CRD 37 Agricolae

User Manual: CRD-37

Open the PDF directly: View PDF .
Page Count: 153

Download
Open PDF In Browser	View PDF

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 
Imports klaR, MASS, nlme, cluster, spdep, AlgDesign, graphics
Description Original idea was presented in the thesis ``A statistical analysis tool for agricultural research'' to obtain the degree of Master on science, National Engineering University (UNI), LimaPeru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, GraecoLatin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several 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

R topics documented:
agricolae-package
AMMI . . . . . .
AMMI.contour .
audpc . . . . . .
audps . . . . . .
bar.err . . . . . .

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.
1

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

.
.
.
.
.
.

. 4
. 5
. 7
. 8
. 10
. 11

R topics documented:

2
bar.group . . . .
BIB.test . . . . .
carolina . . . . .
Chz2006 . . . . .
CIC . . . . . . .
clay . . . . . . .
ComasOxapampa
consensus . . . .
corn . . . . . . .
correl . . . . . .
correlation . . . .
cotton . . . . . .
cv.model . . . . .
cv.similarity . . .
DAU.test . . . .
DC . . . . . . . .
delete.na . . . . .
design.ab . . . .
design.alpha . . .
design.bib . . . .
design.crd . . . .
design.cyclic . .
design.dau . . . .
design.graeco . .
design.lattice . .
design.lsd . . . .
design.rcbd . . .
design.split . . .
design.strip . . .
design.youden . .
diffograph . . . .
disease . . . . . .
duncan.test . . .
durbin.test . . . .
friedman . . . . .
frijol . . . . . . .
genxenv . . . . .
Glycoalkaloids .
graph.freq . . . .
grass . . . . . . .
greenhouse . . .
growth . . . . . .
haynes . . . . . .
Hco2006 . . . . .
hcut . . . . . . .
heterosis . . . . .
hgroups . . . . .
HSD.test . . . . .

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

13
14
16
17
18
19
20
21
22
23
24
26
27
28
29
30
31
32
34
35
37
38
40
41
43
44
45
47
48
49
51
52
53
54
56
57
58
59
59
61
62
63
64
65
66
67
68
69

R topics documented:
huasahuasi . . . .
index.AMMI . .
index.bio . . . .
index.smith . . .
intervals.freq . .
join.freq . . . . .
kendall . . . . . .
kruskal . . . . . .
kurtosis . . . . .
lastC . . . . . . .
lateblight . . . .
lineXtester . . . .
LSD.test . . . . .
LxT . . . . . . .
markers . . . . .
Median.test . . .
melon . . . . . .
montecarlo . . .
natives . . . . . .
nonadditivity . .
normal.freq . . .
ogive.freq . . . .
order.group . . .
orderPvalue . . .
pamCIP . . . . .
paracsho . . . . .
path.analysis . . .
PBIB.test . . . .
plot.AMMI . . .
plot.graph.freq . .
plot.group . . . .
plots . . . . . . .
plrv . . . . . . .
polygon.freq . . .
potato . . . . . .
ralstonia . . . . .
reg.homog . . . .
REGW.test . . .
resampling.cv . .
resampling.model
rice . . . . . . .
RioChillon . . . .
scheffe.test . . .
similarity . . . .
simulation.model
sinRepAmmi . .
skewness . . . .
SNK.test . . . . .

3
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

70
72
73
74
75
76
77
78
79
80
80
83
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
102
104
106
107
108
109
110
110
111
113
114
115
117
118
119
120
121
122
123
124

4

agricolae-package
soil . . . . . . . . .
sp.plot . . . . . . .
ssp.plot . . . . . .
stability.nonpar . .
stability.par . . . .
stat.freq . . . . . .
strip.plot . . . . . .
sturges.freq . . . .
summary.graph.freq
sweetpotato . . . .
table.freq . . . . .
tapply.stat . . . . .
vark . . . . . . . .
waerden.test . . . .
waller . . . . . . .
waller.test . . . . .
weatherSeverity . .
wilt . . . . . . . .
yacon . . . . . . .
zigzag . . . . . . .

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

Index

agricolae-package

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

125
127
128
129
130
132
133
134
135
136
137
138
139
140
141
142
144
145
147
148
150

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:
Type:
Version:
Date:
License:

agricolae
Package
1.2-8
2017-09-12
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, Bonferroni 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, SplitPlot, 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 
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 Departamento 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

Y

Response

MSE

Mean Square Error

console

ouput TRUE or FALSE

PC

Principal components ouput TRUE or FALSE

Details
additional graphics see help(plot.AMMI).

6

AMMI

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)

8

audpc

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 comparison 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
x
variation
horiz
bar
...

object means of the comparisons the LSD.test, HSD.test,...,etc
SE=standard error, range=Max-Min or IQR=interquartil range
Horizontal or vertical bars
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.
x

eje-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
x

Object 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.
x

eje-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 Design

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

y

Response

test

Comparison treatments

alpha

Significant test

group

logical

console

logical, print output

Details
Test of comparison treatment. lsd: Least significant difference. tukey: Honestly significant differente. duncan: Duncan’s new multiple range test waller: Waller-Duncan test. snk: Student-NewmanKeuls (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 Fahrenheit, relative humidity=86.2 AUDPC and relative see function audpc(). help(audpc) Exx: Evaluation 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
distance
method
nboot
duplicate
cex.text
col.text
...

data frame
method distance, see dist()
method cluster, see hclust()
The number of bootstrap samples desired.
control is TRUE other case is FALSE
size text on percentage consensus
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
dendrogram
duplicate

The groups and consensus percentage
The class object is hclust, dendrogram plot
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
x

Vector

y

Vector

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
x

table, matrix or vector

y

table, 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 Nacional Agraria - La Molina - Peru..

References
Book spanish: Metodos estadisticos para la investigacion. Autor: Calzada Benza Universidad Nacional 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
x

object 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
A

matrix 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

y

Response

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):191208.
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
x

matrix with NA

alternative

"less" or "greater"

Value
x

matrix

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

r

Replications 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", "KnuthTAOCP", "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 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

k

Number 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
x

a 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
y

model(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
P a numeric vector
K a 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
B a 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 IndependenciaChinchao(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

Y

Variable, 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

Y

Variable, 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 protected 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 adjusted 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

Y

Variable, 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
x

vector

k

constant

Value
Statistics of sturges for a histogram.
Author(s)
Felipe de mendiburu
References
Reza A. Hoshmand. 1988. Statistical Methods for Agricultural Sciences, Timber Press, Incorporated, 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 treatments 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 repetitions 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
y
x
stat

data.frame variables
data.frame factors
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
x

Vector

y

vector

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
y

Variable 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
K

Is the loss ratio between type I and type II error

q

Numerator Degrees of freedom

f

Denominator 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 Comparisons, 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
y

model(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

K

K-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 Comparisons, 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

yacon

147

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
SAL136

AMM5136 AMM5150 AMM5163 ARB5125

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

CLLUNC118 P1385

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
Examples
library(agricolae)
trt<-letters[1:5]
r<-4
outdesign <- design.rcbd(trt,r,seed=9)
fieldbook <- zigzag(outdesign)

149

Index
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

∗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
150

INDEX
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

151
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
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
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

weatherSeverity, 82, 144
wilt, 145

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

zigzag, 148

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

yacon, 147



File Type                       : PDF
File Type Extension             : pdf
MIME Type                       : application/pdf
PDF Version                     : 1.5
Linearized                      : No
Page Count                      : 153
Page Mode                       : UseOutlines
Author                          : 
Title                           : 
Subject                         : 
Creator                         : LaTeX with hyperref package
Producer                        : pdfTeX-1.40.15
Create Date                     : 2017:09:12 22:38:36+02:00
Modify Date                     : 2017:09:12 22:38:36+02:00
Trapped                         : False
PTEX Fullbanner                 : This is pdfTeX, Version 3.14159265-2.6-1.40.15 (TeX Live 2015/dev/Debian) kpathsea version 6.2.1dev