observed.
plot(model,0,1,type=1)
# with principal components model$PC is class "princomp"
pc<- model$PC
pc$loadings
summary(pc)
biplot(pc)
# Principal components by means of the covariance similar AMMI
# It is to compare results with AMMI
cova<-cov(model$genXenv)
values<-eigen(cova)
total<-sum(values$values)
round(values$values*100/total,2)
# AMMI: 64.81 18.58 13.50 3.11 0.00
AMMI.contour AMMI contour
Description
Draws a polygon or a circumference around the center of the Biplot with a proportional radio at the
longest distance of the genotype.
Usage
AMMI.contour(model, distance, shape, ...)
Arguments
model Object
distance Circumference radius >0 and <=1
shape Numerical, relating to the shape of the polygon outline.
... Parameters corresponding to the R lines function
Details
First, it is necessary to execute the AMMI function. It is only valid for the BIPLOT function but
not for the TRIPLOT one.
Value
Genotypes within and outside the area.
distance Distance from genotype to origin (0,0)
8audpc
Note
Complement graphics AMMI
Author(s)
Felipe de Mendiburu
See Also
AMMI
Examples
library(agricolae)
# see AMMI.
data(sinRepAmmi)
Environment <- sinRepAmmi$ENV
Genotype <- sinRepAmmi$GEN
Yield <- sinRepAmmi$YLD
REP <- 3
MSerror <- 93.24224
model<-AMMI(Environment, Genotype, REP, Yield, MSerror)
plot(model)
AMMI.contour(model,distance=0.7,shape=8,col="red",lwd=2,lty=5)
audpc Calculating the absolute or relative value of the AUDPC
Description
Area Under Disease Progress Curve. The AUDPC measures the disease throughout a period. The
AUDPC is the area that is determined by the sum of trapezes under the curve.
Usage
audpc(evaluation, dates, type = "absolute")
Arguments
evaluation Table of data of the evaluations: Data frame
dates Vector of dates corresponding to each evaluation
type relative, absolute
Details
AUDPC. For the illustration one considers three evaluations (14, 21 and 28 days) and percentage of
damage in the plant 40, 80 and 90 (interval between dates of evaluation 7 days). AUDPC = 1045.
The evaluations can be at different interval.
audpc 9
Value
Vector with relative or absolute audpc.
Author(s)
Felipe de Mendiburu
References
Campbell, C. L., L. V. Madden. (1990): Introduction to Plant Disease Epidemiology. John Wiley
& Sons, New York City.
Examples
library(agricolae)
dates<-c(14,21,28) # days
# example 1: evaluation - vector
evaluation<-c(40,80,90)
audpc(evaluation,dates)
# example 2: evaluation: dataframe nrow=1
evaluation<-data.frame(E1=40,E2=80,E3=90) # percentages
plot(dates,evaluation,type="h",ylim=c(0,100),col="red",axes=FALSE)
title(cex.main=0.8,main="Absolute or Relative AUDPC\nTotal area = 100*(28-14)=1400")
lines(dates,evaluation,col="red")
text(dates,evaluation+5,evaluation)
text(18,20,"A = (21-14)*(80+40)/2")
text(25,60,"B = (28-21)*(90+80)/2")
text(25,40,"audpc = A+B = 1015")
text(24.5,33,"relative = audpc/area = 0.725")
abline(h=0)
axis(1,dates)
axis(2,seq(0,100,5),las=2)
lines(rbind(c(14,40),c(14,100)),lty=8,col="green")
lines(rbind(c(14,100),c(28,100)),lty=8,col="green")
lines(rbind(c(28,90),c(28,100)),lty=8,col="green")
# It calculates audpc absolute
absolute<-audpc(evaluation,dates,type="absolute")
print(absolute)
rm(evaluation, dates, absolute)
# example 3: evaluation dataframe nrow>1
data(disease)
dates<-c(1,2,3) # week
evaluation<-disease[,c(4,5,6)]
# It calculates audpc relative
index <-audpc(evaluation, dates, type = "relative")
# Correlation between the yield and audpc
correlation(disease$yield, index, method="kendall")
# example 4: days infile
data(CIC)
comas <- CIC$comas
oxapampa <- CIC$oxapampa
dcomas <- names(comas)[9:16]
10 audps
days<- as.numeric(substr(dcomas,2,3))
AUDPC<- audpc(comas[,9:16],days)
relative<-audpc(comas[,9:16],days,type = "relative")
h1<-graph.freq(AUDPC,border="red",density=4,col="blue")
table.freq(h1)
h2<-graph.freq(relative,border="red",density=4,col="blue",
frequency=2, ylab="relative frequency")
audps The Area Under the Disease Progress Stairs
Description
A better estimate of disease progress is the area under the disease progress stairs (AUDPS). The
AUDPS approach improves the estimation of disease progress by giving a weight closer to optimal
to the first and last observations.
Usage
audps(evaluation, dates, type = "absolute")
Arguments
evaluation Table of data of the evaluations: Data frame
dates Vector of dates corresponding to each evaluation
type relative, absolute
Details
AUDPS. For the illustration one considers three evaluations (14, 21 and 28 days) and percentage
of damage in the plant 40, 80 and 90 (interval between dates of evaluation 7 days). AUDPS =
1470. The evaluations can be at different interval. AUDPS= sum( rectangle area by interval in
times evaluation ) see example.
Value
Vector with relative or absolute audps.
Author(s)
Felipe de Mendiburu
References
Ivan Simko, and Hans-Peter Piepho, (2012). The area under the disease progress stairs: Calculation,
advantage, and application. Phytopathology 102:381- 389.
bar.err 11
Examples
library(agricolae)
dates<-c(14,21,28) # days
# example 1: evaluation - vector
evaluation<-c(40,80,90)
audps(evaluation,dates)
audps(evaluation,dates,"relative")
x<-seq(10.5,31.5,7)
y<-c(40,80,90,90)
plot(x,y,"s",ylim=c(0,100),xlim=c(10,32),axes=FALSE,col="red" ,ylab="",xlab="")
title(cex.main=0.8,main="Absolute or Relative AUDPS\nTotal area=(31.5-10.5)*100=2100",
ylab="evaluation",xlab="dates" )
points(x,y,type="h")
z<-c(14,21,28)
points(z,y[-3],col="blue",lty=2,pch=19)
points(z,y[-3],col="blue",lty=2,pch=19)
axis(1,x,pos=0)
axis(2,c(0,40,80,90,100),las=2)
text(dates,evaluation+5,dates,col="blue")
text(14,20,"A = (17.5-10.5)*40",cex=0.8)
text(21,40,"B = (24.5-17.5)*80",cex=0.8)
text(28,60,"C = (31.5-24.5)*90",cex=0.8)
text(14,95,"audps = A+B+C = 1470")
text(14,90,"relative = audps/area = 0.7")
# It calculates audpc absolute
absolute<-audps(evaluation,dates,type="absolute")
print(absolute)
rm(evaluation, dates, absolute)
bar.err Plotting the standard error or standard deviance of a multiple com-
parison of means
Description
It plots bars of the averages of treatments and standard error or standard deviance. It uses the objects
generated by a procedure of comparison like LSD, HSD, Kruskal and Waller-Duncan.
Usage
bar.err(x,variation=c("SE","SD","range","IQR"),horiz=FALSE, bar=TRUE,...)
Arguments
xobject means of the comparisons the LSD.test, HSD.test,...,etc
variation SE=standard error, range=Max-Min or IQR=interquartil range
horiz Horizontal or vertical bars
bar paint bar
... Parameters of the function barplot()
12 bar.err
Details
x: data frame formed by 5 columns: name of the bars, height, level out: LSD.test, HSD, waller.test,
scheffe.test, duncan.test, SNK.test, friedman, kruskal, waerden.test and Median.test.
Value
A list with numeric vectors giving the coordinates of all the bar midpoints drawn.
xeje-1 coordinate
height eje-2 coordinate by group
Author(s)
Felipe de Mendiburu
See Also
LSD.test,HSD.test,waller.test,kruskal,bar.group
Examples
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
out <- waller.test(model,"virus", console=TRUE,
main="Yield of sweetpotato\ndealt with different virus")
par(mfrow=c(2,2),cex=1)
bar.err(out$means,variation="range",horiz=TRUE,xlim=c(0,45),angle=125,density=6,
main="range")
bar.err(out$means,variation="SD",ylim=c(0,45),col=colors()[30],
main="Standard deviation",density=8)
bar.err(out$means,variation="SE",horiz=TRUE,xlim=c(0,45),density=8,
col="brown",main="Standard error")
bar.err(out$means,variation="range",ylim=c(0,45),bar=FALSE,col="green",
main="range")
par(mfrow=c(1,2),cex=1)
bar.err(out$means,variation="range",ylim=c(0,45),bar=FALSE,col=0)
abline(h=0)
# horiz = TRUE
bar.err(out$means,variation="SE",horiz=TRUE,xlim=c(0,45),bar=FALSE,col=0)
#startgraph
par(mfrow=c(1,1))
#endgraph
bar.group 13
bar.group Plotting the multiple comparison of means
Description
It plots bars of the averages of treatments to compare. It uses the objects generated by a procedure
of comparison like LSD, HSD, Kruskall, Waller-Duncan, Friedman or Durbin. It can also display
the ’average’ value over each bar in a bar chart.
Usage
bar.group(x, horiz = FALSE, ...)
Arguments
xObject created by a test of comparison
horiz Horizontal or vertical bars
... Parameters of the function barplot()
Details
x: data frame formed by 5 columns: name of the bars, height and level of the bar.
Value
A list with numeric vectors giving the coordinates of all the bar midpoints drawn.
xeje-1 coordinate
height eje-2 coordinate by group
Author(s)
Felipe de Meniburu
See Also
LSD.test,HSD.test,kruskal ,friedman,durbin.test,waller.test ,plot.group
Examples
# Example 1
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
comparison<- LSD.test(model,"virus",alpha=0.01,group=TRUE)
print(comparison$groups)
#startgraph
par(cex=1.5)
14 BIB.test
bar.group(comparison$groups,horiz=TRUE,density=8,col="blue",border="red",
xlim=c(0,50),las=1)
title(cex.main=0.8,main="Comparison between\ntreatment means",xlab="Yield",ylab="Virus")
#endgraph
# Example 2
library(agricolae)
x <- 1:4
y <- c(0.29, 0.44, 0.09, 0.49)
xy <- data.frame(x,y,y)
#startgraph
par(cex=1.5)
bar.group(xy,density=30,angle=90,col="brown",border=FALSE,ylim=c(0,0.6),lwd=2,las=1)
#endgraph
BIB.test Finding the Variance Analysis of the Balanced Incomplete Block De-
sign
Description
Analysis of variance BIB and comparison mean adjusted.
Usage
BIB.test(block, trt, y, test = c("lsd","tukey","duncan","waller","snk"),
alpha = 0.05, group = TRUE,console=FALSE)
Arguments
block blocks
trt Treatment
yResponse
test Comparison treatments
alpha Significant test
group logical
console logical, print output
Details
Test of comparison treatment. lsd: Least significant difference. tukey: Honestly significant differ-
ente. duncan: Duncan’s new multiple range test waller: Waller-Duncan test. snk: Student-Newman-
Keuls (SNK)
BIB.test 15
Value
parameters Design parameters
statistics Statistics of the model
comparison Comparison between treatments
means Adjusted mean and statistics summary
groups Grouping of treatments
Author(s)
F. de Mendiburu
References
Design of Experiments. Robert O. Kuehl. 2nd ed., Duxbury, 2000 Linear Estimation and Design of
Experiments. D.D. Joshi. WILEY EASTERN LIMITED 1987, New Delhi, India. Introduction to
experimental statistics. Ching Chun Li McGraw - Hill Book Company, Inc. New York. 1964
See Also
DAU.test,duncan.test,durbin.test,friedman,HSD.test,kruskal,LSD.test,Median.test,
PBIB.test,REGW.test,scheffe.test,SNK.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
# Example Design of Experiments. Robert O. Kuehl. 2da. Edicion. 2001
run<-gl(10,3)
psi<-c(250,325,475,250,475,550,325,400,550,400,475,550,325,475,550,
250,400,475,250,325,400,250,400,550,250,325,550,325,400,475)
monovinyl<-c(16,18,32,19,46,45,26,39,61,21,35,55,19,47,48,20,33,31,13,13,34,21,
30,52,24,10,50,24,31,37)
out<-BIB.test(run,psi,monovinyl,test="waller",group=FALSE)
print(out)
bar.err(out$means,variation="range",ylim=c(0,60),bar=FALSE,col=0)
out<-BIB.test(run,psi,monovinyl,test="waller",group=TRUE)
out<-BIB.test(run,psi,monovinyl,test="tukey",group=TRUE,console=TRUE)
out<-BIB.test(run,psi,monovinyl,test="tukey",group=FALSE,console=TRUE)
rm(run,psi,monovinyl,out)
# Example linear estimation and design of experiments. D.D. Joshi. 1987
# Professor of Statistics, Institute of Social Sciences Agra, India
# 6 varieties of wheat crop in a BIB whit 10 blocks of 3 plots each.
y <-c(69,77,72,63,70,54,65,65,57,59,50,45,68,75,59,38,60,60,62,
55,54,65,62,65,61,39,54,67,63,56)
varieties<-gl(6,5)
block <- c(1,2,3,4,5,1,2,6,7,8,1,3,6,9,10,2,4,7,9,10,3,5,7,8,9,4,5,6,8,10)
BIB.test(block, varieties, y)
# Example Introduction to experimental statistics. Ching Chun Li. 1964
# pag. 395 table. 27.2
# 7 trt, k=3 and b=7.
y <-c(10,15,11,4,12,15,5,14,10,14,19,19,8,10,17,6,11,12,5,14,21)
16 carolina
block<-gl(7,3)
trt <- c(1,2,4,2,3,5,3,4,6,4,5,7,1,5,6,2,6,7,1,3,7)
out<-BIB.test(block, trt, y, test="duncan")
bar.group(out$groups,col="blue",density=4,ylim=c(0,max(y)))
rm(y,block,trt,out)
carolina North Carolina Designs I, II and III
Description
Statistic analysis of the Carolina I, II and III genetic designs.
Usage
carolina(model,data)
Arguments
model Constant
data Data frame
Details
model = 1,2 and 3 is I, II and III see carolina1,2 and 3.
Value
model model analysis (I, II or III) of caroline design
and variance and additive variance of male, female and male.female interaction.
Author(s)
Felipe de Mendiburu
References
Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979
See Also
DC
Chz2006 17
Examples
library(agricolae)
data(DC)
carolina1 <- DC$carolina1
# str(carolina1)
output<-carolina(model=1,carolina1)
output[][-1]
carolina2 <- DC$carolina2
# str(carolina2)
majes<-subset(carolina2,carolina2[,1]==1)
majes<-majes[,c(2,5,4,3,6:8)]
output<-carolina(model=2,majes[,c(1:4,6)])
output[][-1]
carolina3 <- DC$carolina3
# str(carolina3)
output<-carolina(model=3,carolina3)
output[][-1]
Chz2006 Data amendment Carhuaz 2006
Description
Incidents and performance of healthy tubers and rotten potato field infested with naturally Ralstonia
solanacearum Race 3/Bv 2A, after application of inorganic amendments and a rotation crop in
Carhuaz Peru, 2006.
Usage
data(Chz2006)
Format
The format is: List of 2
amendment a factor
crop a factor
block a numeric vector, replications
plant a numeric vector, number plant
wilt_percent a numeric vector, wilt percentage at 60 days
health a numeric vector, kg/8m2
rot a numeric vector, kg/8m2
18 CIC
Details
Application of inorganic amendment and crop rotation to control bacterial wilt of the potato (MBP).
Source
Experimental field, 2006. Data Kindly provided by Pedro Aley.
References
International Potato Center. CIP - Lima Peru.
Examples
library(agricolae)
data(Chz2006)
str(Chz2006)
wilt<-Chz2006$wilt
yield<-Chz2006$yield
means <- tapply.stat(wilt[,5],wilt[,1:3],function(x) mean(x,na.rm=TRUE))
names(means)[4]<-"wilt_percent"
model <- aov(wilt_percent ~ block + crop, means)
anova(model)
cv.model(model)
yield<-yield[order(paste(yield[,1],yield[,2],yield[,3])),]
correlation(means[,4],yield[,4],method="spearman")
CIC Data for late blight of potatoes
Description
A study of Phytophthora infestans in the potato plant in the localities of Comas and Oxapampa in
Peru, 2005.
Usage
data(CIC)
Format
The format is: List of 2 (comas, oxapampa)
Locality a factor with levels Comas Oxapampa
Genotype a factor
Rep a numeric vector, replications
E9 a numeric vector, infestans percentaje to 9 days
AUDPC a numeric vector: the area under the disease-progress curve
Relative a numeric vector, relative area
clay 19
Details
comas: temperature=59.9 Fahrenheit, relative humidity=83.3 oxapampa: temperature=64.8 Fahren-
heit, relative humidity=86.2 AUDPC and relative see function audpc(). help(audpc) Exx: Evalua-
tion in percentaje, xx is days. ORD1, ORD2, SBLK and row are references location of the plot in
the field.
Source
Experimental field, 2004-2005. Data Kindly provided by Matilde Orrillo.
References
International Potato Center. CIP - Lima Peru.
Examples
library(agricolae)
data(CIC)
CIC$comas
CIC$oxapampa
clay Data of Ralstonia population in clay soil
Description
An evaluation over a time period.
Usage
data(clay)
Format
A data frame with 69 observations on the following 3 variables.
per.clay a numeric vector
days a numeric vector
ralstonia a numeric vector
Source
Experimental field.
References
International Potato Center. CIP - Lima Peru.
20 ComasOxapampa
Examples
library(agricolae)
data(clay)
str(clay)
ComasOxapampa Data AUDPC Comas - Oxapampa
Description
Fifty-three potato varieties developed by the breeding program of the International Potato Center
and released in different countries around the world were evaluated for their resistance to late blight
in two locations in Peru.
Usage
data(ComasOxapampa)
Format
A data frame with 168 observations on the following 4 variables.
cultivar a factor with 56 levels
replication a factor with 3 levels
comas a numeric vector
oxapampa a numeric vector
Details
The experimental design was a randomized complete block design with 3 replications of 15 apical
stem cuttings in Oxapampa and 10 tubers in Mariscal Castilla. Plots were 11.9 x 18.5 m in size
with 30 cm in-row and 0.9 m between-row spacings. Spreader rows around plots were used at each
site. Mancozeb was applied weekly until 30 days after transplanting or planting, after which the
plants were left to natural infection. Due to climatic conditions not conductive to the disease in
Oxapampa, inoculum was enhanced with local isolate (POX 067, with virulence R1, 2, 3, 4, 5, 6,
7, 10, 11) at a concentration of 5000-sporangia/ ml at 49 days after planting. Percentage of foliar
infection was estimated visually every 3 days for 8 times in Oxapampa and every 7 days for 12
times in Comas, then values were converted to the relative area under the diseases progress curve
(rAUPDC). rAUDPC rankings were analyzed for phenotypic stability with nonparametric measures.
Source
Experimental field, 2002. Data Kindly provided by Wilmer Perez.
References
International Potato Center. CIP - Lima Peru.
consensus 21
Examples
library(agricolae)
data(ComasOxapampa)
# Oxapampa (10 35 31 S latitude, 75 23 0 E longitude, 1813 m.a.s.l )
# Comas, Mariscal Castilla (11 42 54 S latitude, 75 04 45 E longitude, 2800 m.a.s.l,)
# cultivars LBr-40 (resistant), Cruza 148 (moderately resistant) and Pimpernell (susceptible)
str(ComasOxapampa)
means <- tapply.stat(ComasOxapampa[,3:4],ComasOxapampa$cultivar,mean)
correlation(means$comas,means$oxapampa, method="kendall")
consensus consensus of clusters
Description
The criterion of the consensus is to produce many trees by means of boostrap and to such calculate
the relative frequency with members of the clusters.
Usage
consensus(data,distance=c("binary","euclidean","maximum","manhattan",
"canberra", "minkowski", "gower","chisq"),method=c("complete","ward","single","average",
"mcquitty","median", "centroid"),nboot=500,duplicate=TRUE,cex.text=1,
col.text="red", ...)
Arguments
data data frame
distance method distance, see dist()
method method cluster, see hclust()
nboot The number of bootstrap samples desired.
duplicate control is TRUE other case is FALSE
cex.text size text on percentage consensus
col.text color text on percentage consensus
... parameters of the plot dendrogram
Details
distance: "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "gower",
"chisq". Method: "ward", "single", "complete", "average", "mcquitty", "median", "centroid". see
functions: dist(), hclust() and daisy() of cluster.
Value
table.dend The groups and consensus percentage
dendrogram The class object is hclust, dendrogram plot
duplicate Homonymous elements
22 corn
Author(s)
F. de Mendiburu
References
An Introduction to the Boostrap. Bradley Efron and Robert J. Tibshirani. 1993. Chapman and
Hall/CRC
See Also
hclust,hgroups,hcut
Examples
library(agricolae)
data(pamCIP)
# only code
rownames(pamCIP)<-substr(rownames(pamCIP),1,6)
# par(cex=0.8)
output<-consensus( pamCIP,distance="binary", method="complete",nboot=5)
# Order consensus
Groups<-output$table.dend[,c(6,5)]
Groups<-Groups[order(Groups[,2],decreasing=TRUE),]
print(Groups)
## Identification of the codes with the numbers.
cbind(output$dendrogram$labels)
## To reproduce dendrogram
dend<-output$dendrogram
data<-output$table.dend
plot(dend)
text(data[,3],data[,4],data[,5])
# Other examples
# classical dendrogram
dend<-as.dendrogram(output$dendrogram)
plot(dend,type="r",edgePar = list(lty=1:2, col=2:1))
text(data[,3],data[,4],data[,5],col="blue",cex=1)
plot(dend,type="t",edgePar = list(lty=1:2, col=2:1))
text(data[,3],data[,4],data[,5],col="blue",cex=1)
## Without the control of duplicates
output<-consensus( pamCIP,duplicate=FALSE,nboot=5)
## using distance gower, require cluster package.
# output<-consensus( pamCIP,distance="gower", method="complete",nboot=5)
corn Data of corn
correl 23
Description
Data from a completely randomized design where four different methods of growing corn resulted
in various yields per acre on various plots of ground where the four methods were tried. Ordinarily,
only one statistical analysis is used, but here we will use the kuskal-wallis test so that a rough
comparison may be made with the mediasn test.
Usage
data(corn)
Format
A data frame with 34 observations on the following 3 variables.
method a numeric vector
observation a numeric vector
rx a numeric vector
Details
The observations are ranked from the smallest, 77, of rank 1 to the largest 101, of rank N=34. Ties
values receive the averarge rank.
Source
Book: Practical Nonparametric Statistics.
References
Practical Nonparametrics Statistics. W.J. Conover. Third Edition, 1999.
Examples
data(corn)
str(corn)
correl Correlation Coefficient
Description
An exact correlation for ties or without ties. Methods of Kendall, Spearman and Pearson.
Usage
correl(x, y, method = "pearson",alternative="two.sided")
24 correlation
Arguments
xVector
yVector
method "pearson", "kendall", "spearman"
alternative "two.sided", "less", "greater"
Value
The correlation of x,y vector with the statistical value and its probability
Author(s)
Felipe de Mendiburu
References
Numerical Recipes in C. Second Edition.
See Also
correlation
Examples
library(agricolae)
data(soil)
with(soil,correl(pH,clay,method="kendall"))
with(soil,correl(pH,clay,method="spearman"))
with(soil,correl(pH,clay,method="pearson"))
correlation Correlation analysis. Methods of Pearson, Spearman, Kendall and
Lin
Description
It obtains the coefficients of correlation and p-value between all the variables of a data table. The
methods to apply are Pearson, Spearman , Kendall and lin’s concordance index. In case of not
specifying the method, the Pearson method will be used. The results are similar to SAS.
Usage
correlation(x,y=NULL, method = c("pearson", "kendall", "spearman", "lin")
,alternative="two.sided")
correlation 25
Arguments
xtable, matrix or vector
ytable, matrix or vector
method "pearson", "kendall", "spearman", "lin"
alternative "two.sided", "less", "greater"
Details
Parameters equal to function cor()
Value
The correlation matrix with its probability
Author(s)
Felipe de Mendiburu
References
Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989; 45,
255-268.
See Also
correl
Examples
library(agricolae)
data(soil)
# example 1
analysis<-correlation(soil[,2:8],method="pearson")
analysis
# Example 2: correlation between pH, variable 2 and other elements from soil.
analysis<-with(soil,correlation(pH,soil[,3:8],method="pearson",alternative="less"))
analysis
# Example 3: correlation between pH and clay method kendall.
with(soil,correlation(pH,clay,method="kendall", alternative="two.sided"))
26 cotton
cotton Data of cotton
Description
Data of cotton collected in experiments of two localities in Lima and Pisco, Peru.
Usage
data(cotton)
Format
A data frame with 96 observations on the following 5 variables.
site a factor with levels Lima Pisco
block a factor with levels I II III IV V VI
lineage a numeric vector
epoca a numeric vector
yield a numeric vector
Source
Book spanish: Metodos estadisticos para la investigacion. Autor: Calzada Benza Universidad Na-
cional Agraria - La Molina - Peru..
References
Book spanish: Metodos estadisticos para la investigacion. Autor: Calzada Benza Universidad Na-
cional Agraria - La Molina - Peru.
Examples
library(agricolae)
data(cotton)
str(cotton)
cv.model 27
cv.model Coefficient of the experiment variation
Description
It obtains the coefficient of variation of the experiment obtained by models lm() or aov()
Usage
cv.model(x)
Arguments
xobject of model lm() or AOV()
Details
sqrt(MSerror)*100/mean(x)
Value
Returns the coefficient of variation of the experiment according to the applied statistical model
Author(s)
Felipe de Mendiburu
See Also
LSD.test,HSD.test,waller.test
Examples
# see examples from LSD , Waller-Duncan or HSD and complete with it:
library(agricolae)
# not run
# cv<-cv.model(model)
28 cv.similarity
cv.similarity Coefficient of the similarity matrix variation
Description
This process consists of finding the coefficient of the distances of similarity of binary tables (1
and 0) as used for scoring molecular marker data for presence and absence of PCR amplification
products.
Usage
cv.similarity(A)
Arguments
Amatrix of binary data
Value
Returns the coefficient of variation of the similarity model
Author(s)
Felipe de Mendiburu
See Also
similarity,resampling.cv
Examples
# molecular markers.
library(agricolae)
data(markers)
cv<-cv.similarity(markers)
DAU.test 29
DAU.test Finding the Variance Analysis of the Augmented block Design
Description
Analysis of variance Augmented block and comparison mean adjusted.
Usage
DAU.test(block, trt, y, method = c("lsd","tukey"),alpha=0.05,group=TRUE,console=FALSE)
Arguments
block blocks
trt Treatment
yResponse
method Comparison treatments
alpha Significant test
group TRUE or FALSE
console logical, print output
Details
Method of comparison treatment. lsd: Least significant difference. tukey: Honestly significant
differente.
Value
means Statistical summary of the study variable
parameters Design parameters
statistics Statistics of the model
comparison Comparison between treatments
groups Formation of treatment groups
SE.difference Standard error of:
Two Control Treatments
Two Augmented Treatments
Two Augmented Treatments(Different Blocks)
A Augmented Treatment and A Control Treatment
vartau Variance-covariance matrix of the difference in treatments
Author(s)
F. de Mendiburu
30 DC
References
Federer, W. T. (1956). Augmented (or hoonuiaku) designs. Hawaiian Planters, Record LV(2):191-
208.
See Also
BIB.test,duncan.test,durbin.test,friedman,HSD.test,kruskal,LSD.test,Median.test,
PBIB.test,REGW.test,scheffe.test,SNK.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
block<-c(rep("I",7),rep("II",6),rep("III",7))
trt<-c("A","B","C","D","g","k","l","A","B","C","D","e","i","A","B","C","D","f","h","j")
yield<-c(83,77,78,78,70,75,74,79,81,81,91,79,78,92,79,87,81,89,96,82)
out<- DAU.test(block,trt,yield,method="lsd", group=TRUE)
print(out$groups)
plot(out)
DC Data for the analysis of carolina genetic design
Description
Data for the analysis of carolina I, II and III genetic design
Usage
data(DC)
Details
DC is list, 3 data.frame: carolina1(72 obs, 6 var), carolina2(300 obs, 9 var) and carolina3(64 obs, 5
var).
Carolina1: Data for the analysis of Carolina I Genetic design. In this design F2 or any advanced
generation maintained by random mating, produced from cross between two pure-lines, is taken as
base population. From the population an individual is randomly selected and used as a male. A
set of 4 randomly selected plans are used as females and are mated to the above male. Thus a set
of 4 full-sib families are produced. This is denoted as a male group. Similarly, a large number of
male groups are produced. No female is used for any second mating. four male groups (16 female
groups) from a set.
Carolina2: Data for the analysis of Carolina II Genetic design. Both paternal and maternal half-sibs
are produced in this design. From an F2 population, n1 males and n2 females are randomly selected
and each male is crossed to each of the females. Thus n1 x n2 progenies are produced whitch are
analysed in a suitably laid experiment.
Carolina3: Data for the analysis of Carolina III genetic design. The F2 population is produced by
crossing two inbreds, say L1 and L2. The material for estimation of genetic parameters is produced
delete.na 31
by back crossing randomly selected F2 individuals (using as males) to each of the inbreds (used as
females).
Source
Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979.
References
Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979.
Examples
data(DC)
names(DC)
str(DC$carolina1)
str(DC$carolina2)
str(DC$carolina3)
delete.na Omitting the rows or columns with missing observations of a matrix
(NA)
Description
In many situations it is required to omit the rows or columns less or greater with NA of the matrix.
Usage
delete.na(x, alternative=c("less", "greater") )
Arguments
xmatrix with NA
alternative "less" or "greater"
Value
xmatrix
Author(s)
Felipe de Mendiburu
32 design.ab
Examples
library(agricolae)
x<-c(2,5,3,7,5,NA,8,0,4,3,NA,NA)
dim(x)<-c(4,3)
x
# [,1] [,2] [,3]
#[1,] 2 5 4
#[2,] 5 NA 3
#[3,] 3 8 NA
#[4,] 7 0 NA
delete.na(x,"less")
# [,1]
#[1,] 2
#[2,] 5
#[3,] 3
#[4,] 7
delete.na(x,"greater")
# [,1] [,2] [,3]
#[1,] 2 5 4
design.ab Design of experiments for a factorial
Description
It generates a design of blocks, randomize and latin square for combined n. factors uses the methods
of number generation in R. The seed is by set.seed(seed, kinds).
Usage
design.ab(trt, r, serie = 2, design=c("rcbd","crd","lsd"),
seed = 0, kinds = "Super-Duper",first=TRUE,randomization=TRUE)
Arguments
trt n levels factors
rReplications or Blocks
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
design type
seed Seed
kinds Method for to randomize
first TRUE or FALSE - randomize rep 1
randomization TRUE or FALSE - randomize
design.ab 33
Details
kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-
TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )
Value
parameters Design parameters
book Fieldbook
Author(s)
Felipe de Mendiburu
References
Introduction to Experimental Statistics. Ching Chun Li. McGraw-Hill Book Company, INC, New.
York, 1964
See Also
design.split,design.alpha,design.bib,design.crd ,design.cyclic ,design.dau ,
design.graeco,design.lattice,design.lsd,design.rcbd,design.strip
Examples
# factorial 3 x 2 with 3 blocks
library(agricolae)
trt<-c(3,2) # factorial 3x2
outdesign <-design.ab(trt, r=3, serie=2)
book<-outdesign$book
head(book,10) # print of the field book
# factorial 2 x 2 x 2 with 5 replications in completely randomized design.
trt<-c(2,2,2)
outdesign<-design.ab(trt, r=5, serie=2,design="crd")
book<-outdesign$book
print(book)
# factorial 3 x 3 in latin square design.
trt <-c(3,3)
outdesign<-design.ab(trt, serie=2, design="lsd")
book<-outdesign$book
print(book)
34 design.alpha
design.alpha Alpha design type (0,1)
Description
Generates an alpha designs starting from the alpha design fixing under the series formulated by
Patterson and Williams. These designs are generated by the alpha arrangements. They are similar
to the lattice designs, but the tables are rectangular s by k (with s blocks and k<s columns. The
number of treatments should be equal to s*k and all the experimental units r*s*k (r replications).
Usage
design.alpha(trt, k, r, serie = 2, seed = 0, kinds = "Super-Duper",randomization=TRUE)
Arguments
trt Treatments
ksize block
rReplications
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
seed seed
kinds method for to randomize
randomization TRUE or FALSE - randomize
Details
Parameters for the alpha design: I. r=2, k <= s; II. r=3, s odd, k <= s; III.r=3, s even, k <= s-1; IV.
r=4, s odd but not a multiple of 3, k<=s
r= replications s=number of blocks k=size of block Number of treatment is equal to k*s
Value
parameters Design parameters
statistics Design statistics
sketch Design sketch
book Fieldbook
Author(s)
Felipe de Mendiburu
References
H.D. Patterson and E.R. Williams. Biometrika (1976) A new class of resolvable incomplete block
designs. printed in Great Britain. Online: http://biomet.oxfordjournals.org/cgi/content/abstract/63/1/83
design.bib 35
See Also
design.ab,design.split,design.bib,design.crd ,design.cyclic ,design.dau ,design.graeco,
design.lattice,design.lsd,design.rcbd,design.strip
Examples
library(agricolae)
#Example one
trt<-1:30
t <- length(trt)
# size block k
k<-3
# Blocks s
s<-t/k
# replications r
r <- 2
outdesign<- design.alpha(trt,k,r,serie=2)
book<-outdesign$book
plots<-book[,1]
dim(plots)<-c(k,s,r)
for (i in 1:r) print(t(plots[,,i]))
outdesign$sketch
# Example two
trt<-letters[1:12]
t <- length(trt)
k<-3
r<-3
s<-t/k
outdesign<- design.alpha(trt,k,r,serie=2)
book<-outdesign$book
plots<-book[,1]
dim(plots)<-c(k,s,r)
for (i in 1:r) print(t(plots[,,i]))
outdesign$sketch
design.bib Randomized Balanced Incomplete Block Designs. BIB
Description
Creates Randomized Balanced Incomplete Block Design. "Random" uses the methods of number
generation in R. The seed is by set.seed(seed, kinds).
Usage
design.bib(trt, k, r=NULL, serie = 2, seed = 0, kinds = "Super-Duper",
maxRep=20,randomization=TRUE)
36 design.bib
Arguments
trt Treatments
ksize block
rReplications
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
seed seed
kinds method for to randomize
maxRep repetition maximum
randomization TRUE or FALSE - randomize
Details
The package AlgDesign is necessary.
if r = NULL, then it calculates the value of r smaller for k defined. In the case of r = value, then the
possible values for "r" is calculated
K is the smallest integer number of treatments and both values are consistent in design.
kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-
TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )
Value
parameters Design parameters
statistics Design statistics
sketch Design sketch
book Fieldbook
Author(s)
Felipe de Mendiburu
References
1. Experimental design. Cochran and Cox. Second edition. Wiley Classics Library Edition pub-
lished 1992
2. Optimal Experimental Design with R. Dieter Rasch, Jurgen Pilz, Rob Verdooren and Albrecht
Gebhardt. 2011 by Taylor and Francis Group, LLC CRC Press is an imprint of Taylor and Francis
Group, an Informa business.
3. Design of Experiments. Robert O. Kuehl. 2nd ed., Duxbury, 2000.
See Also
design.ab,design.alpha,design.split,design.crd ,design.cyclic ,design.dau ,design.graeco,
design.lattice,design.lsd,design.rcbd,design.strip
design.crd 37
Examples
library(agricolae)
# 4 treatments and k=3 size block
trt<-c("A","B","C","D")
k<-3
outdesign<-design.bib(trt,k,serie=2,seed =41,kinds ="Super-Duper") # seed = 41
print(outdesign$parameters)
book<-outdesign$book
plots <-as.numeric(book[,1])
matrix(plots,byrow=TRUE,ncol=k)
print(outdesign$sketch)
# write in hard disk
# write.csv(book,"book.csv", row.names=FALSE)
# file.show("book.csv")
design.crd Completely Randomized Design
Description
It generates completely a randomized design with equal or different repetition. "Random" uses the
methods of number generation in R. The seed is by set.seed(seed, kinds).
Usage
design.crd(trt, r, serie = 2, seed = 0, kinds = "Super-Duper",randomization=TRUE)
Arguments
trt Treatments
rReplications
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
seed seed
kinds method for to randomize
randomization TRUE or FALSE - randomize
Details
kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-
TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )
Value
parameters Design parameters
book Fieldbook
38 design.cyclic
Author(s)
Felipe de Mendiburu
References
Introduction to Experimental Statistics. Ching Chun Li. McGraw-Hill Book Company, INC, New.
York, 1964
See Also
design.ab,design.alpha,design.bib,design.split ,design.cyclic ,design.dau ,design.graeco,
design.lattice,design.lsd,design.rcbd,design.strip
Examples
library(agricolae)
trt <-c("CIP-101","CIP-201","CIP-301","CIP-401","CIP-501")
r <-c(4,3,5,4,3)
# seed = 12543
outdesign1 <-design.crd(trt,r,serie=2,2543,"Mersenne-Twister")
book1<-outdesign1
# no seed
outdesign2 <-design.crd(trt,r,serie=3)
print(outdesign2$parameters)
book2<-outdesign2
# write to hard disk
# write.table(book1,"crd.txt", row.names=FALSE, sep="\t")
# file.show("crd.txt")
design.cyclic Cyclic designs
Description
The cyclic design is a incomplete blocks designs, it is generated from a incomplete block initial of
the size k, the plan is generated and randomized. The efficient and robust cyclic designs for 6 to 30
treatments, replications <= 10.
Usage
design.cyclic(trt, k, r, serie = 2, rowcol = FALSE, seed = 0, kinds = "Super-Duper"
,randomization=TRUE)
design.cyclic 39
Arguments
trt vector treatments
kblock size
rReplications
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
rowcol TRUE: row-column design
seed init seed random
kinds random method
randomization TRUE or FALSE - randomize
Details
Number o treatment 6 to 30. (r) Replication 2 to 10. (k) size of block 2 to 10. replication = i*k, "i"
is value integer.
Value
parameters Design parameters
sketch Design sketch
book Fieldbook
Author(s)
Felipe de Mendiburu
References
Kuehl, Robert(2000), Design of Experiments. 2nd ed., Duxbury. John, J.A. (1981) Efficient Cyclic
Design. J. R. Statist. Soc. B, 43, No. 1, pp, 76-80.
See Also
design.ab,design.alpha,design.bib,design.crd ,design.split ,design.dau ,design.graeco,
design.lattice,design.lsd,design.rcbd,design.strip
Examples
library(agricolae)
trt<-letters[1:8]
# block size = 2, replication = 6
outdesign1 <- design.cyclic(trt,k=2, r=6,serie=2)
names(outdesign1)
# groups 1,2,3
outdesign1$sketch[[1]]
outdesign1$sketch[[2]]
outdesign1$sketch[[3]]
outdesign1$book
40 design.dau
# row-column design
outdesign2<- design.cyclic(trt,k=2, r=6, serie=2, rowcol=TRUE)
outdesign2$sketch
design.dau Augmented block design
Description
These are designs for two types of treatments: the control treatments (common) and the increased
treatments. The common treatments are applied in complete randomized blocks, and the increased
treatments, at random. Each treatment should be applied in any block once only. It is understood
that the common treatments are of a greater interest; the standard error of the difference is much
smaller than when between two increased ones in different blocks.
Usage
design.dau(trt1, trt2, r, serie = 2, seed = 0, kinds = "Super-Duper", name="trt"
,randomization=TRUE)
Arguments
trt1 checks
trt2 new
rReplications or blocks
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
seed seed
kinds method for to randomize
name name of treatments
randomization TRUE or FALSE - randomize
Details
kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-
TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )
Value
parameters Design parameters
book Fieldbook
Author(s)
Felipe de Mendiburu
design.graeco 41
References
1. Augmented (or Hoonuiaku) Design. Federer, W.T. (1956), Hawaii Plr. rec., 55: 191-208. 2. In
Augmented Designs. Federer, W.T and Raghavarao, D. (1975). Bometrics, vol. 31, No. 1 (mar..,
1975), pp. 29-35
See Also
design.ab,design.alpha,design.bib,design.crd ,design.cyclic ,design.split ,design.graeco,
design.lattice,design.lsd,design.rcbd,design.strip
Examples
library(agricolae)
# 4 treatments and 5 blocks
T1<-c("A","B","C","D")
T2<-letters[20:26]
outdesign <-design.dau(T1,T2, r=5,serie=2)
# field book
book<-outdesign$book
by(book,book[2],function(x) paste(x[,1],"-",as.character(x[,3])))
# write in hard disk
# write.table(book,"dau.txt", row.names=FALSE, sep="\t")
# file.show("dau.txt")
# Augmented designs in Completely Randomized Design
trt<-c(T1,T2)
r<-c(4,4,4,4,1,1,1,1,1,1,1)
outdesign <- design.crd(trt,r)
outdesign$book
design.graeco Graeco - latin square design
Description
A graeco - latin square is a KxK pattern that permits the study of k treatments simultaneously with
three different blocking variables, each at k levels.
The function is only for squares of the odd numbers and even numbers (4, 8, 10 and 12)
Usage
design.graeco(trt1, trt2, serie = 2, seed = 0, kinds = "Super-Duper",randomization=TRUE)
Arguments
trt1 Treatments
trt2 Treatments
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
42 design.graeco
seed seed
kinds method for to randomize
randomization TRUE or FALSE - randomize
Details
kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-
TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )
Value
parameters Design parameters
book Fieldbook
Author(s)
Felipe de Mendiburu
References
1. Statistics for Experimenters Design, Innovation, and Discovery Second Edition. George E. P.
Box. Wiley-Interscience. 2005.
2. Experimental design. Cochran and Cox. Second edition. Wiley Classics Library Edition pub-
lished 1992.
See Also
design.ab,design.alpha,design.bib,design.crd ,design.cyclic ,design.dau ,design.split,
design.lattice,design.lsd,design.rcbd,design.strip
Examples
library(agricolae)
T1<-c("a","b","c","d")
T2<-c("v","w","x","y")
outdesign <- design.graeco(T1,T2,serie=1)
graeco<-outdesign$book
plots <-as.numeric(graeco[,1])
print(outdesign$sketch)
print(matrix(plots,byrow=TRUE,ncol=4))
# 10 x 10
T1 <- letters[1:10]
T2 <- 1:10
outdesign <- design.graeco(T1,T2,serie=2)
print(outdesign$sketch)
design.lattice 43
design.lattice Lattice designs
Description
SIMPLE and TRIPLE lattice designs. It randomizes treatments in k x k lattice.
Usage
design.lattice(trt, r=3, serie = 2, seed = 0, kinds = "Super-Duper",randomization=TRUE)
Arguments
trt treatments
rr=2(simple) or r=3(triple) lattice
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
seed seed
kinds method for to randomize
randomization TRUE or FALSE - randomize
Details
kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-
TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )
Value
parameters Design parameters
statistics Design statistics
sketch Design sketch
book Fieldbook
Author(s)
Felipe de Mendiburu
References
FIELD PLOT TECHNIQUE. Erwin L. LeCLERG. 2nd ed., 1962, Burgess Publishing Company,
Minnesota
See Also
design.ab,design.alpha,design.bib,design.crd ,design.cyclic ,design.dau ,design.graeco,
design.split,design.lsd,design.rcbd,design.strip
44 design.lsd
Examples
library(agricolae)
# triple lattice
trt<-LETTERS[1:9]
outdesign<-design.lattice(trt,r=3,serie=2) # triple lattice design ( 9 trt)
# simple lattice
trt<-1:100
outdesign<-design.lattice(trt,r=2,serie=3) # simple lattice design, 10x10
design.lsd Latin Square Design
Description
It generates Latin Square Design. "Random" uses the methods of number generation in R. The seed
is by set.seed(seed, kinds).
Usage
design.lsd(trt, serie = 2, seed = 0, kinds = "Super-Duper",first=TRUE,randomization=TRUE)
Arguments
trt Treatments
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
seed seed
kinds method for to randomize
first TRUE or FALSE - randomize rep 1
randomization TRUE or FALSE - randomize
Details
kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-
TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )
Value
parameters Design parameters
book Fieldbook
Author(s)
Felipe de Mendiburu
design.rcbd 45
References
Introduction to Experimental Statistics. Ching Chun Li. McGraw-Hill Book Company, INC, New.
York, 1969
See Also
design.ab,design.alpha,design.bib,design.crd ,design.cyclic ,design.dau ,design.graeco,
design.lattice,design.split,design.rcbd,design.strip
Examples
library(agricolae)
varieties<-c("perricholi","yungay","maria bonita","tomasa")
outdesign <-design.lsd(varieties,serie=2,seed=23)
lsd <- outdesign$book
print(outdesign$sketch)
print(lsd) # field book.
plots <-as.numeric(lsd[,1])
print(matrix(plots,byrow = TRUE, ncol = 4))
# Write on hard disk.
# write.table(lsd,"lsd.txt", row.names=FALSE, sep="\t")
# file.show("lsd.txt")
design.rcbd Randomized Complete Block Design
Description
It generates Randomized Complete Block Design. "Random" uses the methods of number genera-
tion in R. The seed is by set.seed(seed, kinds).
Usage
design.rcbd(trt, r, serie = 2, seed = 0, kinds = "Super-Duper", first=TRUE,
continue=FALSE,randomization=TRUE )
Arguments
trt Treatments
rReplications or blocks
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
seed seed
kinds method for to randomize
first TRUE or FALSE - randomize rep 1
continue TRUE or FALSE, continuous numbering of plot
randomization TRUE or FALSE - randomize
46 design.rcbd
Details
kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-
TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )
Value
parameters Design parameters
sketch Design sketch
book Fieldbook
Author(s)
Felipe de Mendiburu
References
Introduction to Experimental Statistics. Ching Chun Li. McGraw-Hill Book Company, INC, New.
York, 1964
See Also
design.ab,design.alpha,design.bib,design.crd ,design.cyclic ,design.dau ,design.graeco,
design.lattice,design.lsd,design.split,design.strip
Examples
library(agricolae)
# 5 treatments and 6 blocks
trt<-c("A","B","C","D","E")
outdesign <-design.rcbd(trt,6,serie=2,986,"Wichmann-Hill") # seed = 986
book <-outdesign$book # field book
# write in hard disk
# write.table(book,"rcbd.txt", row.names=FALSE, sep="\t")
# file.show("rcbd.txt")
# Plots in field model ZIGZAG
fieldbook <- zigzag(outdesign)
print(outdesign$sketch)
print(matrix(fieldbook[,1],byrow=TRUE,ncol=5))
# continuous numbering of plot
outdesign <-design.rcbd(trt,6,serie=0,continue=TRUE)
head(outdesign$book)
design.split 47
design.split Split Plot Design
Description
It generates split plot design. "Random" uses the methods of number generation in R. The seed is
by set.seed(seed, kinds).
Usage
design.split(trt1, trt2,r=NULL, design=c("rcbd","crd","lsd"),serie = 2,
seed = 0, kinds = "Super-Duper", first=TRUE,randomization=TRUE)
Arguments
trt1 Treatments in Plots
trt2 Treatments in Subplots
rReplications or blocks
design Experimental design
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
seed seed
kinds method for to randomize
first TRUE or FALSE - randomize rep 1
randomization TRUE or FALSE - randomize
Details
kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-
TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )
Value
parameters Design parameters
book Fieldbook
Author(s)
Felipe de Mendiburu
References
Statistical Procedures for Agricultural Research. Kwanchai A. Gomez, Arturo A. Gomez. John
Wiley & Sons, new York, 1984
48 design.strip
See Also
design.ab,design.alpha,design.bib,design.crd ,design.cyclic ,design.dau ,design.graeco,
design.lattice,design.lsd,design.rcbd,design.strip
Examples
library(agricolae)
# 4 treatments and 5 blocks in split-plot
t1<-c("A","B","C","D")
t2<-c(1,2,3)
outdesign <-design.split(t1,t2,r=3,serie=2,seed=45,kinds ="Super-Duper")#seed=45
book<-outdesign$book# field book
# write in hard disk
# write.table(book,"book.txt", row.names=FALSE, sep="\t")
# file.show("book.txt")
design.strip Strip Plot Design
Description
It generates strip plot design. "Random" uses the methods of number generation in R. The seed is
by set.seed(seed, kinds).
Usage
design.strip(trt1, trt2,r, serie = 2, seed = 0, kinds = "Super-Duper",randomization=TRUE)
Arguments
trt1 Row treatments
trt2 column treatments
rReplications
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
seed seed
kinds method for to randomize
randomization TRUE or FALSE - randomize
Details
kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-
TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )
design.youden 49
Value
parameters Design parameters
book Fieldbook
Author(s)
Felipe de Mendiburu
References
Statistical Procedures for Agricultural Research. Kwanchai A. Gomez, Arturo A. Gomez. John
Wiley & Sons, new York, 1984
See Also
design.ab,design.alpha,design.bib,design.crd ,design.cyclic ,design.dau ,design.graeco,
design.lattice,design.lsd,design.rcbd,design.split
Examples
library(agricolae)
# 4 and 3 treatments and 3 blocks in strip-plot
t1<-c("A","B","C","D")
t2<-c(1,2,3)
r<-3
outdesign <-design.strip(t1,t2,r, serie=2,seed=45,kinds ="Super-Duper") # seed = 45
book <-outdesign$book # field book
# write in hard disk
# write.table(book,"book.txt", row.names=FALSE, sep="\t")
# file.show("book.txt")
design.youden Incomplete Latin Square Design
Description
Such designs are referred to as Youden squares since they were introduced by Youden (1937) after
Yates (1936) considered the special case of column equal to number treatment minus 1. "Random"
uses the methods of number generation in R. The seed is by set.seed(seed, kinds).
Usage
design.youden(trt, r, serie = 2, seed = 0, kinds = "Super-Duper",first=TRUE
,randomization=TRUE)
50 design.youden
Arguments
trt Treatments
rReplications or number of columns
serie number plot, 1: 11,12; 2: 101,102; 3: 1001,1002
seed seed
kinds method for to randomize
first TRUE or FALSE - randomize rep 1
randomization TRUE or FALSE - randomize
Details
kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-
TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )
Value
parameters Design parameters
sketch Design sketch
book Fieldbook
Author(s)
Felipe de Mendiburu
References
Design and Analysis of experiment. Hinkelmann, Klaus and Kempthorne, Oscar. Wiley-Interscience.
Copyright (2008) by John Wiley and Sons. Inc., Hoboken, new Yersy
See Also
design.ab,design.alpha,design.bib,design.crd ,design.cyclic ,design.dau ,design.graeco,
design.lattice,design.split,design.rcbd,design.strip,design.lsd
Examples
library(agricolae)
varieties<-c("perricholi","yungay","maria bonita","tomasa")
r<-3
outdesign <-design.youden(varieties,r,serie=2,seed=23)
youden <- outdesign$book
print(outdesign$sketch)
plots <-as.numeric(youden[,1])
print(matrix(plots,byrow=TRUE,ncol=r))
print(youden) # field book.
# Write on hard disk.
# write.table(youden,"youden.txt", row.names=FALSE, sep="\t")
# file.show("youden.txt")
diffograph 51
diffograph Plotting the multiple comparison of means
Description
It plots bars of the averages of treatments to compare. It uses the objects generated by a procedure
of comparison like LSD (Fisher), duncan, Tukey (HSD), Student Newman Keul (SNK), Scheffe,
Ryan, Einot and Gabriel and Welsch (REGW), Kruskal Wallis, Friedman and Waerden.
Usage
diffograph(x, main=NULL,color1="red",color2="blue",color3="black",
cex.axis=0.8,las=1,pch=20,bty="l",cex=0.8,lwd=1,xlab="",ylab="",...)
Arguments
xObject created by a test of comparison, group=FALSE
main The main title (on top)
color1 non significant color
color2 significant color
color3 center line color
cex.axis parameters of the plot()
las parameters of the plot()
pch parameters of the plot()
bty parameters of the plot()
cex parameters of the plot()
lwd parameters of the plot()
xlab parameters of the plot()
ylab parameters of the plot()
... Other parameters of the function plot()
Details
The graph.diff function should be used for functions: LSD, duncan, SNK, scheffe, REGW, HSD,
kruskal, friedman and waerden test.
Value
xlist, object comparison test
Author(s)
Felipe de Mendiburu
52 disease
References
Multiple comparisons theory and methods. Departament of statistics the Ohio State University.
USA, 1996. Jason C. Hsu. Chapman Hall/CRC
See Also
LSD.test,HSD.test,duncan.test,SNK.test,scheffe.test,REGW.test,kruskal,friedman,
waerden.test
Examples
# Example 1
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
x<- LSD.test(model,"virus",alpha=0.01,group=FALSE)
diffograph(x,cex.axis=0.8,xlab="Yield",ylab="")
# Example 2
x<- REGW.test(model,"virus",alpha=0.01,group=FALSE)
diffograph(x,cex.axis=0.6,xlab="Yield",ylab="",color1="brown",color2="green")
disease Data evaluation of the disease overtime
Description
Three evaluations over time and the potato yield when applying several treatments.
Usage
data(disease)
Format
A data frame with 21 observations on the following 7 variables.
plots a numeric vector
rep a numeric vector
trt a factor with levels T0 T1 T2 T3 T4 T5 T6
E2 a numeric vector
E5 a numeric vector
E7 a numeric vector
yield a numeric vector
Source
Experimental data.
duncan.test 53
References
International Potato Center. CIP - Lima Peru.
Examples
library(agricolae)
data(disease)
str(disease)
duncan.test Duncan’s new multiple range test
Description
This test is adapted from the Newman-Keuls method. Duncan’s test does not control family wise
error rate at the specified alpha level. It has more power than the other post tests, but only because it
doesn’t control the error rate properly. The Experimentwise Error Rate at: 1-(1-alpha)^(a-1); where
"a" is the number of means and is the Per-Comparison Error Rate. Duncan’s procedure is only very
slightly more conservative than LSD. The level by alpha default is 0.05.
Usage
duncan.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL,console=FALSE)
Arguments
ymodel(aov or lm) or answer of the experimental unit
trt Constant( only y=model) or vector treatment applied to each experimental unit
DFerror Degree free
MSerror Mean Square Error
alpha Significant level
group TRUE or FALSE
main Title
console logical, print output
Details
It is necessary first makes a analysis of variance.
Value
statistics Statistics of the model
parameters Design parameters
duncan Critical Range Table
means Statistical summary of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
54 durbin.test
Author(s)
Felipe de Mendiburu
References
1. Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third
Edition 1997 2. Multiple comparisons theory and methods. Departament of statistics the Ohio State
University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.
See Also
BIB.test,DAU.test,durbin.test,friedman,HSD.test,kruskal,LSD.test,Median.test,
PBIB.test,REGW.test,scheffe.test,SNK.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
out <- duncan.test(model,"virus",
main="Yield of sweetpotato. Dealt with different virus")
plot(out,variation="IQR")
duncan.test(model,"virus",alpha=0.01,console=TRUE)
# version old duncan.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
out <- with(sweetpotato,duncan.test(yield,virus,df,MSerror, group=TRUE))
plot(out,horiz=TRUE,las=1)
print(out$groups)
durbin.test Durbin test and multiple comparison of treatments
Description
A multiple comparison of the Durbin test for the balanced incomplete blocks for sensorial or cate-
gorical evaluation. It forms groups according to the demanded ones for level of significance (alpha);
by default, 0.05.
Usage
durbin.test(judge, trt, evaluation, alpha = 0.05, group =TRUE,
main = NULL, console=FALSE)
durbin.test 55
Arguments
judge Identification of the judge in the evaluation
trt Treatments
evaluation variable
alpha level of significant
group TRUE or FALSE
main Title
console logical, print output
Details
The post hoc test is using the criterium Fisher’s least significant difference.
Value
statistics Statistics of the model
parameters Design parameters
means Statistical summary of the study variable
rank rank table of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
Author(s)
Felipe de Mendiburu
References
Practical Nonparametrics Statistics. W.J. Conover, 1999 Nonparametric Statistical Methods. Myles
Hollander and Douglas A. Wofe, 1999
See Also
BIB.test,DAU.test,duncan.test,friedman,HSD.test,kruskal,LSD.test,Median.test,
PBIB.test,REGW.test,scheffe.test,SNK.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
# Example 1. Conover, pag 391
person<-gl(7,3)
variety<-c(1,2,4,2,3,5,3,4,6,4,5,7,1,5,6,2,6,7,1,3,7)
preference<-c(2,3,1,3,1,2,2,1,3,1,2,3,3,1,2,3,1,2,3,1,2)
out<-durbin.test(person,variety,preference,group=TRUE,console=TRUE,
main="Seven varieties of ice cream manufacturer")
#startgraph
bar.group(out$groups,horiz=TRUE,xlim=c(0,10),density=4,las=1)
56 friedman
#endgraph
# Example 2. Myles Hollander, pag 311
# Source: W. Moore and C.I. Bliss. 1942
day<-gl(7,3)
chemical<-c("A","B","D","A","C","E","C","D","G","A","F","G","B","C","F",
"B","E","G","D","E","F")
toxic<-c(0.465,0.343,0.396,0.602,0.873,0.634,0.875,0.325,0.330,0.423,0.987,
0.426,0.652,1.142,0.989,0.536,0.409,0.309,0.609,0.417,0.931)
out<-durbin.test(day,chemical,toxic,group=TRUE,console=TRUE,
main="Logarithm of Toxic Dosages")
plot(out)
friedman Friedman test and multiple comparison of treatments
Description
The data consist of b-blocks mutually independent k-variate random variables Xij, i=1,..,b; j=1,..k.
The random variable X is in block i and is associated with treatment j. It makes the multiple
comparison of the Friedman test with or without ties. A first result is obtained by friedman.test of
R.
Usage
friedman(judge,trt,evaluation,alpha=0.05,group=TRUE,main=NULL,console=FALSE)
Arguments
judge Identification of the judge in the evaluation
trt Treatment
evaluation Variable
alpha Significant test
group TRUE or FALSE
main Title
console logical, print output
Details
The post hoc friedman test is using the criterium Fisher’s least significant difference (LSD)
Value
statistics Statistics of the model
parameters Design parameters
means Statistical summary of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
frijol 57
Author(s)
Felipe de Mendiburu
References
Practical Nonparametrics Statistics. W.J. Conover, 1999
See Also
BIB.test,DAU.test,duncan.test,durbin.test,HSD.test,kruskal,LSD.test,Median.test,
PBIB.test,REGW.test,scheffe.test,SNK.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
data(grass)
out<-with(grass,friedman(judge,trt, evaluation,alpha=0.05, group=TRUE,console=TRUE,
main="Data of the book of Conover"))
#startgraph
plot(out,variation="IQR")
#endgraph
frijol Data of frijol
Description
Data of frijol under 4 technologies for the homogeneity of regression study. Yield of Frijol in kg/ha
in clean and dry grain.
Tecnnologies: 20-40-20 kg/ha. N. P2O5 and K2O + 2 t/ha of gallinaza. 40-80-40 kg/ha. N. P2O5
and K2O + 2 t/ha of gallinaza. 60-120-60 kg/ha. N. P2O5 and K2O + 2 t/ha of gallinaza. 40-80-40
kg/ha. N. P2O5 and K2O + 4 t/ha of gallinaza.
Usage
data(frijol)
Format
A data frame with 84 observations on the following 3 variables.
technology a factor with levels abcd
production a numeric vector
index a numeric vector
References
Oriente antioqueno (1972) (ICA.- Orlando Martinez W.) Colombia.
58 genxenv
Examples
library(agricolae)
data(frijol)
str(frijol)
genxenv Data of potato yield in a different environment
Description
50 genotypes and 5 environments.
Usage
data(genxenv)
Format
A data frame with 250 observations on the following 3 variables.
ENV a numeric vector
GEN a numeric vector
YLD a numeric vector
Source
International Potato Center. CIP - Lima Peru.
References
International Potato Center. CIP - Lima Peru.
Examples
library(agricolae)
data(genxenv)
str(genxenv)
Glycoalkaloids 59
Glycoalkaloids Data Glycoalkaloids
Description
A measurement of the Glycoalkaloids by two methods: HPLC and spectrophotometer.
Usage
data(Glycoalkaloids)
Format
A data frame with 25 observations on the following 2 variables.
HPLC a numeric vector
spectrophotometer a numeric vector
Source
International Potato Center. CIP - Lima Peru.
References
International Potato Center. CIP - Lima Peru.
Examples
library(agricolae)
data(Glycoalkaloids)
str(Glycoalkaloids)
graph.freq Histogram
Description
In many situations it has intervals of class defined with its respective frequencies. By means of this
function, the graphic of frequency is obtained and it is possible to superpose the normal distribution,
polygon of frequency, Ojiva and to construct the table of complete frequency.
Usage
graph.freq(x, breaks=NULL,counts=NULL,frequency=1, plot=TRUE, nclass=NULL,
xlab="",ylab="",axes = "",las=1,...)
60 graph.freq
Arguments
xa vector of values, a object hist(), graph.freq()
counts frequency and x is class intervals
breaks a vector giving the breakpoints between histogram cells
frequency 1=counts, 2=relative, 3=density
plot logic
nclass number of classes
xlab x labels
ylab y labels
las numeric in 0,1,2,3; the style of axis labels. see plot()
axes TRUE or FALSE
... other parameters of plot
Value
breaks a vector giving the breakpoints between histogram cells
counts frequency and x is class intervals
mids center point in class
relative Relative frequency, height
density Density frequency, height
Author(s)
Felipe de Mendiburu
See Also
polygon.freq,table.freq,stat.freq,intervals.freq,sturges.freq,join.freq,ogive.freq,
normal.freq
Examples
library(agricolae)
data(genxenv)
yield <- subset(genxenv$YLD,genxenv$ENV==2)
yield <- round(yield,1)
h<- graph.freq(yield,axes=FALSE, frequency=1, ylab="frequency",col="yellow")
axis(1,h$breaks)
axis(2,seq(0,20,0.1))
# To reproduce histogram.
h1 <- graph.freq(h, col="blue", frequency=2,border="red", density=8,axes=FALSE,
xlab="YIELD",ylab="relative")
axis(1,h$breaks)
axis(2,seq(0,.4,0.1))
# summary, only frecuency
grass 61
limits <-seq(10,40,5)
frequencies <-c(2,6,8,7,3,4)
#startgraph
h<-graph.freq(limits,counts=frequencies,col="bisque",xlab="Classes")
polygon.freq(h,col="red")
title( main="Histogram and polygon of frequency",
ylab="frequency")
#endgraph
# Statistics
measures<-stat.freq(h)
print(measures)
# frequency table full
round(table.freq(h),2)
#startgraph
# ogive
ogive.freq(h,col="red",type="b",ylab="Accumulated relative frequency",
xlab="Variable")
# only .frequency polygon
h<-graph.freq(limits,counts=frequencies,border=FALSE,col=NULL,xlab=" ",ylab="")
title( main="Polygon of frequency",
xlab="Variable", ylab="Frecuency")
polygon.freq(h,col="blue")
grid(col="brown")
#endgraph
# Draw curve for Histogram
h<- graph.freq(yield,axes=FALSE, frequency=3, ylab="f(yield)",col="yellow")
axis(1,h$breaks)
axis(2,seq(0,0.18,0.03),las=2)
lines(density(yield), col = "red", lwd = 2)
title("Draw curve for Histogram")
grass Data for Friedman test
Description
Twelve homeowners are selected randomly to participate in an experiment with a plant nursery.
Each homeowner is asked to select four fairly identical areas in his yard and to plant four different
types of grasses, one in each area.
Usage
data(grass)
Format
A data frame with 48 observations on the following 3 variables.
judge a numeric vector
trt a factor with levels t1 t2 t3 t4
evaluation a numeric vector
62 greenhouse
Details
Each of the 12 blocks consists of four fairly identical plots of land, each receiving care of ap-
proximately the same degree of skill because the four plots are presumably cared for by the same
homeowern.
Source
Book: Practical Nonparametrics Statistics, pag 372.
References
Practical Nonparametrics Statistics. W.J. Conover, 1999
Examples
data(grass)
str(grass)
greenhouse Data in greenhouse
Description
Potato minituber production in greenhouse, three sets of data in potato varieties with different meth-
ods: hydroponics, Aeroponic, Pots and Plant beds, the unit is in grams and the number of tubers in
units,
Usage
data(greenhouse)
Details
greenhouse is list, three tables: greenhouse1(480 obs, 5 var), yield for plant, unit is grams. green-
house2(48 obs, 5 var), Yields of 10 plants by experimental unit(grams). planting date(April 24,
2004) and harvest date(July 16, 2004) and greenhouse3(480 obs, 5 var), Tubers by plants.
Source
International Potato Center(CIP). Lima-Peru. Data Kindly provided by Carlos Chuquillanqui.
References
- Produccion de semila de papa por hidroponia tecnica de flujo continuo de una pelicula de solu-
cion nutritiva (nft) Carlos Chuquillanqui(CIP), Jorge Tenorio(CIP) and L. F. Salazar(Agdia Inc).
AGROENFOQUE Lima-Peru (2004) - Potato Minituber Production Using Aeroponics: Effect of
Plant Density and Harvesting Intervals American Journal of Potato Research, Jan/Feb 2006 by Far-
ran, Imma, Mingo-Castel, Angel M
growth 63
Examples
library(agricolae)
data(greenhouse)
greenhouse1 <- greenhouse$greenhouse1
greenhouse2 <- greenhouse$greenhouse2
greenhouse3 <- greenhouse$greenhouse3
growth Data growth of trees
Description
Data growth of pijuayo trees in several localities.
Usage
data(growth)
Format
A data frame with 30 observations on the following 3 variables.
place a factor with levels L1 L2
slime a numeric vector
height a numeric vector
Source
Experimental data (Pucallpa - Peru)
References
ICRAF lima Peru.
Examples
library(agricolae)
data(growth)
str(growth)
64 haynes
haynes Data of AUDPC for nonparametrical stability analysis
Description
Published data. Haynes. Mean area under the disease progress curve (AUDPC) for each of 16
potato clones evaluated at eight sites across the United States in 1996
Usage
data(haynes)
Format
A data frame with 16 observations on the following 9 variables.
clone a factor with levels A84118-3 AO80432-1 AO84275-3 AWN86514-2 B0692-4 B0718-3 B0749-2F
B0767-2 Bertita Bzura C0083008-1 Elba Greta Krantz Libertas Stobrawa
FL a numeric vector
MI a numeric vector
ME a numeric vector
MN a numeric vector
ND a numeric vector
NY a numeric vector
PA a numeric vector
WI a numeric vector
References
Haynes K G, Lambert D H, Christ B J, Weingartner D P, Douches D S, Backlund J E, Fry W and
Stevenson W. 1998. Phenotypic stability of resistance to late blight in potato clones evaluated at
eight sites in the United States American Journal Potato Research 75, pag 211-217.
Examples
library(agricolae)
data(haynes)
str(haynes)
Hco2006 65
Hco2006 Data amendment Huanuco 2006
Description
Incidents and performance of healthy tubers and rotten potato field infested with naturally Ralstonia
solanacearum Race 3/Bv 2A, after application of inorganic amendments and a rotation crop in
Huanuco Peru, 2006.
Usage
data(Hco2006)
Format
The format is: List of 2
amendment a factor
crop a factor
block a numeric vector, replications
plant a numeric vector, number plant
wilt_percent a numeric vector, wilt percentage at 60 days
health a numeric vector, kg/8m2, 20 plants
rot a numeric vector, kg/8m2, 20 plants
Details
Application of inorganic amendment and crop rotation to control bacterial wilt of the potato (MBP).
Source
Experimental field, 2006. Data Kindly provided by Pedro Aley.
References
International Potato Center. CIP - Lima Peru.
Examples
library(agricolae)
data(Hco2006)
str(Hco2006)
wilt<-Hco2006$wilt
yield<-Hco2006$yield
means <- tapply.stat(wilt[,5],wilt[,1:3],function(x) mean(x,na.rm=TRUE))
names(means)[4]<-"wilt_percent"
model <- aov(wilt_percent ~ block + crop, means)
66 hcut
anova(model)
cv.model(model)
yield<-yield[order(paste(yield[,1],yield[,2],yield[,3])),]
correlation(means[,4],yield[,4],method="spearman")
hcut Cut tree of consensus
Description
It shows dendrogram of a consensus of a tree generated by hclust.
Usage
hcut(consensus,h,group,col.text="blue",cex.text=1,...)
Arguments
consensus object consensus
hnumeric scalar or vector with heights where the tree should be cut.
group an integer scalar with the desired number of group
col.text color of number consensus
cex.text size of number consensus
... Other parameters of the function plot() in cut()
Value
hcut Returns a data frame with group memberships and consensus tree.
Author(s)
F. de Mendiburu
See Also
hclust,consensus,hgroups
Examples
library(agricolae)
data(pamCIP)
# only code
rownames(pamCIP)<-substr(rownames(pamCIP),1,6)
# groups of clusters
# output<-consensus(pamCIP,nboot=100)
# hcut(output,h=0.4,group=5,main="Group 5")
#
# hcut(output,h=0.4,group=8,type="t",edgePar=list(lty=1:2,col=2:1),main="group 8"
# ,col.text="blue",cex.text=1)
heterosis 67
heterosis Data of potato, Heterosis
Description
Determination of heterosis, general combining ability (GCA) and specific combining ability in tuber
dry matter, reducing sugars and agronomic characteristics in TPS families.
Usage
data(heterosis)
Format
A data frame with 216 observations on the following 11 variables.
Place 1: La Molina, 2=Huancayo
Replication a numeric vector
Treatment a numeric vector
Factor a factor with levels Control progenie progenitor testigo
Female a factor with levels Achirana LT-8 MF-I MF-II Serrana TPS-2 TPS-25 TPS-7
Male a factor with levels TPS-13 TPS-67 TS-15
v1 Yield (Kg/plant)
v2 Reducing sugars (scale):1 low and 5=High
v3 Tuber dry matter (percentage)
v4 Tuber number/plant
v5 Average tuber weight (g)
Details
The study was conducted in 3 environments, La Molina-PERU to 240 masl. during autumn-winter
and spring, and in Huancayo-PERU 3180 masl., during summer. The experimental material con-
sisted of 24 families half brother in the form of tubers derived from TPS, obtained crossing between
8 female and 3 male parents. Design used was randomized complete block with three repetitions.
The experimental unit was 30 plants in two rows at a distance of 30cm between plants and 90 cm
between rows. Variables evaluated were Yield, Tubers number, Dry matter and content and reducing
sugars. The analysis was conducted line x tester. The control variety was Desiree.
Source
International Potato Center(CIP). Lima-Peru. Data Kindly provided by of Rolando Cabello.
68 hgroups
References
Tesis "Heterosis, habilidad combinatoria general y especifica para materia seca, azucares reduc-
tores y caracteres agronomicos en familias de tuberculos provenientes de semilla sexual de papa.
Magister Scientiae Rodolfo Valdivia Lorente. Universidad Nacional Agraria La molina-Lima Peru,
Escuela de Post Grado, Mejoramiento genetico de plantas, 2004". Poster: Congreso de la Sociedad
Peruana de Genetica - Peru, 2008.
Examples
library(agricolae)
data(heterosis)
str(heterosis)
site1<-subset(heterosis,heterosis[,1]==1)
site2<-subset(heterosis,heterosis[,1]==2)
site3<-subset(heterosis,heterosis[,1]==3)
model1<-with(site1,lineXtester(Replication, Female, Male, v1))
DFe <- df.residual(model1)
CMe <- deviance(model1)/DFe
test1 <- with(site1,HSD.test(v1, Factor,DFe,CMe))
test2 <- with(site1,HSD.test(v1, Treatment,DFe,CMe))
model22<-with(site2,lineXtester(Replication, Female, Male, v3))
model3<-with(site3,lineXtester(Replication, Female, Male, v4))
hgroups groups of hclust
Description
Returns a vector with group memberships. This function is used by the function consensus of
clusters.
Usage
hgroups(hmerge)
Arguments
hmerge The object is components of the hclust
Value
The merge clusters is printed.
Author(s)
F. de Mendiburu
HSD.test 69
See Also
hclust,hcut,consensus
Examples
library(agricolae)
data(pamCIP)
# only code
rownames(pamCIP)<-substr(rownames(pamCIP),1,6)
distance <- dist(pamCIP,method="binary")
clusters<- hclust( distance, method="complete")
# groups of clusters
hgroups(clusters$merge)
HSD.test Multiple comparisons: Tukey
Description
It makes multiple comparisons of treatments by means of Tukey. The level by alpha default is 0.05.
Usage
HSD.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL,console=FALSE)
Arguments
ymodel(aov or lm) or answer of the experimental unit
trt Constant( only y=model) or vector treatment applied to each experimental unit
DFerror Degree free
MSerror Mean Square Error
alpha Significant level
group TRUE or FALSE
main Title
console logical, print output
Details
It is necessary first makes a analysis of variance.
Value
statistics Statistics of the model
parameters Design parameters
means Statistical summary of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
70 huasahuasi
Author(s)
Felipe de Mendiburu
References
1. Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third
Edition 1997 2. Multiple comparisons theory and methods. Departament of statistics the Ohio State
University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.
See Also
BIB.test,DAU.test,duncan.test,durbin.test,friedman,kruskal,LSD.test,Median.test,
PBIB.test,REGW.test,scheffe.test,SNK.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus, data=sweetpotato)
out <- HSD.test(model,"virus", group=TRUE,console=TRUE,
main="Yield of sweetpotato\nDealt with different virus")
#stargraph
# Variation range: max and min
plot(out)
#endgraph
out<-HSD.test(model,"virus", group=FALSE)
print(out$comparison)
# Old version HSD.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
with(sweetpotato,HSD.test(yield,virus,df,MSerror, group=TRUE,console=TRUE,
main="Yield of sweetpotato. Dealt with different virus"))
huasahuasi Data: Rainfall thresholds as support for timing fungicide applications
in the control of potato late blight in Peru
Description
Timing fungicide sprays based on accumulated rainfall thresholds can be a successful component
of integrated management packages that include cultivars with moderate or high levels of resistance
to late blight. The simplicity of measuring accumulated rainfall means that the technology can
potentially be used by resource-poor farmers in developing countries.
Usage
data(huasahuasi)
huasahuasi 71
Format
The format is: List of 2 ( AUDPC, YIELD )
block a factor with levels I II III
trt a factor with levels 40mm 7-days Non-application
clon a factor with levels C386209.10 C387164.4 Cruza148 Musuq Yungay
y1da a numeric vector, Kgr./plot
y2da a numeric vector, Kgr./plot
y3ra a numeric vector, Kgr./plot
d44 a numeric vector, 44 days
d51 a numeric vector, 51 days
d100 a numeric vector, 100 days
Details
The experimental unit was formed by 4 furrows of 1.8 m of length, with distance between furrows
from 0.90 m and between plants of 0.30 m. In each furrow was installed 5 plants. The experiment
had 3 repetitions. From the beginning of the experiment were fulfilled the following treatments
Thresholds 40 mm: Apply the fungicide when 40 precipitation mm accumulates. The minimum
interval between applications will be of 7 days. Schedule 7 days: The applications should be
carried out every 7 days calendar. Without application: No fungicide application will be made. The
evaluation of the severity of the late blight in each treatment started to emergency 80 percentage
and then evaluations were made every 7 days until being observed a physiological maturation of the
crop.
Source
Experimental field, 2003. Data Kindly provided by Wilmer Perez.
References
International Potato Center. CIP - Lima Peru.
Examples
library(agricolae)
data(huasahuasi)
names(huasahuasi)
str(huasahuasi$AUDPC)
str(huasahuasi$YIELD)
72 index.AMMI
index.AMMI AMMI index and yield stability
Description
calculate AMMI stability value (ASV) and Yield stability index (YSI).
Usage
index.AMMI(model)
Arguments
model object AMMI
Details
AMMI stability value (ASV) was calculated using the following formula, as suggested by Purchase
(1997)
ASV = sqrt(SSpc1/SSpc2 *(PC1i)^2+(PC2i)^2)
YSI = RASV + RY
RASV = rank(ASV) and RY = rank(Y across by environment)
Value
ASV AMMI stability value
YSI Yield stability index
rASV Rank of AMMI stability value
rYSI Rank of yield stability index
means average genotype by environment
Author(s)
F. de Mendiburu
References
The use of an AMMI model and its parameters to analyse yield stability in multienvironment trials.
N. SABAGHNIA, S.H. SABAGHPOUR AND H. DEHGHANI. Journal of Agricultural Science
(2008), 146, 571-581. f 2008 Cambridge University Press 571 doi:10.1017/S0021859608007831
Printed in the United Kingdom
Parametric analysis to describe genotyperenvironment interaction and yield stability in winter wheat.
PURCHASE, J. L. (1997). Ph.D. Thesis, Department of Agronomy, Faculty of Agriculture of the
University of the Free State, Bloemfontein, South Africa.
index.bio 73
See Also
AMMI,plot.AMMI
Examples
library(agricolae)
# Index AMMI
data(plrv)
model<- with(plrv,AMMI(Locality, Genotype, Rep, Yield, console=FALSE))
Idx<-index.AMMI(model)
names(Idx)
# Crops with improved stability according AMMI.
print(Idx[order(Idx[,3]),])
# Crops with better response and improved stability according AMMI.
print(Idx[order(Idx[,4]),])
index.bio Biodiversity Index
Description
Scientists use a formula called the biodiversity index to describe the amount of species diversity in
a given area.
Usage
index.bio(data, method = c("Margalef", "Simpson.Dom", "Simpson.Div",
"Berger.Parker", "McIntosh", "Shannon"), level=95, nboot=100, console=TRUE)
Arguments
data number of specimens
method Describe method bio-diversity
level Significant level
nboot size bootstrap
console output console TRUE
Details
method bio-diversity. "Margalef" "Simpson.Dom" "Simpson.Div" "Berger.Parker" "McIntosh" "Shan-
non"
Value
Index and confidence intervals.
74 index.smith
Author(s)
Felipe de Mendiburu
References
Magurran, A.E. (1988) Ecological diversity and its measurement. Princeton University Press Efron,
B., Tibshirani, R. (1993) An Introduction to the Boostrap. Chapman and Hall/CRC
Examples
library(agricolae)
data(paracsho)
# date 22-06-05 and treatment CON = application with insecticide
specimens <- paracsho[1:10,6]
output1 <- index.bio(specimens,method="Simpson.Div",level=95,nboot=100)
output2 <- index.bio(specimens,method="Shannon",level=95,nboot=100)
rbind(output1, output2)
index.smith Uniformity soil. Smith’s Index of Soil Heterogeneity
Description
Smith’s index of soil heterogeneity is used primarily to derive optimum plot size. The index gives a
single value as a quantitative measure of soil heterogeneity in an area. Graph CV for plot size and
shape.
Usage
index.smith(data, PLOT=TRUE,...)
Arguments
data dataframe or matrix
PLOT graphics, TRUE or FALSE
... Parameters of the plot()
Details
Vx=V(x)/x b
V(x) is the between-plot variance, Vx is the variance per unit area for plot size of x basic unit, and
b is the Smith’ index of soil heterogeneity.
Value
model function pattern of uniformity
uniformity Table of the soil uniformity
intervals.freq 75
Author(s)
Felipe de Mendiburu
References
Statistical Procedures for Agriculture Research. Second Edition. Kwanchai A. Gomez and Arturo
A. Gomez. 1976. USA
Examples
library(agricolae)
data(rice)
#startgraph
table<-index.smith(rice,
main="Relationship between CV per unit area and plot size",col="red")
#endgraph
uniformity <- data.frame(table$uniformity)
uniformity
# regression variance per unit area an plot size.
model <- lm(Vx ~ I(log(Size)),uniformity)
coeff <- coef(model)
x<-1:max(uniformity$Size)
Vx<- coeff[1]+coeff[2]*log(x)
#startgraph
plot(x,Vx, type="l", col="blue",
main="Relationship between variance per unit area and plot size")
points(uniformity$Size,uniformity$Vx)
#endgraph
intervals.freq Class intervals
Description
List class intervals.
Usage
intervals.freq(x)
Arguments
xclass graph.freq, histogram or numeric
Value
It show interval classes.
76 join.freq
Author(s)
Felipe de Mendiburu
See Also
polygon.freq,table.freq,stat.freq,graph.freq,sturges.freq,join.freq,ogive.freq,
normal.freq
Examples
library(agricolae)
# example 1
data(growth)
h<-hist(growth$height,plot=FALSE)
intervals.freq(h)
# example 2
x<-seq(10,40,5)
y<-c(2,6,8,7,3,4)
intervals.freq(x)
histogram <- graph.freq(x,counts=y)
join.freq Join class for histogram
Description
In many situations it is required to join classes because of the low .frequency in the intervals. In this
process, it is required to join the intervals and ad the .frequencies of them.
Usage
join.freq(histogram, join)
Arguments
histogram Class graph.freq
join vector
Value
New histogram with union of classes.
Author(s)
Felipe de Mendiburu
kendall 77
See Also
polygon.freq,table.freq,stat.freq,intervals.freq,sturges.freq,graph.freq,ogive.freq,
normal.freq
Examples
library(agricolae)
data(natives)
# histogram
h1<-graph.freq(natives$size,plot=FALSE)
round(table.freq(h1),4)
# Join classes 9, 10,11 and 12 with little frequency.
h2<-join.freq(h1,9:12)
# new table
plot(h2,col="bisque",xlab="Size")
round(summary(h2),4)
kendall Correlation of Kendall
Description
Correlation of Kendall two set. Compute exact p-value with ties.
Usage
kendall(data1, data2)
Arguments
data1 vector
data2 vector
Value
The correlation of data1, data2 vector with the statistical value and its probability
Author(s)
Felipe de Mendiburu
References
Numerical Recipes in C. Second Edition. Pag 634
See Also
correlation
78 kruskal
Examples
library(agricolae)
x <-c(1,1,1,4,2,2,3,1,3,2,1,1,2,3,2,1,1,2,1,2)
y <-c(1,1,2,3,4,4,2,1,2,3,1,1,3,4,2,1,1,3,1,2)
kendall(x,y)
kruskal Kruskal Wallis test and multiple comparison of treatments.
Description
It makes the multiple comparison with Kruskal-Wallis. The alpha parameter by default is 0.05. Post
hoc test is using the criterium Fisher’s least significant difference. The adjustment methods include
the Bonferroni correction and others.
Usage
kruskal(y, trt, alpha = 0.05, p.adj=c("none","holm","hommel",
"hochberg", "bonferroni", "BH", "BY", "fdr"), group=TRUE, main = NULL,console=FALSE)
Arguments
yresponse
trt treatment
alpha level signification
p.adj Method for adjusting p values (see p.adjust)
group TRUE or FALSE
main Title
console logical, print output
Details
For equal or different repetition.
For the adjustment methods, see the function p.adjusted.
p-adj = "none" is t-student.
Value
statistics Statistics of the model
parameters Design parameters
means Statistical summary of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
kurtosis 79
Author(s)
Felipe de Mendiburu
References
Practical Nonparametrics Statistics. W.J. Conover, 1999
See Also
BIB.test,DAU.test,duncan.test,durbin.test,friedman,HSD.test,LSD.test,Median.test,
PBIB.test,REGW.test,scheffe.test,SNK.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
data(corn)
str(corn)
comparison<-with(corn,kruskal(observation,method,group=TRUE, main="corn"))
comparison<-with(corn,kruskal(observation,method,p.adj="bon",group=FALSE, main="corn"))
kurtosis Finding the Kurtosis coefficient
Description
It obtains the value of the kurtosis for a normally distributed variable. The result is similar to SAS.
Usage
kurtosis(x)
Arguments
xa numeric vector
Value
xThe kurtosis of x
See Also
skewness
Examples
library(agricolae)
x<-c(3,4,5,2,3,4,5,6,4,NA,7)
kurtosis(x)
# value is -0.1517996
80 lateblight
lastC Setting the last character of a chain
Description
A special function for the group of treatments in the multiple comparison tests. Use plot.group.
Usage
lastC(x)
Arguments
xletters
Value
xReturns the last character of a string
Author(s)
Felipe de Mendiburu
See Also
plot.group
Examples
library(agricolae)
x<-c("a","ab","b","c","cd")
lastC(x)
# "a" "b" "b" "c" "d"
lateblight LATEBLIGHT - Simulator for potato late blight Version LB2004
Description
LATEBLIGHT is a mathematical model that simulates the effect of weather, host growth and resis-
tance, and fungicide use on asexual development and growth of Phytophthora infestans on potato
foliage.
lateblight 81
Usage
lateblight(WS, Cultivar, ApplSys,InocDate, LGR, IniSpor, SR, IE, LP, InMicCol,
MatTime=c('EARLYSEASON','MIDSEASON','LATESEASON'),...)
Arguments
WS object weather-severity
Cultivar chr
ApplSys chr
InocDate days
LGR num, see example
IniSpor num
SR num, see example
IE num, Initialization infection
LP num, latent period
InMicCol num
MatTime chr
... plot graphics parameters
Details
LATEBLIGHT Version LB2004 was created in October 2004 (Andrade-Piedra et al., 2005a, b and
c), based on the C-version written by B.E. Ticknor (’BET 21191 modification of cbm8d29.c’),
reported by Doster et al. (1990) and described in detail by Fry et al. (1991) (This version is referred
as LB1990 by Andrade-Piedra et al. [2005a]). The first version of LATEBLIGHT was developed
by Bruhn and Fry (1981) and described in detail by Bruhn et al. (1980).
Value
Ofile "Date","nday","MicCol","SimSeverity",...
Gfile "dates","nday","MeanSeverity","StDevSeverity"
Note
All format data for date is yyyy-mm,dd, for example "2000-04-22". change with function as.Date()
Author(s)
Jorge L. Andrade-Piedra (1) (j.andrade@cgar.org), Gregory A. Forbes (1) (g.forbes@cgiar.org),
Robert J. Hijmans (2) (rhijmans@ucdavis.edu), William E. Fry (3) (wef1@cornell.edu) Translation
from C language into SAS language: G.A. Forbes Modifications: J.L. Andrade-Piedra and R.J.
Hijmans Translation from SAS into R: Felipe de Mendiburu (1) (1) International Potato Center,
P.O. Box 1558, Lima 12, Peru (2) University of California, One Shields Avenue, Davis, California
95616, USA (3) Cornell University, 351 Plant Science, Ithaca, NY 14853, USA
82 lateblight
References
Andrade-Piedra, J. L., Hijmans, R. J., Forbes, G. A., Fry, W. E., and Nelson, R. J. 2005a. Simula-
tion of potato late blight in the Andes. I: Modification and parameterization of the LATEBLIGHT
model. Phytopathology 95:1191-1199.
Andrade-Piedra, J. L., Hijmans, R. J., Juarez, H. S., Forbes, G. A., Shtienberg, D., and Fry, W. E.
2005b. Simulation of potato late blight in the Andes. II: Validation of the LATEBLIGHT model.
Phytopathology 95:1200-1208.
Andrade-Piedra, J. L., Forbes, G. A., Shtienberg, D., Grunwald, N. J., Chacon, M. G., Taipe,
M. V., Hijmans, R. J., and Fry, W. E. 2005c. Qualification of a plant disease simulation model:
Performance of the LATEBLIGHT model across a broad range of environments. Phytopathology
95:1412-1422.
Bruhn, J.A., Bruck, R.I., Fry, W.E., Arneson, P.A., and Keokosky, E.V. 1980. User’s manual for
LATEBLIGHT: a plant disease management game. Cornell University, Department of Plant Pathol-
ogy, Ithaca, NY, USA. Mimeo 80-1.
Bruhn, J.A., and Fry, W.E. 1981. Analysis of potato late blight epidemiology by simulation model-
ing. Phytopathology 71:612-616.
Doster, M. A., Milgroom, M. G., and Fry, W. E. 1990. Quantification of factors influencing potato
late blight suppression and selection for metalaxyl resistance in Phytophthora infestans - A simula-
tion approach. Phytopathology 80:1190-1198.
Fry, W.E., Milgroom, M.G., Doster, M.A., Bruhn, J.A., and Bruck, R.I. 1991. LATEBLIGHT: a
plant disease management game - User Manual. Version 3.1. Microsoft Windows Adaptation by B.
E. Ticknor, and P. A. Arneson. Ithaca, Cornell University, Department of Plant Pathology, Ithaca,
NY, USA.
See Also
weatherSeverity
Examples
library(agricolae)
f <- system.file("external/weather.csv", package="agricolae")
weather <- read.csv(f,header=FALSE)
f <- system.file("external/severity.csv", package="agricolae")
severity <- read.csv(f)
weather[,1]<-as.Date(weather[,1],format = "%m/%d/%Y")
# Parameters dates
dates<-c("2000-03-25","2000-04-09","2000-04-12","2000-04-16","2000-04-22")
dates<-as.Date(dates)
EmergDate <- as.Date('2000/01/19')
EndEpidDate <- as.Date("2000-04-22")
dates<-as.Date(dates)
NoReadingsH<- 1
RHthreshold <- 90
WS<-weatherSeverity(weather,severity,dates,EmergDate,EndEpidDate,
NoReadingsH,RHthreshold)
# Parameters Lateblight
InocDate<-"2000-03-18"
LGR <- 0.00410
IniSpor <- 0
lineXtester 83
SR <- 292000000
IE <- 1.0
LP <- 2.82
InMicCol <- 9
Cultivar <- 'NICOLA'
ApplSys <- "NOFUNGICIDE"
main<-"Cultivar: NICOLA"
#--------------------------
model<-lateblight(WS, Cultivar,ApplSys, InocDate, LGR,IniSpor,SR,IE, LP,
MatTime='LATESEASON',InMicCol,main=main,type="l",xlim=c(65,95),lwd=1.5,
xlab="Time (days after emergence)", ylab="Severity (Percentage)")
# reproduce graph
x<- model$Ofile$nday
y<- model$Ofile$SimSeverity
w<- model$Gfile$nday
z<- model$Gfile$MeanSeverity
Min<-model$Gfile$MinObs
Max<-model$Gfile$MaxObs
plot(x,y,type="l",xlim=c(65,95),lwd=1.5,xlab="Time (days after emergence)",
ylab="Severity (Percentage)")
points(w,z,col="blue",cex=1,pch=19)
npoints <- length(w)
for ( i in 1:npoints){
segments(w[i],Min[i],w[i],Max[i],lwd=1.5,col="blue")
}
legend("topleft",c("Disease progress curves","Weather-Severity"),
title="Description",lty=1,pch=c(3,19),col=c("black","blue"))
lineXtester Line x Tester Analysis
Description
It makes the Line x Tester Genetic Analysis. It also estimates the general and specific combinatory
ability effects and the line and tester genetic contribution.
Usage
lineXtester(replications, lines, testers, y)
Arguments
replications Replications
lines Lines
testers Testers
yVariable, response
84 lineXtester
Details
ANOVA with parents and crosses
ANOVA for line X tester analysis
ANOVA for line X tester analysis including parents
GCA Effects: Lines Effects, Testers Effects and SCA Effects.
Standard Errors for Combining Ability Effects.
Genetic Components.
...
Proportional contribution of lines, testers and their interactions to total variance
Value
return anova(formula = Y ~ Replications + Treatments).
where the Treatments contains parents, crosses and crosses vs Parents.
The crosses contains Lines, Testers and its interaction .
Author(s)
Felipe de Mendiburu
References
Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979. Hierarchial and
factorial mating designs for quantitative genetic analysis in tetrasomic potato. R. Ortis; A.Golmirzaie.
Theor Appl Genet (2002) 104:675-679
See Also
AMMI
Examples
# see structure line by testers
library(agricolae)
# example 1
data(heterosis)
site1<-subset(heterosis,heterosis[,1]==1)
output1<-with(site1,lineXtester(Replication, Female, Male, v2))
# example 2
data(LxT)
str(LxT)
output2<-with(LxT,lineXtester(replication, line, tester, yield))
LSD.test 85
LSD.test Multiple comparisons, "Least significant difference" and Adjust P-
values
Description
Multiple comparisons of treatments by means of LSD and a grouping of treatments. The level by
alpha default is 0.05. Returns p-values adjusted using one of several methods
Usage
LSD.test(y, trt, DFerror, MSerror, alpha = 0.05, p.adj=c("none","holm","hommel",
"hochberg", "bonferroni", "BH", "BY", "fdr"), group=TRUE, main = NULL,console=FALSE)
Arguments
ymodel(aov or lm) or answer of the experimental unit
trt Constant( only y=model) or vector treatment applied to each experimental unit
DFerror Degrees of freedom of the experimental error
MSerror Means square error of the experimental
alpha Level of risk for the test
p.adj Method for adjusting p values (see p.adjust)
group TRUE or FALSE
main title of the study
console logical, print output
Details
For equal or different repetition.
For the adjustment methods, see the function p.adjusted.
p-adj ="none" is t-student.
Value
statistics Statistics of the model
parameters Design parameters
means Statistical summary of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
Author(s)
Felipe de Mendiburu
86 LxT
References
Steel, R.; Torri,J; Dickey, D.(1997) Principles and Procedures of Statistics A Biometrical Approach.
pp178.
See Also
BIB.test,DAU.test,duncan.test,durbin.test,friedman,HSD.test,kruskal,Median.test,
PBIB.test,REGW.test,scheffe.test,SNK.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus, data=sweetpotato)
out <- LSD.test(model,"virus", p.adj="bonferroni")
#stargraph
# Variation range: max and min
plot(out)
#endgraph
# Old version LSD.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
out <- with(sweetpotato,LSD.test(yield,virus,df,MSerror))
#stargraph
# Variation interquartil range: Q75 and Q25
plot(out,variation="IQR")
#endgraph
out<-LSD.test(model,"virus",p.adj="hommel",console=TRUE)
plot(out,variation="SD") # variation standard deviation
LxT Data Line by tester
Description
Data frame with yield by line x tester.
Usage
data(LxT)
Format
A data frame with 92 observations on the following 4 variables.
replication a numeric vector
line a numeric vector
tester a numeric vector
yield a numeric vector
markers 87
Source
Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979
markers Data of molecular markers
Description
A partial study on 27 molecular markers.
Usage
data(markers)
Format
A data frame with 23 observations on the following 27 variables.
marker1 a numeric vector
marker2 a numeric vector
marker3 a numeric vector
marker4 a numeric vector
marker5 a numeric vector
marker6 a numeric vector
marker7 a numeric vector
marker8 a numeric vector
marker9 a numeric vector
marker10 a numeric vector
marker11 a numeric vector
marker12 a numeric vector
marker13 a numeric vector
marker14 a numeric vector
marker15 a numeric vector
marker16 a numeric vector
marker17 a numeric vector
marker18 a numeric vector
marker19 a numeric vector
marker20 a numeric vector
marker21 a numeric vector
marker22 a numeric vector
88 Median.test
marker23 a numeric vector
marker24 a numeric vector
marker25 a numeric vector
marker26 a numeric vector
marker27 a numeric vector
Source
International Potato Center Lima-Peru.
References
International Potato Center Lima-Peru.
Examples
library(agricolae)
data(markers)
str(markers)
Median.test Median test. Multiple comparisons
Description
A nonparametric test for several independent samples. The median test is designed to examine
whether several samples came from populations having the same median.
Usage
Median.test(y,trt,alpha=0.05,correct=TRUE,simulate.p.value = FALSE, group = TRUE,
main = NULL,console=TRUE)
Arguments
yVariable response
trt Treatments
alpha error type I
correct a logical indicating whether to apply continuity correction when computing the
test statistic for 2 groups. The correction will not be bigger than the differences
themselves. No correction is done if simulate.p.value = TRUE.
simulate.p.value
a logical indicating whether to compute p-values by Monte Carlo simulation
group TRUE or FALSE
main Title
console logical, print output
melon 89
Details
The data consist of k samples of possibly unequal sample size.
Greater: is the number of values that exceed the median of all data and
LessEqual: is the number less than or equal to the median of all data.
Value
statistics Statistics of the model
parameters Design parameters
medians Statistical summary of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
Author(s)
Felipe de Mendiburu
References
Practical Nonparametrics Statistics. W.J. Conover, 1999
See Also
BIB.test,DAU.test,duncan.test,durbin.test,friedman,HSD.test,kruskal,LSD.test,
PBIB.test,REGW.test,scheffe.test,SNK.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
# example 1
data(corn)
out<-with(corn,Median.test(observation,method,console=FALSE))
z<-bar.err(out$medians,variation = "range",ylim=c(0,120),
space=2,border=4,col=3,density=10,angle=45)
# example 2
out<-with(corn,Median.test(observation,method,console=FALSE,group=FALSE))
print(out$comparison)
melon Data of yield of melon in a Latin square experiment
Description
An irrigation system evaluation by exudation using four varieties of melon, under modality of sow-
ing, SIMPLE ROW. The goal is to analyze the behavior of three hybrid melon varieties and one
standard.
90 montecarlo
Usage
data(melon)
Format
A data frame with 16 observations on the following 4 variables.
row a numeric vector
col a numeric vector
variety a factor with levels V1 V2 V3 V4
yield a numeric vector
Details
Varieties: Hibrido Mission (V1); Hibrido Mark (V2); Hibrido Topfligth (V3); Hibrido Hales Best
Jumbo (V4).
Source
Tesis. "Evaluacion del sistema de riego por exudacion utilizando cuatro variedades de melon, bajo
modalidad de siembra, SIMPLE HILERA". Alberto Angeles L. Universidad Agraria la Molina -
Lima Peru.
References
Universidad Nacional Agraria la molina.
Examples
library(agricolae)
data(melon)
str(melon)
montecarlo Random generation by Montecarlo
Description
Random generation form data, use function density and parameters
Usage
montecarlo(data, k, ...)
natives 91
Arguments
data vector or object(hist, graph.freq)
knumber of simulations
... Other parameters of the function density, only if data is vector
Value
Generate random numbers with empirical distribution.
Author(s)
Felipe de Mendiburu
See Also
density
Examples
library(agricolae)
r<-rnorm(50, 10,2)
montecarlo(r, k=100, kernel="epanechnikov")
# other example
h<-hist(r,plot=FALSE)
montecarlo(h, k=100)
# other example
breaks<-c(0, 150, 200, 250, 300)
counts<-c(10, 20, 40, 30)
par(mfrow=c(1,2),cex=0.8,mar=c(2,3,0,0))
h1<-graph.freq(x=breaks,counts=counts,plot=FALSE)
r<-montecarlo(h, k=1000)
plot(h1,frequency = 3,ylim=c(0,0.008))
text(90,0.006,"Population\n100 obs.")
h2<-graph.freq(r,breaks,frequency = 3,ylim=c(0,0.008))
lines(density(r),col="blue")
text(90,0.006,"Montecarlo\n1000 obs.")
natives Data of native potato
Description
An evaluation of the number, weight and size of 24 native potatoes varieties.
Usage
data(natives)
92 nonadditivity
Format
A data frame with 876 observations on the following 4 variables.
variety a numeric vector
number a numeric vector
weight a numeric vector
size a numeric vector
Source
International Potato Center. CIP - Lima Peru.
Examples
library(agricolae)
data(natives)
str(natives)
nonadditivity Nonadditivity model test
Description
The resistance for the transformable nonadditivity, due to J. W. Tukey, is based on the detection of
a curvilinear relation between y-est(y) and est(y). A freedom degree for the transformable nonad-
ditivity.
Usage
nonadditivity(y, factor1, factor2, df, MSerror)
Arguments
yAnswer of the experimental unit
factor1 Firts treatment applied to each experimental unit
factor2 Second treatment applied to each experimental unit
df Degrees of freedom of the experimental error
MSerror Means square error of the experimental
Details
Only two factor: Block and treatment or factor 1 and factor 2.
Value
P, Q and non-additivity analysis of variance
normal.freq 93
Author(s)
Felipe de Mendiburu
References
1. Steel, R.; Torri,J; Dickey, D.(1997) Principles and Procedures of Statistics A Biometrical Ap-
proach
2. George E.P. Box; J. Stuart Hunter and William G. Hunter. Statistics for experimenters. Wile
Series in probability and statistics
Examples
library(agricolae)
data(potato )
potato[,1]<-as.factor(potato[,1])
model<-lm(cutting ~ date + variety,potato)
df<-df.residual(model)
MSerror<-deviance(model)/df
analysis<-with(potato,nonadditivity(cutting, date, variety, df, MSerror))
normal.freq Normal curve on the histogram
Description
A normal distribution graph elaborated from the histogram previously constructed. The average and
variance are obtained from the data grouped in the histogram.
Usage
normal.freq(histogram, frequency=1, ...)
Arguments
histogram object constructed by the function hist
frequency 1=counts, 2=relative, 3=density
... Other parameters of the function hist
Author(s)
Felipe de Mendiburu
See Also
polygon.freq,table.freq,stat.freq,intervals.freq,sturges.freq,join.freq,ogive.freq,
graph.freq
94 ogive.freq
Examples
library(agricolae)
data(growth)
#startgraph
h1<-with(growth,hist(height,col="green",xlim=c(6,14)))
normal.freq(h1,col="blue")
#endgraph
#startgraph
h2<-with(growth,graph.freq(height,col="yellow",xlim=c(6,14),frequency=2))
normal.freq(h2,frequency=2)
#endgraph
ogive.freq Plotting the ogive from a histogram
Description
It plots the cumulative relative .frequencies with the intervals of classes defined in the histogram.
Usage
ogive.freq(histogram,type="",xlab="",ylab="",axes="",las=1,...)
Arguments
histogram object created by the function hist() or graph.freq()
type what type of plot should be drawn. See plot()
xlab x labels
ylab y labels
axes TRUE or FALSE
las numeric in 0,1,2,3; the style of axis labels. see plot()
... Parameters of the plot()
Value
Ogive points.
Author(s)
Felipe de Mendiburu
See Also
polygon.freq,table.freq,stat.freq,intervals.freq,sturges.freq,join.freq,graph.freq,
normal.freq
order.group 95
Examples
library(agricolae)
data(growth)
h<-graph.freq(growth$height,plot=FALSE)
points<-ogive.freq(h,col="red",frame=FALSE,
xlab="Height", ylab="Accumulated relative frequency", main="ogive")
plot(points,type="b",pch=16,las=1,bty="l")
order.group Ordering the treatments according to the multiple comparison
Description
This function allows us to compare the treatments averages or the adding of their ranges with the
minimal significant difference which can vary from one comparison to another one.
Usage
order.group(trt, means, N, MSerror, Tprob, std.err, parameter=1, snk=0,
DFerror=NULL,alpha=NULL,sdtdif=NULL,vartau=NULL,console)
Arguments
trt Treatments
means Means of treatment
NReplications
MSerror Mean square error
Tprob minimum value for the comparison
std.err standard error
parameter Constante 1 (Sd), 0.5 (Sx)
snk Constante = 1 (Student Newman Keuls)
DFerror Degrees of freedom of the experimental error
alpha Level of risk for the test
sdtdif standard deviation of difference in BIB
vartau matrix var-cov in PBIB
console logical, print output
Details
This function was changed by orderPvalue function that use agricolae. Now the grouping in agri-
colae is with the probability of the treatments differences and alpha level.
96 orderPvalue
Value
The output is data frame.
trt Treatment Levels, Factor
means height, Numeric
Mgroups levels, Factor
Nreplications, Numeric
std.err Standard error, Numeric
Author(s)
Felipe de Mendiburu
See Also
orderPvalue
Examples
library(agricolae)
treatments <- c("A","B","C","D","E","F")
means<-c(20,40,35,72,49,58)
std.err<-c(1.2, 2, 1.5, 2.4, 1, 3.1)
replications <- c(4,4,3,4,3,3)
MSerror <- 55.8
value.t <- 2.1314
groups<-order.group(treatments,means,replications,MSerror,value.t,std.err,console=FALSE)
print(groups)
orderPvalue Grouping the treatments averages in a comparison with a minimum
value
Description
When there are treatments and their respective values, these can be compared with a minimal dif-
ference of meaning.
Usage
orderPvalue(treatment, means, alpha, pvalue, console)
pamCIP 97
Arguments
treatment treatment
means means of treatment
alpha Alpha value, significante value to comparison
pvalue Matrix of probabilities to comparison
console logical, print output
Value
The means and groups for treatments
Author(s)
Felipe de Mendiburu
Examples
library(agricolae)
treatments <- c("A","B","C")
means<-c(2,5,3)
alpha <- 0.05
pvalue<-matrix(1,nrow=3,ncol=3)
pvalue[1,2]<-pvalue[2,1]<-0.03
pvalue[1,3]<-pvalue[3,1]<-0.10
pvalue[2,3]<-pvalue[3,2]<-0.06
out<-orderPvalue(treatments,means,alpha,pvalue,console=TRUE)
barplot(out[,1],names.arg = row.names(out),col=colors()[84:87])
legend("topright",as.character(out$groups),pch=15,col=colors()[84:87],box.col=0)
pamCIP Data Potato Wild
Description
Potato Wild
Usage
data(pamCIP)
Format
A data frame with 43 observations on the following 107 variables. Rownames: code and genotype’s
name. column data: molecular markers.
Details
To study the molecular markers in Wild.
98 paracsho
Source
Laboratory data.
References
International Potato Center Lima-Peru (CIP)
Examples
library(agricolae)
data(pamCIP)
str(pamCIP)
paracsho Data of Paracsho biodiversity
Description
A locality in Peru. A biodiversity.
Usage
data(paracsho)
Format
A data frame with 110 observations on the following 6 variables.
date a factor with levels 15-12-05 17-11-05 18-10-05 20-09-05 22-06-05 23-08-05 28-07-05
plot a factor with levels PARACSHO
Treatment a factor with levels CON SIN
Orden a factor with levels COLEOPTERA DIPTERA HEMIPTERA HYMENOPTERA LEPIDOPTERA NEUROPTERA
NEUROPTERO NOCTUIDAE
Family a factor with levels AGROMYZIDAE ANTHOCORIDAE ANTHOMYIIDAE ANTHOMYLIDAE BLEPHAROCERIDAE
BRACONIDAE BROCONIDAE CALUPHORIDAE CECIDOMYIDAE CHENEUMONIDAE CHNEUMONIDAE CHRYOMELIDAE
CICADELLIDAE CULICIDAE ERIOCPAMIDAE HEMEROBIIDAE ICHNEUMONIDAE LOUCHAPIDAE MIRIDAE
MUSCIDAE MUSICADAE MUSLIDAE MYCETOPHILIDAE MYCETOPHILIIDAE NENPHALIDAE NOCLUIDAE
NOCTERIDAE NOCTUIDAE PERALIDAE PIPUNCULIDAE PROCTOTRUPIDAE PSYLLIDAE PYRALIDAE
SARCOPHAGIDAE SARCOPILAGIDAE SCATOPHAGIDAE SCATOPHOGIDAE SCIARIDAE SERSIDAE SYRPHIDAE
TACHINIDAE TIPULIDAE
Number.of.specimens a numeric vector
Details
Country Peru, Deparment Junin, province Tarma, locality Huasahuasi.
path.analysis 99
Source
Entomology dataset.
References
International Potato Center.
Examples
library(agricolae)
data(paracsho)
str(paracsho)
path.analysis Path Analysis
Description
If the cause and effect relationship is well defined, it is possible to represent the whole system of
variables in a diagram form known as path-analysis. The function calculates the direct and indirect
effects and uses the variables correlation or covariance.
Usage
path.analysis(corr.x, corr.y)
Arguments
corr.x Matrix of correlations of the independent variables
corr.y vector of dependent correlations with each one of the independent ones
Details
It is necessary first to calculate the correlations.
Value
Direct and indirect effects and residual Effect^2.
Author(s)
Felipe de Mendiburu
References
Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979
100 PBIB.test
See Also
correlation
Examples
# Path analysis. Multivarial Analysis. Anderson. Prentice Hall, pag 616
library(agricolae)
# Example 1
corr.x<- matrix(c(1,0.5,0.5,1),c(2,2))
corr.y<- rbind(0.6,0.7)
names<-c("X1","X2")
dimnames(corr.x)<-list(names,names)
dimnames(corr.y)<-list(names,"Y")
path.analysis(corr.x,corr.y)
# Example 2
# data of the progress of the disease related bacterial wilt to the ground
# for the component CE Ca K2 Cu
data(wilt)
data(soil)
x<-soil[,c(3,12,14,20)]
y<-wilt[,14]
cor.y<-correlation(y,x)$correlation
cor.x<-correlation(x)$correlation
path.analysis(cor.x,cor.y)
PBIB.test Analysis of the Partially Balanced Incomplete Block Design
Description
Analysis of variance PBIB and comparison mean adjusted. Applied to resoluble designs: Lattices
and alpha design.
Usage
PBIB.test(block,trt,replication,y,k, method=c("REML","ML","VC"),
test = c("lsd","tukey"), alpha=0.05, console=FALSE, group=TRUE)
Arguments
block blocks
trt Treatment
replication Replication
yResponse
kBlock size
method Estimation method: REML, ML and VC
PBIB.test 101
test Comparison treatments
alpha Significant test
console logical, print output
group logical, groups
Details
Method of comparison treatment. lsd: least significant difference. tukey: Honestly significant
difference. Estimate: specifies the estimation method for the covariance parameters. The REML
is the default method. The REML specification performs residual (restricted) maximum likelihood,
and The ML specification performs maximum likelihood, and the VC specifications apply only to
variance component models.
Value
ANOVA Analysis of variance
method Estimation method: REML, ML and VC
parameters Design parameters
statistics Statistics of the model
model Object: estimation model
Fstat Criterion AIC and BIC
comparison Comparison between treatments
means Statistical summary of the study variable
groups Formation of treatment groups
vartau Variance-Covariance Matrix
Author(s)
F. de Mendiburu
References
1. Iterative Analysis of Generalizad Lattice Designs. E.R. Williams (1977) Austral J. Statistics
19(1) 39-42.
2. Experimental design. Cochran and Cox. Second edition. Wiley Classics Library Edition pub-
lished 1992
See Also
BIB.test,DAU.test,duncan.test,durbin.test,friedman,HSD.test,kruskal,LSD.test,
Median.test,REGW.test,scheffe.test,SNK.test,waerden.test,waller.test,plot.group
102 plot.AMMI
Examples
require(agricolae)
# alpha design
Genotype<-c(paste("gen0",1:9,sep=""),paste("gen",10:30,sep=""))
ntr<-length(Genotype)
r<-2
k<-3
s<-10
obs<-ntr*r
b <- s*r
book<-design.alpha(Genotype,k,r,seed=5)
book$book[,3]<- gl(20,3)
dbook<-book$book
# dataset
yield<-c(5,2,7,6,4,9,7,6,7,9,6,2,1,1,3,2,4,6,7,9,8,7,6,4,3,2,2,1,1,2,
1,1,2,4,5,6,7,8,6,5,4,3,1,1,2,5,4,2,7,6,6,5,6,4,5,7,6,5,5,4)
rm(Genotype)
# not run
# analysis
# require(nlme) # method = REML or LM in PBIB.test and require(MASS) method=VC
model <- with(dbook,PBIB.test(block, Genotype, replication, yield, k=3, method="VC"))
# model$ANOVA
# plot(model,las=2)
plot.AMMI PLOT AMMI
Description
Biplot AMMI.
Usage
## S3 method for class 'AMMI'
plot(x,first=1,second=2,third=3,type=1,number=FALSE,gcol=NULL,ecol=NULL,
icol=NULL,angle=25,lwd=1.8,length=0.1,xlab=NULL,ylab=NULL,xlim=NULL,ylim=NULL,...)
Arguments
xobject AMMI
first position axis x, 0=Y-dependent, 1=PC1, 2=PC2, 3=PC3
second position axis y,0=Y-dependent, 1=PC1, 2=PC2, 3=PC3
third position axis z,0=Y-dependent, 1=PC1, 2=PC2, 3=PC3
type 1=biplot, 2= triplot 3=influence genotype
number TRUE or FALSE names or number genotypes
gcol genotype color
plot.AMMI 103
ecol environment color
icol influence color
angle angle from the shaft of the arrow to the edge of the arrow head
lwd parameter line width in function arrow
length parameter length in function arrow
xlab x labels
ylab y labels
xlim x limites
ylim y limites
... other parameters of plot
Details
type=1 produce graphs biplot. type=2 produce graphs triplot, the components are normalizad in
scale 0-1. type=3 produce graphs on a 2d point set that are all subgraphs of the Delaunay triangula-
tion with relative neighbor graph.
The relative neighbor graph is defined by the relation, x and y are neighbors if
d(x, y)≤min(max(d(x, z), d(y, z))|z∈S)
where d() is the distance, S is the set of BIPLOT points and z is an arbitrary point in S.
help(relativeneigh) package=spdep
Author(s)
Felipe de Mendiburu
See Also
AMMI
Examples
library(agricolae)
data(plrv)
model<- with(plrv,AMMI(Locality, Genotype, Rep, Yield))
# biplot PC2 vs PC1
plot(model)
## plot PC1 vs Yield
plot(model,0,1,gcol="blue",ecol="green")
## triplot PC 2,3,4
if (requireNamespace("klaR", quietly = TRUE)) {
plot(model,first=2,second=3,third=4, type=2,number=TRUE)
}
# biplot with influence genotype in pc3 vs pc2
if (requireNamespace("spdep", quietly = TRUE)) {
plot(model,first=2,second=3, type=3,number=TRUE,icol="green")
}
104 plot.graph.freq
plot.graph.freq Histogram
Description
In many situations it has intervals of class defined with its respective frequencies. By means of this
function, the graphic of frequency is obtained and it is possible to superpose the normal distribution,
polygon of frequency, Ojiva and to construct the table of complete frequency.
Usage
## S3 method for class 'graph.freq'
plot(x, breaks=NULL,counts=NULL,frequency=1,plot=TRUE,
nclass=NULL,xlab="",ylab="",axes = "",las=1,...)
Arguments
xa vector of values, a object hist(), graphFreq()
counts frequency and x is class intervals
breaks a vector giving the breakpoints between histogram cells
frequency 1=counts, 2=relative, 3=density
plot logic
nclass number of classes
xlab x labels
ylab y labels
axes TRUE or FALSE
las numeric in 0,1,2,3; the style of axis labels. see plot()
... other parameters of plot
Value
breaks a vector giving the breakpoints between histogram cells
counts frequency and x is class intervals
mids center point in class
relative Relative frequency, height
density Density frequency, height
Author(s)
Felipe de Mendiburu
plot.graph.freq 105
See Also
polygon.freq,table.freq,stat.freq,intervals.freq,sturges.freq,join.freq,ogive.freq,
normal.freq
Examples
library(agricolae)
data(genxenv)
yield <- subset(genxenv$YLD,genxenv$ENV==2)
yield <- round(yield,1)
h<- graph.freq(yield,axes=FALSE, frequency=1, ylab="frequency",col="yellow")
axis(1,h$breaks)
axis(2,seq(0,20,0.1))
# To reproduce histogram.
h1 <- plot(h, col="blue", frequency=2,border="red", density=8,axes=FALSE,
xlab="YIELD",ylab="relative")
axis(1,h$breaks)
axis(2,seq(0,.4,0.1))
# summary, only frecuency
limits <-seq(10,40,5)
frequencies <-c(2,6,8,7,3,4)
#startgraph
h<-graph.freq(limits,counts=frequencies,col="bisque",xlab="Classes")
polygon.freq(h,col="red")
title( main="Histogram and polygon of frequency",
ylab=".frequency")
#endgraph
# Statistics
measures<-stat.freq(h)
print(measures)
# frequency table full
round(table.freq(h),2)
#startgraph
# ogive
ogive.freq(h,col="red",type="b",ylab="Accumulated relative frequency",
xlab="Variable")
# only frequency polygon
h<-graph.freq(limits,counts=frequencies,border=FALSE,col=NULL,xlab=" ",ylab="")
title( main="Polygon of frequency",
xlab="Variable", ylab="Frecuency")
polygon.freq(h,col="blue")
grid(col="brown")
#endgraph
# Draw curve for Histogram
h<- graph.freq(yield,axes=FALSE, frequency=3, ylab="f(yield)",col="yellow")
axis(1,h$breaks)
axis(2,seq(0,0.18,0.03),las=2)
lines(density(yield), col = "red", lwd = 2)
title("Draw curve for Histogram")
106 plot.group
plot.group Plotting the multiple comparison of means
Description
It plots bars of the averages of treatments to compare. It uses the objects generated by a procedure
of comparison like LSD, HSD, Kruskall, Waller-Duncan, Friedman or Durbin. It can also display
the ’average’ value over each bar in a bar chart.
Usage
## S3 method for class 'group'
plot(x,variation=c("range","IQR","SE","SD"), horiz=FALSE,
col=NULL,xlim=NULL,ylim=NULL,main=NULL,...)
Arguments
xObject created by a test of comparison
variation in lines by range, IQR, standard deviation or error
horiz Horizontal or vertical image
col line colors
xlim optional, axis x limits
ylim optional, axis y limits
main optional, main title
... Parameters of the function barplot()
Details
The output is a vector that indicates the position of the treatments on the coordinate axes.
Author(s)
Felipe de Mendiburu
See Also
BIB.test,DAU.test,duncan.test,durbin.test,friedman,HSD.test,kruskal,LSD.test,
Median.test,PBIB.test,REGW.test,scheffe.test,SNK.test,waerden.test,waller.test
plots 107
Examples
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
comparison<- LSD.test(model,"virus",alpha=0.01,group=TRUE)
#startgraph
par(cex=1.5)
plot(comparison,horiz=TRUE,xlim=c(0,50),las=1)
title(cex.main=0.8,main="Comparison between\ntreatment means",xlab="Yield",ylab="Virus")
#endgraph
plots Data for an analysis in split-plot
Description
Experimental data in blocks, factor A in plots and factor B in sub-plots.
Usage
data(plots)
Format
A data frame with 18 observations on the following 5 variables.
block a numeric vector
plot a factor with levels p1 p2 p3 p4 p5 p6
Aa factor with levels a1 a2
Ba factor with levels b1 b2 b3
yield a numeric vector
Source
International Potato Center. CIP
Examples
library(agricolae)
data(plots)
str(plots)
plots[,1] <-as.factor(plots[,1])
# split-plot analysis
model <- aov(yield ~ block + A + Error(plot)+ B + A:B, data=plots)
summary(model)
b<-nlevels(plots$B)
a<-nlevels(plots$A)
108 plrv
r<-nlevels(plots$block)
dfa <- df.residual(model$plot)
Ea <-deviance(model$plot)/dfa
dfb <- df.residual(model$Within)
Eb <-deviance(model$Within)/dfb
Eab <- (Ea +(b-1)*Eb)/(b*r)
# Satterthwaite
dfab<-(Ea +(b-1)*Eb)^2/(Ea^2/dfa +((b-1)*Eb)^2/dfb)
# Comparison A, A(b1), A(b2), A(b3)
comparison1 <-with(plots,LSD.test(yield,A,dfa,Ea))
comparison2 <-with(plots,LSD.test(yield[B=="b1"],A[B=="b1"],dfab,Eab))
comparison3 <-with(plots,LSD.test(yield[B=="b2"],A[B=="b2"],dfab,Eab))
comparison4 <-with(plots,LSD.test(yield[B=="b3"],A[B=="b3"],dfab,Eab))
# Comparison B, B(a1), B(a2)
comparison5 <-with(plots,LSD.test(yield,B,dfb,Eb))
comparison6 <-with(plots,LSD.test(yield[A=="a1"],B[A=="a1"],dfb,Eb))
comparison7 <-with(plots,LSD.test(yield[A=="a2"],B[A=="a2"],dfb,Eb))
plrv Data clones from the PLRV population
Description
Six environments: Ayacucho, La Molina 02, San Ramon 02, Huancayo, La Molina 03, San Ramon
03.
Usage
data(plrv)
Format
A data frame with 504 observations on the following 6 variables.
Genotype a factor with levels 102.18 104.22 121.31 141.28 157.26 163.9 221.19 233.11
235.6 241.2 255.7 314.12 317.6 319.20 320.16 342.15 346.2 351.26 364.21 402.7
405.2 406.12 427.7 450.3 506.2 Canchan Desiree Unica
Locality a factor with levels Ayac Hyo-02 LM-02 LM-03 SR-02 SR-03
Rep a numeric vector
WeightPlant a numeric vector
WeightPlot a numeric vector
Yield a numeric vector
Source
International Potato Center Lima-Peru
polygon.freq 109
References
International Potato Center Lima-Peru
Examples
library(agricolae)
data(plrv)
str(plrv)
polygon.freq The polygon of frequency on the histogram
Description
The polygon is constructed single or on a histogram. It is necessary to execute the function previ-
ously hist.
Usage
polygon.freq(histogram, frequency=1, ...)
Arguments
histogram Object constructed by the function hist
frequency numeric, counts(1), relative(2) and density(3)
... Other parameters of the function hist
Author(s)
Felipe de Mendiburu Delgado
See Also
polygon.freq,table.freq,stat.freq,intervals.freq,sturges.freq,join.freq,graph.freq,
normal.freq
Examples
library(agricolae)
data(growth)
#startgraph
h1<-with(growth,hist(height,border=FALSE,xlim=c(6,14)))
polygon.freq(h1,frequency=1,col="red")
#endgraph
#startgraph
h2<-with(growth,graph.freq(height,frequency=2,col="yellow",xlim=c(6,14)))
polygon.freq(h2,frequency=2,col="red")
#endgraph
110 ralstonia
potato Data of cutting
Description
A study on the yield of two potato varieties performed at the CIP experimental station.
Usage
data(potato)
Format
A data frame with 18 observations on the following 4 variables.
date a numeric vector
variety a factor with levels Canchan Unica
harvest a numeric vector
cutting a numeric vector
Source
Experimental data.
References
International Potato Center
Examples
library(agricolae)
data(potato)
str(potato)
ralstonia Data of assessment of the population in the soil R.solanacearum
Description
The assessment of the population of R.solanacearum on the floor took place after 48 hours of infes-
tation, during days 15, 29, 43, 58, and 133 days after the infestation soil. More information on soil
data(soil).
Usage
data(ralstonia)
reg.homog 111
Format
A data frame with 13 observations on the following 8 variables.
place a factor with levels Chmar Chz Cnt1 Cnt2 Cnt3 Hco1 Hco2 Hco3 Hyo1 Hyo2 Namora SR1 SR2
Day2 a numeric vector
Day15 a numeric vector
Day29 a numeric vector
Day43 a numeric vector
Day58 a numeric vector
Day73 a numeric vector
Day133 a numeric vector
Details
Logarithm average counts of colonies on plates containing half of M-SMSA 3 repetitions (3 plates
by repetition) incubated at 30 degrees centigrade for 48 hours. log(1+UFC/g soil)
Source
Experimental field, 2004. Data Kindly provided by Dr. Sylvie Priou, Liliam Gutarra and Pedro
Aley.
References
International Potato Center. CIP - Lima Peru.
Examples
library(agricolae)
data(ralstonia)
str(ralstonia)
reg.homog Homologation of regressions
Description
It makes the regressions homogeneity test for a group of treatments where each observation presents
a linearly dependent reply from another one. There is a linear function in every treatment. The
objective is to find out if the linear models of each treatment come from the same population.
Usage
reg.homog(trt, x, y)
112 reg.homog
Arguments
trt treatment
xindependent variable
ydependent variable
Value
list objects:
Number regressions.
Residual.
Difference of regression.
DF.homgeneity (homogenity degree free).
DF.Residual (degree free error).
F.value. Test statitics.
P.value. P Value (Significant
Criterion. conclusion
Author(s)
Felipe de Mendiburu
References
Book in Spanish: Metodos estadisticos para la investigacion. Calzada Benza 1960
Examples
library(agricolae)
data(frijol)
evaluation<-with(frijol,reg.homog(technology,index,production))
# Example 2. Applied Regression Analysis a Research tools
# 1988. John O.Rawlings. Wadsworth & brooks/cole Advanced Books
# & Software. Pacific Grove. Califonia.
# Statistics/probability. Series
LineNumber<-c(rep("39","30"),rep("52","30"))
PlantingDate<-rep(c("16","20","21"),20)
HeadWt <- c(2.5,3.0,2.2,2.2,2.8,1.8,3.1,2.8,1.6,4.3,2.7,2.1,2.5,2.6,3.3,4.3,
2.8,3.8,3.8,2.6,3.2,4.3,2.6,3.6,1.7,2.6,4.2,3.1,3.5,1.6,2.0,4.0,1.5,2.4,2.8,
1.4,1.9,3.1,1.7,2.8,4.2,1.3,1.7,3.7,1.7,3.2,3.0,1.6,2.0,2.2,1.4,2.2,2.3,1.0,
2.2,3.8,1.5,2.2,2.0,1.6)
Ascorbic <-c(51,65,54,55,52,59,45,41,66,42,51,54,53,41,45,50,45,49,50,51,49,
52,45,55,56,61,49,49,42,68,58,52,78,55,70,75,67,57,70,61,58,84,67,47,71,68,
56,72,58,72,62,63,63,68,56,54,66,72,60,72)
trt<-paste(LineNumber,PlantingDate,sep="-")
output<-reg.homog(trt,HeadWt,Ascorbic)
REGW.test 113
REGW.test Ryan, Einot and Gabriel and Welsch multiple range test
Description
Multiple range tests for all pairwise comparisons, to obtain a confident inequalities multiple range
tests.
Usage
REGW.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL,console=FALSE)
Arguments
ymodel(aov or lm) or answer of the experimental unit
trt Constant( only y=model) or vector treatment applied to each experimental unit
DFerror Degree free
MSerror Mean Square Error
alpha Significant level
group TRUE or FALSE
main Title
console logical, print output
Details
It is necessary first makes a analysis of variance.
Value
statistics Statistics of the model
parameters Design parameters
regw Critical Range Table
means Statistical summary of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
Author(s)
Felipe de Mendiburu
References
Multiple comparisons theory and methods. Departament of statistics the Ohio State University.
USA, 1996. Jason C. Hsu. Chapman Hall/CRC
114 resampling.cv
See Also
BIB.test,DAU.test,duncan.test,durbin.test,friedman,HSD.test,kruskal,LSD.test,
Median.test,PBIB.test,scheffe.test,SNK.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
out<- REGW.test(model,"virus",
main="Yield of sweetpotato. Dealt with different virus")
print(out)
REGW.test(model,"virus",alpha=0.05,console=TRUE,group=FALSE)
resampling.cv Resampling to find the optimal number of markers
Description
This process finds the curve of CV for a different number of markers which allows us to determine
the number of optimal markers for a given relative variability. A method of the curvature.
Usage
resampling.cv(A, size, npoints)
Arguments
Adata frame or matrix of binary data
size number of re-samplings
npoints Number of points to consider the model
Value
lm(formula = CV ~ I(1/marker))
Table with variation coefficient by number of markers
Author(s)
Felipe de Mendiburu
References
Efron, B., Tibshirani, R. (1993) An Introduction to the Boostrap. Chapman and Hall/CRC
See Also
cv.similarity,similarity
resampling.model 115
Examples
library(agricolae)
#example table of molecular markers
data(markers)
study<-resampling.cv(markers,size=1,npoints=15)
#
# Results of the model
summary(study$model)
coef<-coef(study$model)
py<-predict(study$model)
Rsq<-summary(study$model)$"r.squared"
table.cv <- data.frame(study$table.cv,estimate=py)
print(table.cv)
# Plot CV
#startgraph
limy<-max(table.cv[,2])+10
plot(table.cv[,c(1,2)],col="red",frame=FALSE,xlab="number of markers",
ylim=c(10,limy), ylab="CV",cex.main=0.8,main="Estimation of the number of markers")
ty<-quantile(table.cv[,2],1)
tx<-median(table.cv[,1])
tz<-quantile(table.cv[,2],0.95)
text(tx,ty, cex=0.8,as.expression(substitute(CV == a + frac(b,markers),
list(a=round(coef[1],2),b=round(coef[2],2)))) )
text(tx,tz,cex=0.8,as.expression(substitute(R^2==r,list(r=round(Rsq,3)))))
# Plot CV = a + b/n.markers
fy<-function(x,a,b) a+b/x
x<-seq(2,max(table.cv[,1]),length=50)
y <- coef[1] + coef[2]/x
lines(x,y,col="blue")
#grid(col="brown")
rug(table.cv[,1])
#endgraph
resampling.model Resampling for linear models
Description
This process consists of finding the values of P-value by means of a re-sampling (permutation)
process along with the values obtained by variance analysis.
Usage
resampling.model(model,data,k,console=FALSE)
116 resampling.model
Arguments
model model in R
data data for the study of the model
knumber of re-samplings
console logical, print output
Value
Model solution with resampling.
Author(s)
Felipe de Mendiburu
References
Efron, B., Tibshirani, R. (1993) An Introduction to the Boostrap. Chapman and Hall/CRC Phillip I.
Good, (2001) Resampling Methods. Birkhauser. Boston . Basel . Berlin
See Also
simulation.model
Examples
#example 1 Simple linear regression
library(agricolae)
data(clay)
model<-"ralstonia ~ days"
analysis<-resampling.model(model,clay,k=2,console=TRUE)
#example 2 Analysis of variance: RCD
data(sweetpotato)
model<-"yield~virus"
analysis<-resampling.model(model,sweetpotato,k=2,console=TRUE)
#example 3 Simple linear regression
data(Glycoalkaloids)
model<-"HPLC ~ spectrophotometer"
analysis<-resampling.model(model,Glycoalkaloids,k=2,console=TRUE)
#example 4 Factorial in RCD
data(potato)
potato[,1]<-as.factor(potato[,1])
potato[,2]<-as.factor(potato[,2])
model<-"cutting~variety + date + variety:date"
analysis<-resampling.model(model,potato,k=2,console=TRUE)
rice 117
rice Data of Grain yield of rice variety IR8
Description
The data correspond to the yield of rice variety IR8 (g/m2) for land uniformity studies. The growing
area is 18x36 meters.
Usage
data(rice)
Format
A data frame with 36 observations on the following 18 variables.
V1 a numeric vector
V2 a numeric vector
V3 a numeric vector
V4 a numeric vector
V5 a numeric vector
V6 a numeric vector
V7 a numeric vector
V8 a numeric vector
V9 a numeric vector
V10 a numeric vector
V11 a numeric vector
V12 a numeric vector
V13 a numeric vector
V14 a numeric vector
V15 a numeric vector
V16 a numeric vector
V17 a numeric vector
V18 a numeric vector
Details
Table 12.1 Measuring Soil Heterogeneity
Source
Statistical Procedures for Agriculture Research. Second Edition. Kwanchai A. Gomez and Arturo
A. Gomez. 1976. USA Pag. 481.
118 RioChillon
References
Statistical Procedures for Agriculture Research. Second Edition. Kwanchai A. Gomez and Arturo
A. Gomez. 1976. USA
Examples
library(agricolae)
data(rice)
str(rice)
RioChillon Data and analysis Mother and baby trials
Description
Mother/Baby Trials allow farmers and researchers to test best-bet technologies or new cultivars.
Evaluation of advanced Clones of potato in the Valley of Rio Chillon - PERU (2004)
Usage
data(RioChillon)
Format
The format is list of 2:
1. mother: data.frame: 30 obs. of 3 variables:
- block (3 levels)
- clon (10 levels)
- yield (kg.)
2. babies: data.frame: 90 obs. of 3 variables:
- farmer (9 levels)
- clon (10 levels)
- yield (kg.)
Details
1. Replicated researcher-managed "mother trials" with typically 10 treatments evaluated in small
plots.
2. Unreplicated "baby trials" with 10 treatments evaluated in large plots.
3. The "baby trials" with a subset of the treatments in the mother trial.
Source
Experimental field.
References
International Potato Center. CIP - Lima Peru.
scheffe.test 119
Examples
# Analisys the Mother/Baby Trial Design
library(agricolae)
data(RioChillon)
# First analysis the Mother Trial Design.
model<-aov(yield ~ block + clon, RioChillon$mother)
anova(model)
cv.model(model)
comparison<-with(RioChillon$mother,LSD.test(yield,clon, 18, 4.922, group=TRUE))
# Second analysis the babies Trial.
comparison<-with(RioChillon$babies,friedman(farmer,clon, yield, group=TRUE))
# Third
# The researcher makes use of data from both mother and baby trials and thereby obtains
# information on suitability of new technologies or cultivars
# for different agro-ecologies and acceptability to farmers.
scheffe.test Multiple comparisons, scheffe
Description
Scheffe 1959, method is very general in that all possible contrasts can be tested for significance and
confidence intervals can be constructed for the corresponding linear. The test is conservative.
Usage
scheffe.test(y, trt, DFerror, MSerror, Fc, alpha = 0.05, group=TRUE, main = NULL,
console=FALSE )
Arguments
ymodel(aov or lm) or answer of the experimental unit
trt Constant( only y=model) or vector treatment applied to each experimental unit
DFerror Degrees of freedom
MSerror Mean Square Error
Fc F Value
alpha Significant level
group TRUE or FALSE
main Title
console logical, print output
Details
It is necessary first makes a analysis of variance.
120 similarity
Value
statistics Statistics of the model
parameters Design parameters
means Statistical summary of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
Author(s)
Felipe de Mendiburu
References
Robert O. Kuehl. 2nd ed. Design of experiments. Duxbury, copyright 2000. Steel, R.; Torri,J;
Dickey, D.(1997) Principles and Procedures of Statistics A Biometrical Approach. pp189
See Also
BIB.test,DAU.test,duncan.test,durbin.test,friedman,HSD.test,kruskal,LSD.test,
Median.test,PBIB.test,REGW.test,SNK.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus, data=sweetpotato)
comparison <- scheffe.test(model,"virus", group=TRUE,console=TRUE,
main="Yield of sweetpotato\nDealt with different virus")
# Old version scheffe.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
Fc<-anova(model)["virus",4]
out <- with(sweetpotato,scheffe.test(yield, virus, df, MSerror, Fc))
print(out)
similarity Matrix of similarity in binary data
Description
It finds the similarity matrix of binary tables (1 and 0).
Usage
similarity(A)
simulation.model 121
Arguments
AMatrix, data binary
Value
Distance matrix. Class = dist.
Author(s)
Felipe de Mendiburu
See Also
cv.similarity,resampling.cv
Examples
#example table of molecular markers
library(agricolae)
data(markers)
distance<-similarity(markers)
#startgraph
tree<-hclust(distance,method="mcquitty")
plot(tree,col="blue")
#endgraph
simulation.model Simulation of the linear model under normality
Description
This process consists of validating the variance analysis results using a simulation process of the
experiment. The validation consists of comparing the calculated values of each source of variation
of the simulated data with respect to the calculated values of the original data. If in more than
50 percent of the cases they are higher than the real one, then it is considered favorable and the
probability reported by the ANOVA is accepted, since the P-Value is the probability of (F > F.value).
Usage
simulation.model(model,file, categorical = NULL,k,console=FALSE)
Arguments
model Model in R
file Data for the study of the model
categorical position of the columns of the data that correspond to categorical variables
kNumber of simulations
console logical, print output
122 sinRepAmmi
Value
model ouput linear model, lm
simulation anova simulation
Author(s)
Felipe de Mendiburu
See Also
resampling.model
Examples
library(agricolae)
#example 1
data(clay)
model<-"ralstonia ~ days"
simulation.model(model,clay,k=15,console=TRUE)
#example 2
data(sweetpotato)
model<-"yield~virus"
simulation.model(model,sweetpotato,categorical=1,k=15,console=TRUE)
#example 3
data(Glycoalkaloids)
model<-"HPLC ~ spectrophotometer"
simulation.model(model,Glycoalkaloids,k=15,console=TRUE)
#example 4
data(potato)
model<-"cutting~date+variety"
simulation.model(model,potato,categorical=c(1,2,3),k=15,console=TRUE)
sinRepAmmi Data for AMMI without repetition
Description
Data frame for AMMI analysis with 50 genotypes in 5 environments.
Usage
data(sinRepAmmi)
skewness 123
Format
A data frame with 250 observations on the following 3 variables.
ENV a factor with levels A1 A2 A3 A4 A5
GEN a numeric vector
YLD a numeric vector
Source
Experimental data.
References
International Potato Center - Lima Peru.
Examples
library(agricolae)
data(sinRepAmmi)
str(sinRepAmmi)
skewness Finding the skewness coefficient
Description
It returns the skewness of a distribution. It is similar to SAS.
Usage
skewness(x)
Arguments
xa numeric vector
Value
The skewness of x.
See Also
kurtosis
Examples
library(agricolae)
x<-c(3,4,5,2,3,4,NA,5,6,4,7)
skewness(x)
# value is 0,3595431, is slightly asimetrica (positive) to the right
124 SNK.test
SNK.test Student-Newman-Keuls (SNK)
Description
SNK is derived from Tukey, but it is less conservative (finds more differences). Tukey controls the
error for all comparisons, where SNK only controls for comparisons under consideration. The level
by alpha default is 0.05.
Usage
SNK.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL,console=FALSE)
Arguments
ymodel(aov or lm) or answer of the experimental unit
trt Constant( only y=model) or vector treatment applied to each experimental unit
DFerror Degree free
MSerror Mean Square Error
alpha Significant level
group TRUE or FALSE
main Title
console logical, print output
Details
It is necessary first makes a analysis of variance.
Value
statistics Statistics of the model
parameters Design parameters
snk Critical Range Table
means Statistical summary of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
Author(s)
Felipe de Mendiburu
soil 125
References
1. Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third
Edition 1997
2. Multiple comparisons theory and methods. Departament of statistics the Ohio State University.
USA, 1996. Jason C. Hsu. Chapman Hall/CRC.
See Also
BIB.test,DAU.test,duncan.test,durbin.test,friedman,HSD.test,kruskal,LSD.test,
Median.test,PBIB.test,REGW.test,scheffe.test,waerden.test,waller.test,plot.group
Examples
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
out <- SNK.test(model,"virus", console=TRUE,
main="Yield of sweetpotato. Dealt with different virus")
print(SNK.test(model,"virus", group=FALSE))
# version old SNK.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
out <- with(sweetpotato,SNK.test(yield,virus,df,MSerror, group=TRUE))
print(out$groups)
soil Data of soil analysis for 13 localities
Description
We analyzed the physical and chemical properties of different soils, as full characterization of soil
and special analysis of micro-elements. These analyses were conducted in the laboratory analysis
of soils, plants, water and fertilizers in the La Molina National Agrarian University (UNALM).
To which the different soil samples were dried to the environment, screened (mesh 0.5xo, 5 mm)
and sterilized by steam 4 to 5 hours with a Lindinger Steam aerator SA150 and SA700, with the
possible aim of eliminating bacteria saprophytic or antagonists to prevent the growth of bacteria
(R.solanacearum).
Usage
data(soil)
Format
A data frame with 13 observations on the following 23 variables.
place a factor with levels Chmar Chz Cnt1 Cnt2 Cnt3 Hco1 Hco2 Hco3 Hyo1 Hyo2 Namora SR1 SR2
pH a numeric vector
126 soil
EC a numeric vector, electrical conductivity
CaCO3 a numeric vector
MO a numeric vector
CIC a numeric vector
Pa numeric vector
Ka numeric vector
sand a numeric vector
slime a numeric vector
clay a numeric vector
Ca a numeric vector
Mg a numeric vector
K2 a numeric vector
Na a numeric vector
Al_H a numeric vector
K_Mg a numeric vector
Ca_Mg a numeric vector
Ba numeric vector
Cu a numeric vector
Fe a numeric vector
Mn a numeric vector
Zn a numeric vector
Details
Cnt1= Canete, Cnt2=Valle Dulce(Canete), Cnt3=Valle Grande(Canete), Chz=Obraje-Carhuaz(Ancash),
Chmar=Chucmar-Chota(Huanuco, Hco1= Mayobamba-Chinchao(Huanuco), Hco2=Nueva Independencia-
Chinchao(Huanuco), Hco3=San Marcos-Umari(Huanuco), Hyo1=La Victoria-Huancayo(Junin), Hyo1=El
Tambo-Huancayo(Junin), Namora=Namora(Cajamarca), SR1= El Milagro-San Ramon(Junin), Sr2=La
Chinchana-San Ramon(Junin).
Source
Experimental field, 2004. Data Kindly provided by Dr. Sylvie Priou, Liliam Gutarra and Pedro
Aley.
References
International Potato Center - Lima, PERU.
Examples
library(agricolae)
data(soil)
str(soil)
sp.plot 127
sp.plot Splip-Plot analysis
Description
The variance analysis of a split plot design is divided into two parts: the plot-factor analysis and the
sub-plot factor analysis.
Usage
sp.plot(block, pplot, splot, Y)
Arguments
block replications
pplot main-plot Factor
splot sub-plot Factor
YVariable, response
Details
The split-plot design is specifically suited for a two-factor experiment on of the factors is assigned
to main plot (main-plot factor), the second factor, called the subplot factor, is assigned into subplots.
Value
ANOVA: Splip plot analysis
Author(s)
Felipe de Mendiburu
References
Statistical procedures for agricultural research. Kwanchai A. Gomez, Arturo A. Gomez. Second
Edition. 1984.
See Also
ssp.plot,strip.plot,design.split,design.strip
128 ssp.plot
Examples
library(agricolae)
data(plots)
model<-with(plots,sp.plot(block,A,B,yield))
# with aov
plots[,1]<-as.factor(plots[,1])
AOV <- aov(yield ~ block + A*B + Error(block/A),data=plots)
summary(AOV)
ssp.plot Split-split-Plot analysis
Description
The variance analysis of a split-split plot design is divided into three parts: the main-plot, subplot
and sub-subplot analysis.
Usage
ssp.plot(block, pplot, splot, ssplot, Y)
Arguments
block replications
pplot Factor main plot
splot Factor subplot
ssplot Factor sub-subplot
YVariable, response
Details
The split-split-plot design is an extension of the split-plot design to accommodate a third factor:
one factor in main-plot, other in subplot and the third factor in sub-subplot
Value
ANOVA: Splip Split plot analysis
Author(s)
Felipe de Mendiburu
References
Statistical procedures for agricultural research. Kwanchai A. Gomez, Arturo A. Gomez. Second
Edition. 1984.
stability.nonpar 129
See Also
sp.plot,strip.plot,design.split,design.strip
Examples
# Statistical procedures for agricultural research, pag 143
# Grain Yields of Three Rice Varieties Grown under
#Three Management practices and Five Nitrogen levels; in a
#split-split-plot design with nitrogen as main-plot,
#management practice as subplot, and variety as sub-subplot
#factores, with three replications.
library(agricolae)
f <- system.file("external/ssp.csv", package="agricolae")
ssp<-read.csv(f)
model<-with(ssp,ssp.plot(block,nitrogen,management,variety,yield))
gla<-model$gl.a; glb<-model$gl.b; glc<-model$gl.c
Ea<-model$Ea; Eb<-model$Eb; Ec<-model$Ec
par(mfrow=c(1,3),cex=0.6)
out1<-with(ssp,LSD.test(yield,nitrogen,gla,Ea,console=TRUE))
out2<-with(ssp,LSD.test(yield,management,glb,Eb,console=TRUE))
out3<-with(ssp,LSD.test(yield,variety,glc,Ec,console=TRUE))
plot(out1,xlab="Nitrogen",las=1,variation="IQR")
plot(out2,xlab="Management",variation="IQR")
plot(out3,xlab="Variety",variation="IQR")
# with aov
AOV<-aov(yield ~ block + nitrogen*management*variety + Error(block/nitrogen/management),data=ssp)
summary(AOV)
stability.nonpar Nonparametric stability analysis
Description
A method based on the statistical ranges of the study variable per environment for the stability
analysis.
Usage
stability.nonpar(data, variable = NULL, ranking = FALSE, console=FALSE)
Arguments
data First column the genotypes following environment
variable Name of variable
ranking logical, print ranking
console logical, print output
130 stability.par
Value
ranking data frame
statistics Statistical analysis chi square test
Author(s)
Felipe de Mendiburu
References
Haynes K G, Lambert D H, Christ B J, Weingartner D P, Douches D S, Backlund J E, Fry W and
Stevenson W. 1998. Phenotypic stability of resistance to late blight in potato clones evaluated at
eight sites in the United States American Journal Potato Research 75, pag 211-217.
See Also
stability.par
Examples
library(agricolae)
data(haynes)
stability.nonpar(haynes,"AUDPC",ranking=TRUE,console=TRUE)
# Example 2
data(CIC)
data1<-CIC$comas[,c(1,6,7,17,18)]
data2<-CIC$oxapampa[,c(1,6,7,19,20)]
cic <- rbind(data1,data2)
means <- by(cic[,5], cic[,c(2,1)], function(x) mean(x,na.rm=TRUE))
means <-as.data.frame(means[,])
cic.mean<-data.frame(genotype=row.names(means),means)
cic.mean<-delete.na(cic.mean,"greater")
out<-stability.nonpar(cic.mean)
out$ranking
out$statistics
stability.par Stability analysis. SHUKLA’S STABILITY VARIANCE AND KANG’S
Description
This procedure calculates the stability variations as well as the statistics of selection for the yield
and the stability. The averages of the genotype through the different environment repetitions are
required for the calculations. The mean square error must be calculated from the joint variance
analysis.
stability.par 131
Usage
stability.par(data,rep,MSerror,alpha=0.1,main=NULL,cova = FALSE,name.cov=NULL,
file.cov=0,console=FALSE)
Arguments
data matrix of averages, by rows the genotypes and columns the environment
rep Number of repetitions
MSerror Mean Square Error
alpha Label significant
main Title
cova Covariable
name.cov Name covariable
file.cov Data covariable
console logical, print output
Details
Stable (i) determines the contribution of each genotype to GE interaction by calculating var(i); (ii)
assigns ranks to genotypes from highest to lowest yield receiving the rank of 1; (iii) calculates pro-
tected LSD for mean yield comparisons; (iv) adjusts yield rank according to LSD (the adjusted rank
labeled Y); (v) determines significance of var(i) usign an aproximate F-test; (vi) assigns stability
rating (S) as follows: -8, -4 and -2 for var(i) significant at the 0.01, 0.05 and 0.10 probability levels,
and 0 for nonsignificant var(i) ( the higher the var(i), the less stable the genotype); (vii) sums ad-
justed yield rank, Y, and stability rating, S, for each genotype to determine YS(i) statistic; and (viii)
calculates mean YS(i) and identifies genotypes (selection) with YS(i) > mean YS(i).
Value
analysis Analysis of variance
statistics Statistics of the model
stability summary stability analysis
Author(s)
Felipe de Mendiburu
References
Kang, M. S. 1993. Simultaneous selection for yield and stability: Consequences for growers.
Agron. J. 85:754-757. Manjit S. Kang and Robert Mangari. 1995. Stable: A basic program
for calculating stability and yield-stability statistics. Agron. J. 87:276-277
See Also
stability.nonpar
132 stat.freq
Examples
library(agricolae)
# example 1
# Experimental data,
# replication rep= 4
# Mean square error, MSerror = 1.8
# 12 environment
# 17 genotype = 1,2,3,.., 17
# yield averages of 13 genotypes in localities
f <- system.file("external/dataStb.csv", package="agricolae")
dataStb<-read.csv(f)
stability.par(dataStb, rep=4, MSerror=1.8, alpha=0.1, main="Genotype",console=TRUE)
#example 2 covariable. precipitation
precipitation<- c(1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000,2100)
stability.par(dataStb, rep=4, MSerror=1.8, alpha=0.1, main="Genotype",
cova=TRUE, name.cov="Precipitation", file.cov=precipitation,console=TRUE)
stat.freq Descriptive measures of grouped data
Description
By this process the variance and central measures ar found: average, medium and mode of grouped
data.
Usage
stat.freq(histogram)
Arguments
histogram Object create by function hist()
Value
Statistics of grouped data.
Author(s)
Felipe de mendiburu
See Also
polygon.freq,table.freq,graph.freq,intervals.freq,sturges.freq,join.freq,ogive.freq,
normal.freq
strip.plot 133
Examples
library(agricolae)
data(growth)
grouped<-with(growth,hist(height,plot=FALSE))
measures<-stat.freq(grouped)
print(measures)
strip.plot Strip-Plot analysis
Description
The variance analysis of a strip-plot design is divided into three parts: the horizontal-factor analysis,
the vertical-factor analysis, and the interaction analysis.
Usage
strip.plot(BLOCK, COL, ROW, Y)
Arguments
BLOCK replications
COL Factor column
ROW Factor row
YVariable, response
Details
The strip-plot design is specifically suited for a two-factor experiment in which the desired precision
for measuring the interaction effects between the two factors is higher than that for measuring the
main efect two factors
Value
Data and analysis of the variance of the strip plot design.
Author(s)
Felipe de Mendiburu
References
Statistical procedures for agricultural research. Kwanchai A. Gomez, Arturo A. Gomez. Second
Edition. 1984.
134 sturges.freq
See Also
ssp.plot,sp.plot,design.split,design.strip
Examples
# Yield
library(agricolae)
data(huasahuasi)
YIELD<-huasahuasi$YIELD
market <- YIELD$y1da + YIELD$y2da
non_market <- YIELD$y3da
yield <- market + non_market
model<-with(YIELD,strip.plot(block, clon, trt, yield))
out1<-with(YIELD,LSD.test(yield,clon,model$gl.a,model$Ea))
par(mar=c(3,8,1,1),cex=0.8)
plot(out1,xlim=c(0,80),horiz=TRUE,las=1)
out2<-with(YIELD,LSD.test(yield,trt,model$gl.b,model$Eb))
plot(out2,xlim=c(0,80),horiz=TRUE,las=1)
sturges.freq Class intervals for a histogram, the rule of Sturges
Description
if k=0 then classes: k = 1 + log(n,2). if k > 0, fixed nclass.
Usage
sturges.freq(x,k=0)
Arguments
xvector
kconstant
Value
Statistics of sturges for a histogram.
Author(s)
Felipe de mendiburu
References
Reza A. Hoshmand. 1988. Statistical Methods for Agricultural Sciences, Timber Press, Incorpo-
rated, pag 18-21.
summary.graph.freq 135
See Also
polygon.freq,table.freq,stat.freq,intervals.freq,graph.freq,join.freq,ogive.freq,
normal.freq
Examples
library(agricolae)
data(natives)
classes<-with(natives,sturges.freq(size))
# information of the classes
breaks <- classes$breaks
breaks
#startgraph
# Histogram with the established classes
h<-with(natives,graph.freq(size,breaks,frequency=1, col="yellow",axes=FALSE,
xlim=c(0,0.12),main="",xlab="",ylab=""))
axis(1,breaks,las=2)
axis(2,seq(0,400,50),las=2)
title(main="Histogram of frequency\nSize of the tubercule of the Oca",
xlab="Size of the oca", ylab="Frequency")
#endgraph
summary.graph.freq frequency Table of a Histogram
Description
It finds the absolute, relative and accumulated frequencies with the class intervals defined from a
previously calculated histogram by the "hist" of R function.
Usage
## S3 method for class 'graph.freq'
summary(object,...)
Arguments
object Object by function graph.freq()
... other parameters of graphic
Value
Frequency table.
Lower Lower limit class
Upper Upper limit class
Main class point
136 sweetpotato
Frequency Frequency
Percentage Percentage frequency
CF Cumulative frequency
CPF Cumulative Percentage frequency
Author(s)
Felipe de Mendiburu
See Also
polygon.freq,stat.freq,graph.freq,intervals.freq,sturges.freq,join.freq,ogive.freq,
normal.freq
Examples
library(agricolae)
data(growth)
h2<-with(growth,graph.freq(height,plot=FALSE))
print(summary(h2),row.names=FALSE)
sweetpotato Data of sweetpotato yield
Description
The data correspond to an experiment with costanero sweetpotato made at the locality of the Tacna
department, southern Peru. The effect of two viruses (Spfmv and Spcsv) was studied. The treat-
ments were the following: CC (Spcsv) = Sweetpotato chlorotic dwarf, FF (Spfmv) = Feathery
mottle, FC (Spfmv y Spcsv) = Viral complex and OO (witness) healthy plants. In each plot, 50
sweetpotato plants were sown and 12 plots were employed. Each treatment was made with 3 rep-
etitions and at the end of the experiment the total weight in kilograms was evaluated. The virus
transmission was made in the cuttings and these were sown in the field.
Usage
data(sweetpotato)
Format
A data frame with 12 observations on the following 2 variables.
virus a factor with levels cc fc ff oo
yield a numeric vector
Source
Experimental field.
table.freq 137
References
International Potato Center. CIP - Lima Peru
Examples
library(agricolae)
data(sweetpotato)
str(sweetpotato)
table.freq frequency Table of a Histogram
Description
It finds the absolute, relative and accumulated frequencies with the class intervals defined from a
previously calculated histogram by the "hist" of R function.
Usage
table.freq(object)
Arguments
object Object by function graph.freq()
Value
Frequency table.
Lower Lower limit class
Upper Upper limit class
Main class point
Frequency Frequency
Percentage Percentage frequency
CF Cumulative frequency
CPF Cumulative Percentage frequency
Author(s)
Felipe de Mendiburu
See Also
polygon.freq,stat.freq,graph.freq,intervals.freq,sturges.freq,join.freq,ogive.freq,
normal.freq
138 tapply.stat
Examples
library(agricolae)
data(growth)
h2<-with(growth,graph.freq(height,plot=FALSE))
print(table.freq(h2),row.names=FALSE)
tapply.stat Statistics of data grouped by factors
Description
This process lies in finding statistics which consist of more than one variable, grouped or crossed
by factors. The table must be organized by columns between variables and factors.
Usage
tapply.stat(y, x, stat = "mean")
Arguments
ydata.frame variables
xdata.frame factors
stat Method
Value
Statistics of quantitative variables by categorical variables.
Author(s)
Felipe de Mendiburu
Examples
library(agricolae)
# case of 1 single factor
data(sweetpotato)
tapply.stat(sweetpotato[,2],sweetpotato[,1],mean)
with(sweetpotato,tapply.stat(yield,virus,sd))
with(sweetpotato,tapply.stat(yield,virus,function(x) max(x)-min(x)))
with(sweetpotato,tapply.stat(yield,virus,
function(x) quantile(x,0.75,6)-quantile(x,0.25,6)))
# other case
data(cotton)
with(cotton,tapply.stat(yield,cotton[,c(1,3,4)],mean))
with(cotton,tapply.stat(yield,cotton[,c(1,4)],max))
# Height of pijuayo
data(growth)
with(growth,tapply.stat(height, growth[,2:1], function(x) mean(x,na.rm=TRUE)))
vark 139
vark Variance K, ties, Kendall
Description
The Kendall method in order to find the K variance.
Usage
vark(x, y)
Arguments
xVector
yvector
Details
Script in C to R.
Value
variance of K for Kendall’s tau
Author(s)
Felipe de Mendiburu
References
Numerical Recipes in C. Second Edition.
See Also
cor.matrix, cor.vector, cor.mv
Examples
library(agricolae)
x <-c(1,1,1,4,2,2,3,1,3,2,1,1,2,3,2,1,1,2,1,2)
y <-c(1,1,2,3,4,4,2,1,2,3,1,1,3,4,2,1,1,3,1,2)
vark(x,y)
140 waerden.test
waerden.test Multiple comparisons. The van der Waerden (Normal Scores)
Description
A nonparametric test for several independent samples.
Usage
waerden.test(y, trt, alpha=0.05, group=TRUE, main=NULL,console=FALSE)
Arguments
yVariable response
trt Treatments
alpha Significant level
group TRUE or FALSE
main Title
console logical, print output
Details
The data consist of k samples of possibly unequal sample size.
The post hoc test is using the criterium Fisher’s least
significant difference (LSD).
Value
statistics Statistics of the model
parameters Design parameters
means Statistical summary of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
Author(s)
Felipe de Mendiburu
References
Practical Nonparametrics Statistics. W.J. Conover, 1999
See Also
BIB.test,DAU.test,duncan.test,durbin.test,friedman,HSD.test,kruskal,LSD.test,
Median.test,PBIB.test,REGW.test,scheffe.test,SNK.test,waller.test,plot.group
waller 141
Examples
library(agricolae)
# example 1
data(corn)
out1<-with(corn,waerden.test(observation,method,group=TRUE))
print(out1$groups)
plot(out1)
out2<-with(corn,waerden.test(observation,method,group=FALSE))
print(out2$comparison)
# example 2
data(sweetpotato)
out<-with(sweetpotato,waerden.test(yield,virus,alpha=0.01,group=TRUE))
print(out)
waller Computations of Bayesian t-values for multiple comparisons
Description
A Bayes rule for the symmetric multiple comparisons problem.
Usage
waller(K, q, f, Fc)
Arguments
KIs the loss ratio between type I and type II error
qNumerator Degrees of freedom
fDenominator Degrees of freedom
Fc F ratio from an analysis of variance
Details
K-RATIO (K): value specifies the Type 1/Type 2 error seriousness ratio for the Waller-Duncan test.
Reasonable values for KRATIO are 50, 100, and 500, which roughly correspond for the two-level
case to ALPHA levels of 0.1, 0.05, and 0.01. By default, the procedure uses the default value of
100.
Value
Waller value for the Waller and Duncan test.
Author(s)
Felipe de Mendiburu
142 waller.test
References
Waller, R. A. and Duncan, D. B. (1969). A Bayes Rule for the Symmetric Multiple Comparison
Problem, Journal of the American Statistical Association 64, pages 1484-1504.
Waller, R. A. and Kemp, K. E. (1976) Computations of Bayesian t-Values for Multiple Compar-
isons, Journal of Statistical Computation and Simulation, 75, pages 169-172.
Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third Edition
1997.
See Also
waller.test
Examples
# Table Duncan-Waller K=100, F=1.2 pag 649 Steel & Torry
library(agricolae)
K<-100
Fc<-1.2
q<-c(8,10,12,14,16,20,40,100)
f<-c(seq(4,20,2),24,30,40,60,120)
n<-length(q)
m<-length(f)
W.D <-rep(0,n*m)
dim(W.D)<-c(n,m)
for (i in 1:n) {
for (j in 1:m) {
W.D[i,j]<-waller(K, q[i], f[j], Fc)
}}
W.D<-round(W.D,2)
dimnames(W.D)<-list(q,f)
print(W.D)
waller.test Multiple comparisons, Waller-Duncan
Description
The Waller-Duncan k-ratio t test is performed on all main effect means in the MEANS statement.
See the K-RATIO option for information on controlling details of the test.
Usage
waller.test(y, trt, DFerror, MSerror, Fc, K = 100, group=TRUE, main = NULL,
console=FALSE)
waller.test 143
Arguments
ymodel(aov or lm) or answer of the experimental unit
trt Constant( only y=model) or vector treatment applied to each unit
DFerror Degrees of freedom
MSerror Mean Square Error
Fc F Value
KK-RATIO
group TRUE or FALSE
main Title
console logical, print output
Details
It is necessary first makes a analysis of variance.
K-RATIO (K): value specifies the Type 1/Type 2 error seriousness ratio for the Waller-Duncan test.
Reasonable values for KRATIO are 50, 100, and 500, which roughly correspond for the two-level
case to ALPHA levels of 0.1, 0.05, and 0.01. By default, the procedure uses the default value of
100.
Value
statistics Statistics of the model
parameters Design parameters
means Statistical summary of the study variable
comparison Comparison between treatments
groups Formation of treatment groups
Author(s)
Felipe de Mendiburu
References
Waller, R. A. and Duncan, D. B. (1969). A Bayes Rule for the Symmetric Multiple Comparison
Problem, Journal of the American Statistical Association 64, pages 1484-1504.
Waller, R. A. and Kemp, K. E. (1976) Computations of Bayesian t-Values for Multiple Compar-
isons, Journal of Statistical Computation and Simulation, 75, pages 169-172.
Steel & Torry & Dickey. Third Edition 1997 Principles and procedures of statistics a biometrical
approach
See Also
BIB.test,DAU.test,duncan.test,durbin.test,friedman,HSD.test,kruskal,LSD.test,
Median.test,PBIB.test,REGW.test,scheffe.test,SNK.test,waerden.test,plot.group
144 weatherSeverity
Examples
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus, data=sweetpotato)
out <- waller.test(model,"virus", group=TRUE)
#startgraph
par(mfrow=c(2,2))
# variation: SE is error standard
# variation: range is Max - Min
bar.err(out$means,variation="SD",horiz=TRUE,xlim=c(0,45),bar=FALSE,
col=colors()[25],space=2, main="Standard deviation",las=1)
bar.err(out$means,variation="SE",horiz=FALSE,ylim=c(0,45),bar=FALSE,
col=colors()[15],space=2,main="SE",las=1)
bar.err(out$means,variation="range",ylim=c(0,45),bar=FALSE,col="green",
space=3,main="Range = Max - Min",las=1)
bar.group(out$groups,horiz=FALSE,ylim=c(0,45),density=8,col="red",
main="Groups",las=1)
#endgraph
# Old version HSD.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
Fc<-anova(model)["virus",4]
out <- with(sweetpotato,waller.test(yield, virus, df, MSerror, Fc, group=TRUE))
print(out)
weatherSeverity Weather and Severity
Description
Weather and Severity
Usage
weatherSeverity(weather,severity,dates,EmergDate,EndEpidDate,NoReadingsH,
RHthreshold)
Arguments
weather object, see example
severity object, see example
dates vector dates
EmergDate date
EndEpidDate date
NoReadingsH num, 1
RHthreshold num, percentage
wilt 145
Details
Weather and severity
Value
Wfile "Date","Rainfall","Tmp","HumidHrs","humidtmp"
Sfile "Cultivar","ApplSys","dates","nday","MeanSeverity","StDevSeverity"
EmergDate date
EndEpidDate date
Note
All format data for date is yyyy-mm,dd, for example "2000-04-22". change with function as.Date()
See Also
lateblight
Examples
library(agricolae)
f <- system.file("external/weather.csv", package="agricolae")
weather <- read.csv(f,header=FALSE)
f <- system.file("external/severity.csv", package="agricolae")
severity <- read.csv(f)
weather[,1]<-as.Date(weather[,1],format = "%m/%d/%Y")
# Parameters dates and threshold
dates<-c("2000-03-25","2000-04-09","2000-04-12","2000-04-16","2000-04-22")
dates<-as.Date(dates)
EmergDate <- as.Date('2000/01/19')
EndEpidDate <- as.Date("2000-04-22")
dates<-as.Date(dates)
NoReadingsH<- 1
RHthreshold <- 90
#--------------------------
WS<-weatherSeverity(weather,severity,dates,EmergDate,EndEpidDate,
NoReadingsH,RHthreshold)
wilt Data of Bacterial Wilt (AUDPC) and soil
Description
Percentage of bacterial wilt and area under the curve of disease progression (AUDPC) relative
tomato plants transplanted in different soil types artificially infested with R.solanacearum 133 days
before.
146 wilt
Usage
data(wilt)
Format
A data frame with 13 observations on the following 15 variables.
place a factor with levels Chmar Chz Cnt1 Cnt2 Cnt3 Hco1 Hco2 Hco3 Hyo1 Hyo2 Namora SR1 SR2
Day7 a numeric vector
Day11 a numeric vector
Day15 a numeric vector
Day19 a numeric vector
Day23 a numeric vector
Day27 a numeric vector
Day31 a numeric vector
Day35 a numeric vector
Day39 a numeric vector
Day43 a numeric vector
Day47 a numeric vector
Day51 a numeric vector
AUDPC a numeric vector
relative a numeric vector
Details
Percentajes bacterial wilt. Day7 = evaluated to 7 days, Days11 = evaluated to 11 days. see data(soil)
and data(ralstonia)
Source
Experimental field, 2004. Data Kindly provided by Dr. Sylvie Priou, Liliam Gutarra and Pedro
Aley.
References
International Potato Center. CIP - Lima Peru.
Examples
library(agricolae)
data(wilt)
days<-c(7,11,15,19,23,27,31,35,39,43,47,51)
AUDPC<-audpc(wilt[,-1],days)
relative<-audpc(wilt[,-1],days,type="relative")
yacon 147
yacon Data Yacon
Description
The yacon (Smallanthus sonchifolius) is a plant native to the Andes, considered a traditional crop
in Peru and natural source of FOS, which is a type of carbohydrate that can not be digested by
the and the human body that have joined several beneficial properties in health, such as improve
the absorption of calcium, reducing the level of triglycerides and cholesterol and stimulate better
gastrointestinal function.
Usage
data(yacon)
Format
A data frame with 432 observations on the following 19 variables.
locality a factor with levels, Cajamarca, Lima, Oxapampa in PERU
site a numeric vector
dose a factor with levels F0 F150 F80
entry a factor with levels AKW5075 AMM5136 AMM5150 AMM5163 ARB5125 CLLUNC118 P1385
SAL136
replication a numeric vector, replications
height a numeric vector, plant height, centimeters
stalks a numeric vector, number of stalks
wfr a numeric vector, weight of fresh roots, grams
wff a numeric vector, weight of fresh foliage, grams
wfk a numeric vector, weight fresh kroner, grams
roots a numeric vector, matter of dried roots, grams
FOS a numeric vector, fructo-oligosaccharides, percentaje
glucose a numeric vector, percentaje
fructose a numeric vector, percentaje
sucrose a numeric vector, percentaje
brix a numeric vector, degrees Brix
foliage a numeric vector, matter dry foliage, grams
dry a numeric vector, dry matter kroner, grams
IH a numeric vector, Index harvest, 0 to 1
148 zigzag
Details
Proportion or fraction of the plant that is used (seeds, fruit, root) on dry basis. Part usable in a
proportion of total mass dissected. Plant of frijol, weight = 100g and frijol = 50g then, IH = 50/100
= 0.5 or 50 percentaje. Degrees Brix is a measurement of the mass ratio of dissolved sugar to water
in a liquid.
Source
CIP. Experimental field, 2003, Data Kindly provided by Ivan Manrique and Carolina Tasso.
References
International Potato Center. CIP - Lima Peru.
Examples
library(agricolae)
data(yacon)
str(yacon)
zigzag order plot in serpentine
Description
applied to designs: complete block, latin square, graeco, split plot, strip plot, lattice, alpha lattice,
Augmented block, cyclic, Balanced Incomplete Block and factorial.
Usage
zigzag(outdesign)
Arguments
outdesign output design
Value
fieldbook Remuneration of serpentine plots.
Author(s)
Felipe de Mendiburu
See Also
design.ab,design.alpha,design.bib,design.split ,design.cyclic ,design.dau ,design.graeco,
design.lattice,design.lsd,design.rcbd,design.strip
zigzag 149
Examples
library(agricolae)
trt<-letters[1:5]
r<-4
outdesign <- design.rcbd(trt,r,seed=9)
fieldbook <- zigzag(outdesign)
Index
∗Topic aplot
AMMI.contour,7
bar.err,11
bar.group,13
diffograph,51
graph.freq,59
normal.freq,93
ogive.freq,94
plot.AMMI,102
plot.graph.freq,104
plot.group,106
polygon.freq,109
∗Topic cluster
consensus,21
hcut,66
hgroups,68
∗Topic datasets
Chz2006,17
CIC,18
clay,19
ComasOxapampa,20
corn,22
cotton,26
DC,30
disease,52
frijol,57
genxenv,58
Glycoalkaloids,59
grass,61
greenhouse,62
growth,63
haynes,64
Hco2006,65
heterosis,67
huasahuasi,70
LxT,86
markers,87
melon,89
natives,91
pamCIP,97
paracsho,98
plots,107
plrv,108
potato,110
ralstonia,110
rice,117
RioChillon,118
sinRepAmmi,122
soil,125
sweetpotato,136
wilt,145
yacon,147
∗Topic design
design.ab,32
design.alpha,34
design.bib,35
design.crd,37
design.cyclic,38
design.dau,40
design.graeco,41
design.lattice,43
design.lsd,44
design.rcbd,45
design.split,47
design.strip,48
design.youden,49
index.smith,74
∗Topic distribution
summary.graph.freq,135
table.freq,137
waller,141
∗Topic htest
duncan.test,53
HSD.test,69
LSD.test,85
REGW.test,113
scheffe.test,119
SNK.test,124
150
INDEX 151
waller.test,142
∗Topic manip
audpc,8
audps,10
delete.na,31
lastC,80
montecarlo,90
order.group,95
orderPvalue,96
sturges.freq,134
zigzag,148
∗Topic models
AMMI,5
BIB.test,14
carolina,16
DAU.test,29
index.AMMI,72
lateblight,80
lineXtester,83
nonadditivity,92
PBIB.test,100
similarity,120
simulation.model,121
sp.plot,127
ssp.plot,128
stability.par,130
strip.plot,133
weatherSeverity,144
∗Topic multivariate
correl,23
correlation,24
cv.similarity,28
path.analysis,99
resampling.model,115
∗Topic nonparametric
durbin.test,54
friedman,56
kendall,77
kruskal,78
Median.test,88
stability.nonpar,129
vark,139
waerden.test,140
∗Topic optimize
resampling.cv,114
∗Topic package
agricolae-package,4
∗Topic regression
reg.homog,111
∗Topic univar
cv.model,27
index.bio,73
intervals.freq,75
join.freq,76
kurtosis,79
skewness,123
stat.freq,132
tapply.stat,138
agricolae (agricolae-package),4
agricolae-package,4
AMMI,5,8,73,84,103
AMMI.contour,7
audpc,8
audps,10
bar.err,11
bar.group,12,13
BIB.test,14,30,54,55,57,70,79,86,89,
101,106,114,120,125,140,143
carolina,16
Chz2006,17
CIC,18
clay,19
ComasOxapampa,20
consensus,21,66,69
corn,22
correl,23,25
correlation,24,24,77,100
cotton,26
cv.model,27
cv.similarity,28,114,121
DAU.test,15,29,54,55,57,70,79,86,89,
101,106,114,120,125,140,143
DC,16,30
delete.na,31
density,91
design.ab,32,35,36,38,39,41–43,45,46,
48–50,148
design.alpha,33,34,36,38,39,41–43,45,
46,48–50,148
design.bib,33,35,35,38,39,41–43,45,46,
48–50,148
design.crd,33,35,36,37,39,41–43,45,46,
48–50
152 INDEX
design.cyclic,33,35,36,38,38,41–43,45,
46,48–50,148
design.dau,33,35,36,38,39,40,42,43,45,
46,48–50,148
design.graeco,33,35,36,38,39,41,41,43,
45,46,48–50,148
design.lattice,33,35,36,38,39,41,42,
43,45,46,48–50,148
design.lsd,33,35,36,38,39,41–43,44,46,
48–50,148
design.rcbd,33,35,36,38,39,41–43,45,
45,48–50,148
design.split,33,35,36,38,39,41–43,45,
46,47,49,50,127,129,134,148
design.strip,33,35,36,38,39,41–43,45,
46,48,48,50,127,129,134,148
design.youden,49
diffograph,51
disease,52
duncan.test,15,30,52,53,55,57,70,79,
86,89,101,106,114,120,125,140,
143
durbin.test,13,15,30,54,54,57,70,79,
86,89,101,106,114,120,125,140,
143
friedman,13,15,30,52,54,55,56,70,79,
86,89,101,106,114,120,125,140,
143
frijol,57
genxenv,58
Glycoalkaloids,59
graph.freq,59,76,77,93,94,109,132,
135–137
grass,61
greenhouse,62
growth,63
haynes,64
hclust,22,66,69
Hco2006,65
hcut,22,66,69
heterosis,67
hgroups,22,66,68
HSD.test,12,13,15,27,30,52,54,55,57,
69,79,86,89,101,106,114,120,
125,140,143
huasahuasi,70
index.AMMI,72
index.bio,73
index.smith,74
intervals.freq,60,75,77,93,94,105,109,
132,135–137
join.freq,60,76,76,93,94,105,109,132,
135–137
kendall,77
kruskal,12,13,15,30,52,54,55,57,70,78,
86,89,101,106,114,120,125,140,
143
kurtosis,79,123
lastC,80
lateblight,80,145
lineXtester,6,83
LSD.test,12,13,15,27,30,52,54,55,57,
70,79,85,89,101,106,114,120,
125,140,143
LxT,86
markers,87
Median.test,15,30,54,55,57,70,79,86,
88,101,106,114,120,125,140,143
melon,89
montecarlo,90
natives,91
nonadditivity,92
normal.freq,60,76,77,93,94,105,109,
132,135–137
ogive.freq,60,76,77,93,94,105,132,
135–137
order.group,95
orderPvalue,96,96
pamCIP,97
paracsho,98
path.analysis,99
PBIB.test,15,30,54,55,57,70,79,86,89,
100,106,114,120,125,140,143
plot.AMMI,6,73,102
plot.graph.freq,104
plot.group,13,15,30,54,55,57,70,79,80,
86,89,101,106,114,120,125,140,
143
plots,107
INDEX 153
plrv,108
polygon.freq,60,76,77,93,94,105,109,
109,132,135–137
potato,110
ralstonia,110
reg.homog,111
REGW.test,15,30,52,54,55,57,70,79,86,
89,101,106,113,120,125,140,143
resampling.cv,28,114,121
resampling.model,115,122
rice,117
RioChillon,118
scheffe.test,15,30,52,54,55,57,70,79,
86,89,101,106,114,119,125,140,
143
similarity,28,114,120
simulation.model,116,121
sinRepAmmi,122
skewness,79,123
SNK.test,15,30,52,54,55,57,70,79,86,
89,101,106,114,120,124,140,143
soil,125
sp.plot,127,129,134
ssp.plot,127,128,134
stability.nonpar,129,131
stability.par,130,130
stat.freq,60,76,77,93,94,105,109,132,
135–137
strip.plot,127,129,133
sturges.freq,60,76,77,93,94,105,109,
132,134,136,137
summary.graph.freq,135
sweetpotato,136
table.freq,60,76,77,93,94,105,109,132,
135,137
tapply.stat,138
vark,139
waerden.test,15,30,52,54,55,57,70,79,
86,89,101,106,114,120,125,140,
143
waller,141
waller.test,12,13,15,27,30,54,55,57,
70,79,86,89,101,106,114,120,
125,140,142,142
weatherSeverity,82,144
wilt,145
yacon,147
zigzag,148

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