MF Manual

User Manual:

Open the PDF directly: View PDF .
Page Count: 30

Package ‘MF’

November 9, 2018

Type Package

Title Mitigated Fraction

Version 4.3.5

Date XX XXXX XXXX

Author David Siev

Maintainer Marie Vendettuoli <marie.c.vendettuoli@aphis.usda.gov>

Description Calculate MF (mitigated fraction) with clustering and bootstrap op-

tions. See http://goo.gl/pcXYVr for deﬁnition of MF. No endorsement, claim, or warranty is im-

plied for this package. It is made available for investigational or pedagogical use only

License MIT + ﬁle LICENSE

LazyLoad yes

LazyData yes

Depends R (>= 3.4.4)

Imports methods, dplyr (>= 0.7.1), plyr, stringr, tidyr, forcats,

purrr

Collate 'aaa.r' 'classes.r' 'generic_methods.r' 'MF-package.r'

'HLBoot.r' 'MFBoot.r' 'MFClus.r' 'MFClusBoot.r' 'MFClusHier.R'

'MFmp.r' 'MFnestBoot.r' 'MFr.r' 'MFSubj.r' 'MFHier-wrappers.r'

RoxygenNote 6.0.1

NeedsCompilation no

Rtopics documented:

MF-package......................................... 2

calﬂung ........................................... 3

HLBoot ........................................... 3

mf-class........................................... 5

MFBoot........................................... 6

mfboot-class......................................... 7

mfbootcluster-class ..................................... 8

MFClus ........................................... 9

MFClusBoot ........................................ 10

MFClusBootHier ...................................... 12

MFClusHier......................................... 13

mfcluster-class ....................................... 15

2MF-package

mfcomponents-class .................................... 15

MFh............................................. 16

MFhBoot .......................................... 17

mfhierdata-class....................................... 19

mfhlboot-class........................................ 19

MFmp............................................ 20

mfmp-class ......................................... 21

MFnest ........................................... 22

MFnestBoot......................................... 24

MFr ............................................. 26

MFSubj ........................................... 27

mlesions........................................... 28

piglung ........................................... 29

Index 30

MF-package MF Package

Description

Includes functions related to mitigated fraction.

For internal use only at the USDA Center for Veterinary Biologics.

Details

Package: MF-package

Type: Package

Version: 4.3.5

Date: XXXX-XX-XX

License: MIT

LazyLoad: yes

Author(s)

David Siev <David.Siev@aphis.usda.gov>

Examples

#---------------------------------------------

# Checking MF package

#---------------------------------------------

example(MFr)

#---------------------------------------------

# End examples

#---------------------------------------------

invisible()

calﬂung 3

calflung calﬂung dataset

Description

Post-mortem examination of the lungs of groups of calves.

Format

a data frame with 50 observations of the following 2 variables, no NAs

group Treatment group. One of con = control or vac = vaccinate

lesion Fraction of lungs with gross lesions.

HLBoot Bootstrap CI for MF, HL, and Qdif

Description

Estimates bootstrap conﬁdence intervals for MF, HL, and Qdif.

Usage

HLBoot(formula, data, compare = c("con", "vac"), b = 100, B = 100,

alpha = 0.05, hpd = TRUE, bca = FALSE, return.boot = FALSE,

trace.it = FALSE, seed = sample(1:1e+05, 1))

Arguments

formula Formula of the form y ~ x + cluster(w), where y is a continuous response, x

is a factor with two levels of treatment, and w is a factor indicating the clusters.

data Data frame

compare Text vector stating the factor levels - compare[1] is the control or reference

group to which compare[2] is compared

bNumber of bootstrap samples to take with each cycle

BNumber of cycles, giving the total number of samples = B * b

alpha Complement of the conﬁdence level

hpd Boolean whether to estimate highest density intervals for MF and HL.

bca Boolean whether to estimate BCa intervals for MF.

return.boot Boolean whether to save the bootstrap samples of the statistics.

trace.it Boolean whether to display verbose tracking of the cycles.

seed to initialize random number generator for reproducibility. Passed to set.seed.

4HLBoot

Details

Estimates bootstrap conﬁdence intervals for the mitigated fraction (MF), Hodge-Lehmann estimator

(HL), and the difference of medians and quartiles (Qdif). Equal tailed intervals are provided for

all three, highest density intervals are optionally provided for MF and HL, and BCa intervals are

optionally provided for MF. The Hodges-Lehmann estimator is the median difference; it assumes

that the two distributions have the same shape and differ by a constant shift. Assumes data is single

pool (no nesting).

Value

amfhlboot-class data object

Author(s)

David Siev <david.siev@aphis.usda.gov>

References

Hodges JL, Lehmann EL, (1963). Estimates of location based on rank tests. Annals of Mathemati-

cal Statistics. 34:598–611.

Siev D, (2005). An estimator of intervention effect on disease severity. Journal of Modern Ap-

plied Statistical Methods. 4:500–508.

Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Chapman and Hall, New York, 1993.

See Also

mfhlboot-class

Examples

HLBoot(lesion~group, calflung, seed = 12345)

# Bootstrapping

#........................................

#....................

# 10000 bootstrap samples

# 95% confidence intervals

# Comparing vac to con

# Mitigated Fraction

# observed median lower upper

# Equal Tailed 0.44 0.4496 0.152 0.7088

# Highest Density 0.44 0.4496 0.152 0.7088

# Hodges-Lehmann

# observed median lower upper

# Equal Tailed -0.07335 -0.07615 -0.17220 -0.01565000

mf-class 5

# Highest Density -0.07335 -0.07615 -0.15635 -0.00850065

# Quartile Differences (quartiles of vac - quartiles of con)

# observed median lower upper

# Q25 -0.041500 -0.041500 -0.10340 -0.000905

# Q50 -0.112525 -0.111175 -0.28115 0.019350

# Q75 -0.168000 -0.170425 -0.38890 0.005300

# Quartiles of con

# observed median lower upper

# Q25 0.054000 0.054000 0.021005 0.11275

# Q50 0.139275 0.139275 0.061400 0.31000

# Q75 0.315000 0.315000 0.173000 0.44625

# Quartiles of vac

# observed median lower upper

# Q25 0.01250 0.01250 0.00125 0.026000

# Q50 0.02675 0.02675 0.01665 0.144575

# Q75 0.14700 0.14700 0.02810 0.219250

mf-class Class mf

Description

Parent class for package MF data objects.

Usage

mf$new(nboot, alpha, seed, compare, rng)

Fields

•nboot: numeric value specifying number of samples

•alpha: numeric value specifying complement of conﬁdence interval

•seed: vector of integers specifying seed for pseudo-random number generator used

•compare: vector of character strings naming groups compared

•rng: character string naming type of random number generator

Author(s)

Marie Vendettuoli <marie.c.vendettuoli@aphis.usda.gov>

See Also

Other mf: mfboot-class,mfbootcluster-class,mfhlboot-class

6MFBoot

MFBoot Bootstrap MF CI

Description

Estimates bootstrap conﬁdence intervals for the mitigated fraction.

Usage

MFBoot(formula, data, compare = c("con", "vac"), b = 100, B = 100,

alpha = 0.05, hpd = TRUE, bca = FALSE, return.boot = FALSE, trace.it = FALSE,

seed = sample(1:100000, 1))

Arguments

formula Formula of the form y~x, where y is a continuous response and x is a factor

with two levels.

data Data frame

compare Text vector stating the factor levels - compare[1] is the control or reference

group to which compare[2] is compared

bNumber of bootstrap samples to take with each cycle

BNumber of cycles, giving the total number of samples = B * b

alpha Complement of the conﬁdence level

hpd Estimate highest density intervals?

bca Estimate BCa intervals?

return.boot Save the bootstrap sample of the MF statistic?

trace.it Verbose tracking of the cycles?

seed to initialize random number generator for reproducibility. Passed to set.seed.

Details

Resamples the data and produces bootstrap conﬁdence intervals. Equal tailed intervals are estimated

by the percentile method. Highest density intervals are estimated by selecting the shortest of all

possible intervals. For BCa intervals, see Efron and Tibshirani section 14.3.

Value

amfboot-class data object

Author(s)

David Siev <david.siev@aphis.usda.gov>

References

Siev D. (2005). An estimator of intervention effect on disease severity. Journal of Modern Applied

Statistical Methods. 4:500–508

Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Chapman and Hall, New York, 1993.

mfboot-class 7

See Also

mfboot-class

Examples

MFBoot(lesion~group, calflung, seed = 12345)

# 10000 bootstrap samples

# 95% confidence interval

# Seed = 12345

# Comparing vac to con

# observed median lower upper

# Equal Tailed 0.44 0.4496 0.152 0.7088

# Highest Density 0.44 0.4496 0.152 0.7088

mfboot-class Class mfboot

Description

class for data objects produced by MFBoot, contains class mf with the two additional ﬁelds stat and

stuff.

Usage

mfboot$new(nboot, alpha, seed, compare, rng, sample, stat, stuff)

Fields

•nboot: numeric value specifying number of samples

•alpha: numeric value specifying complement of conﬁdence interval

•seed: vector of integers specifying seed for pseudo-random number generator used

•compare: vector of character strings naming groups compared

•rng: character string naming type of random number generator

•sample: what is this?

•stat: matrix of estimates

Contains

mf-class

Author(s)

Marie Vendettuoli <marie.c.vendettuoli@aphis.usda.gov>

See Also

MFBoot

Other mf: mf-class,mfbootcluster-class,mfhlboot-class

8mfbootcluster-class

mfbootcluster-class Class mfbootcluster

Description

Class mfbootcluster is created from output of function MFClusBoot

Usage

mfbootcluster$new(nboot, alpha, seed, compare, rng, stat, what, excludedClusters,

call, sample)

Fields

•nboot: numeric value specifying number of samples

•alpha: numeric value specifying complement of conﬁdence interval

•seed: vector of integers specifying seed for pseudo-random number generator used

•compare: vector of character strings naming groups compared

•rng: character string naming type of random number generator

•stat: matrix matrix with columns observed, median, lower, upper for estimates

•what: character vector naming what was resampled: clusters,units,both

•excludedClusters: character vector naming clusters excluded because of missing treat-

ment(s)

•call: the call to MFClusBoot

•sample: what is this?

•All: Field "All" from MFClus call.

Contains

mf-class

Author(s)

Marie Vendettuoli <marie.c.vendettuoli@aphis.usda.gov>

See Also

MFClusBoot

Other mf: mf-class,mfboot-class,mfhlboot-class

MFClus 9

MFClus Clustered mitigated fraction

Description

Estimates mitigated fraction from clustered or stratiﬁed data.

Usage

MFClus(formula, data, compare = c("con", "vac"), trace.it = FALSE)

Arguments

formula Formula of the form y ~ x + cluster(w), where y is a continuous response, x

is a factor with two levels of treatment, and w is a factor indicating the clusters.

data Data frame. See Note for handling of input data with more than two levels.

compare Text vector stating the factor levels - compare[1] is the control or reference

group to which compare[2] is compared

trace.it Verbose tracking of the cycles? Default FALSE.

Details

Averages the U statistic over the clusters and computes MF from it. Clusters are excluded if they

do not include both treatments.

Value

amfcluster-class data object

Note

If input data contains more than two levels of treatment, rows associated with unused treatment

levels will be removed.

Factor levels for treatments not present in the input data will be ignored.

Clusters with missing treatments will be excluded. See mfbootcluster-class or use trace.it to

identify excluded clusters.

Author(s)

David Siev <david.siev@aphis.usda.gov>

References

Siev D. (2005). An estimator of intervention effect on disease severity. Journal of Modern Applied

Statistical Methods. 4:500–508

See Also

mfcluster-class

10 MFClusBoot

Examples

## Not run:

MFClus(lesion ~ group + cluster(litter), piglung)

# Comparing vac to con

# MF = 0.3533835

# By Cluster

# w u r n1 n2 mf

# U 25 10 0.4000000 5 5 -0.2000000

# K 12 2 0.2500000 4 2 -0.5000000

# Z 16 10 0.8333333 3 4 0.6666667

# D 3 2 1.0000000 1 2 1.0000000

# N 1 0 0.0000000 1 3 -1.0000000

# T 8 5 0.8333333 2 3 0.6666667

# P 4 1 0.5000000 2 1 0.0000000

# L 3 2 0.6666667 1 3 0.3333333

# G 15 9 0.7500000 3 4 0.5000000

# J 15 9 1.0000000 3 3 1.0000000

# W 6 3 0.7500000 2 2 0.5000000

# A 9 3 0.3333333 3 3 -0.3333333

# X 12 6 1.0000000 3 2 1.0000000

# F 13 7 0.7777778 3 3 0.5555556

# S 21 11 0.9166667 4 3 0.8333333

# H 14 8 0.8888889 3 3 0.7777778

# Y 2 1 1.0000000 1 1 1.0000000

# E 2 1 1.0000000 1 1 1.0000000

# All

# w u r n1 n2 mf

# All 181 90 0.6766917 50 52 0.3533835

# Excluded Clusters

# [1] M, Q, R, B, O, V, I, C

## End(Not run)

MFClusBoot Bootstrap MF CI from clustered data

Description

Estimates bootstrap conﬁdence intervals for the mitigated fraction from clustered or stratiﬁed data.

Usage

MFClusBoot(formula, data, compare = c("con", "vac"), boot.cluster = TRUE,

boot.unit = TRUE, b = 100, B = 100, alpha = 0.05, hpd = TRUE,

return.boot = FALSE, trace.it = FALSE, seed = sample(1:1e+05, 1))

MFClusBoot 11

Arguments

formula Formula of the form y ~ x + cluster(w), where y is a continuous response, x

is a factor with two levels of treatment, and w is a factor indicating the clusters.

data Data frame. See Note for handling of input data with more than two levels.

compare Text vector stating the factor levels - compare[1] is the control or reference

group to which compare[2] is compared

boot.cluster Boolean whether to resample the clusters.

boot.unit Boolean whether to resample the units within cluster.

bNumber of bootstrap samples to take with each cycle

BNumber of cycles, giving the total number of samples = B * b

alpha Complement of the conﬁdence level

hpd Boolean whether to estimate highest density intervals.

return.boot Boolean whether to save the bootstrap sample of the MF statistic.

trace.it Boolean whether to display verbose tracking of the cycles.

seed to initialize random number generator for reproducibility. Passed to set.seed.

Details

Resamples the data and produces bootstrap conﬁdence intervals. Equal tailed intervals are estimated

by the percentile method. Highest density intervals are estimated by selecting the shortest of all

possible intervals.

Value

amfbootcluster-class data object

Note

If input data contains more than two levels of treatment, rows associated with unused treatment

levels will be removed.

Factor levels for treatments not present in the input data will be ignored.

Clusters with missing treatments will be excluded. See mfbootcluster-class or use trace.it to

identify excluded clusters.

Author(s)

David Siev <david.siev@aphis.usda.gov>

References

Siev D. (2005). An estimator of intervention effect on disease severity. Journal of Modern Applied

Statistical Methods. 4:500–508

Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Chapman and Hall, New York, 1993.

12 MFClusBootHier

Examples

## Not run:

MFClusBoot(lesion ~ group + cluster(litter), piglung, seed = 12345)

Bootstrappingclusters....................................................................................................

Bootstrapping units. . . . . . . . . . . . . . . . . .

10000 bootstrap samples of clusters and units in treatment in cluster

Comparing vac to con

95% confidence interval

observed median lower upper

Equal Tailed 0.3533835 0.3648649 -0.01409471 0.7109966

Highest Density 0.3533835 0.3648649 0.00000000 0.7236842

Excluded Clusters

M, Q, R, B, O, V, I, C

## End(Not run)

MFClusBootHier MFClusBootHier

Description

Combines MFhBoot and MFnestBoot into a single function.

Usage

MFClusBootHier(formula, data, compare = c("con", "vac"), nboot = 10000,

boot.unit = TRUE, boot.cluster = TRUE, which.factor = "All",

alpha = 0.05)

Arguments

formula formula Formula of the form y ~ x + a/b/c, where y is a continuous response,

x is a factor with two levels of treatment, and a/b/c are variables corresponding

to the clusters. It is expected that levels of "c" are nested within levels of "b".

Nesting is assumed to be in order, left to right, highest to lowest.

data a data.frame or tibble with the variables speciﬁed in formula. Additional vari-

ables will be ignored.

compare Text vector stating the factor levels - compare[1] is the control or reference group

to which compare[2] is compared.

nboot number of bootstrapping events

boot.unit Boolean whether to sample observations from within those of the same core.

boot.cluster Boolean whether to sample which cores are present. If TRUE, some trees have

all the cores while others only have a subset.

which.factor Which variables to include in the mitigated fraction summation. Default is

âC™AllâC™, to sum over entire tree.

alpha Passed to emp.hpd to calculate high tailed upper and high tailed lower of miti-

gated fraction.

MFClusHier 13

Value

A list with the following elements:

•MFhBoot as output from MFhBoot.

•MFnestBoot as output from MFnestBoot.

Note

Core variable is the variable corresponding to the lowest nodes of the hierarchical tree. Nest vari-

ables are those above the core. All refers to a summary of the entire tree.

See Also

MFhBoot,MFnestBoot.

Examples

a <- data.frame(

room = paste('Room',rep(c('W','Z'),each=24)),

pen = paste('Pen',rep(LETTERS[1:6],each=8)),

litter = paste('Litter',rep(11:22,each=4)),

tx = rep(rep(c('vac','con'),each=2),12),

stringsAsFactors = FALSE

)

set.seed(76153)

a$lung[a$tx=='vac'] <- rnorm(24,5,1.3)

a$lung[a$tx=='con'] <- rnorm(24,7,1.3)

set.seed(12345)

thismf1 <- MFClusBootHier(lung ~ tx + room/pen/litter, a, nboot = 10000,

boot.cluster = TRUE, boot.unit = TRUE)

thismfhboot <- thismf1$MFhBoot

thismfhboot$bootmfh

thismf1$MFnestBoot

MFClusHier MFClusHier

Description

Combines MFh and MFnest into a single function.

Usage

MFClusHier(formula, data, compare = c("con", "vac"), which.factor = "All")

14 MFClusHier

Arguments

formula Formula of the form y ~ x + a/b/c, where y is a continuous response, x is a

factor with two levels of treatment, and a/b/c are variables corresponding to the

clusters. It is expected that levels of "c" are nested within levels of "b". Nesting

is assumed to be in order, left to right, highest to lowest.

data a data.frame or tibble with the variables speciﬁed in formula. Additional vari-

ables will be ignored.

compare Text vector stating the factor levels - compare[1] is the control or reference group

to which compare[2] is compared.

which.factor one or more variable(s) of interest. This can be any of the core or nest variables

from the data set. If none or NULL is speciﬁed, MF will be calculated for the

whole tree.

Value

A list with the following elements:

•MFh as output from MFh.

•MFnest as output from MFnest.

Note

Core variable is the variable corresponding to the lowest nodes of the hierarchical tree. Nest vari-

ables are those above the core. All refers to a summary of the entire tree.

See Also

MFh,MFnest

Examples

a <- data.frame(

room = paste('Room',rep(c('W','Z'),each=24)),

pen = paste('Pen',rep(LETTERS[1:6],each=8)),

litter = paste('Litter',rep(11:22,each=4)),

tx = rep(rep(c('vac','con'),each=2),12),

stringsAsFactors = FALSE

)

set.seed(76153)

a$lung[a$tx=='vac'] <- rnorm(24,5,1.3)

a$lung[a$tx=='con'] <- rnorm(24,7,1.3)

thismf <- MFClusHier(lung ~ tx + room/pen/litter,a)

thismf$MFnest

aCore <- thismf$MFh

aCore

aCore$data

aCore$formula

aCore$compare

mfcluster-class 15

mfcluster-class Class mfcluster

Description

Class mfcluster is created from output of function MFClus

Usage

mfcluster$new(All, bycluster, excludedClusters, call, compare)

Fields

•All: vector with elements:

–wWilcoxon statistic

–uMann-Whitney statistic

–rmean ridit

–n1 size of group 1

–n2 size of group 2

–mf mitigated fraction

•byCluster: As for All, by clusters

•excludedClusters: character vector naming clusters excluded because of missing treatment

•call: the call to MFClus

•compare: character vector naming groups compared

Author(s)

Marie Vendettuoli <marie.c.vendettuoli@aphis.usda.gov>

See Also

MFClus

mfcomponents-class Class mfcomponents

Description

Class mfcomponents is created from output of function MFSubj

Usage

mfcomponents$new(mf, x, y, subj, compare)

16 MFh

Fields

•mf: numeric estimator for mitigated fraction

•x: numeric vector containing responses of group 1

•y: numeric vector containing responses of group 2

•subj: matrix where mf.j are the subject components

•compare: character vector naming groups being compared

Author(s)

Marie Vendettuoli <marie.c.vendettuoli@aphis.usda.gov>

See Also

MFSubj

MFh Identify ranks for use when evaluating MF for nested hierarchy.

Usage

MFh(formula, data, compare = c("con", "vac"))

Arguments

formula Formula of the form y ~ x + a/b/c, where y is a continuous response, x is a

factor with two levels of treatment, and a/b/c are variables corresponding to the

clusters. It is expected that levels of "c" are nested within levels of "b". Nesting

is assumed to be in order, left to right, highest to lowest.

data a data.frame or tibble with the variables speciﬁed in formula. Additional vari-

ables will be ignored.

compare Text vector stating the factor levels - compare[1] is the control or reference group

to which compare[2] is compared.

Value

Amfhierdata object, which is a list of three items.

coreTbl Atibble with one row for each unique core level showing values for:

•con_n &vac_n - counts of observations for each treatment level in the core level.

•con_medResp &vac_medResp - median of the y continuous response for each treatment

level.

•n1n2 - product of the counts, con_n *vac_n.

•w- Wilcoxon statistic

•u- Mann-Whitney statistic

data Atibble of the restructured input data used for calculations.

compare The compare variables as input by user.

formula The formula as input by user.

MFhBoot 17

Note

Core variable is the variable corresponding to the lowest nodes of the hierarchial tree. Nest variables

are those above the core.

See Also

MFnest for calculation of MF for nest, core and all variables. mfhierdata for returned object.MFClusHier

for a wrapper.

Examples

a <- data.frame(

room = paste('Room',rep(c('W','Z'),each=24)),

pen = paste('Pen',rep(LETTERS[1:6],each=8)),

litter = paste('Litter',rep(11:22,each=4)),

tx = rep(rep(c('vac','con'),each=2),12),

stringsAsFactors = FALSE

)

set.seed(76153)

a$lung[a$tx=='vac'] <- rnorm(24,5,1.3)

a$lung[a$tx=='con'] <- rnorm(24,7,1.3)

aCore <- MFh(lung ~ tx + room/pen/litter,a)

aCore

# A tibble: 12 x 10

# room pen litter con_medResp con_n w vac_medResp vac_n n1n2 u

# <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

# 1 Room W Pen A Litter 11 8.24 2 7 5.13 2 4 4

# 2 Room W Pen A Litter 12 4.91 2 5 3.81 2 4 2

# 3 Room W Pen B Litter 13 8.10 2 7 5.23 2 4 4

# 4 Room W Pen B Litter 14 8.11 2 7 5.59 2 4 4

# 5 Room W Pen C Litter 15 8.09 2 7 5.26 2 4 4

# 6 Room W Pen C Litter 16 6.77 2 7 4.50 2 4 4

# 7 Room Z Pen D Litter 17 5.58 2 7 4.26 2 4 4

# 8 Room Z Pen D Litter 18 7.44 2 6 6.33 2 4 3

# 9 Room Z Pen E Litter 19 7.98 2 7 4.58 2 4 4

# 10 Room Z Pen E Litter 20 6.78 2 7 4.86 2 4 4

# 11 Room Z Pen F Litter 21 6.82 2 7 5.36 2 4 4

# 12 Room Z Pen F Litter 22 7.27 2 7 5.13 2 4 4

MFhBoot MFhBoot

Description

Calculate rank tables for MF using bootstrapping.

Usage

MFhBoot(formula, data, compare = c("con", "vac"), nboot = 10000,

boot.unit = TRUE, boot.cluster = TRUE, seed = sample(1:1e+05, 1))

18 MFhBoot

Arguments

formula Formula of the form y ~ x + a/b/c, where y is a continuous response, x is a

factor with two levels of treatment, and a/b/c are variables corresponding to the

clusters. It is expected that levels of "c" are nested within levels of "b". Nesting

is assumed to be in order, left to right, highest to lowest.

data a data.frame or tibble with the variables speciﬁed in formula. Additional vari-

ables will be ignored.

compare Text vector stating the factor levels - compare[1] is the control or reference group

to which compare[2] is compared.

nboot number of bootstrapping events

boot.unit Boolean whether to sample observations from within those of the same core.

boot.cluster Boolean whether to sample which cores are present. If TRUE, some trees have

all the cores while others only have a subset.

seed to initialize random number generator for reproducibility. Passed to set.seed.

Value

A list with the following elements:

bootmfh Rank table for the bootstrapped values as output from MFh. Includes a new bootID vari-

able to distinguish each bootstrapped incidence.

clusters Table of unique nodes with an ID.

compare Compare vector as speciﬁed by user.

mfh MFh run on original data input.

See Also

MFClusBootHier,MFnestBoot

Examples

set.seed(76153)

a <- data_frame(room = paste('Room', rep(c('W','Z'), each = 24)),

pen = paste('Pen', rep(LETTERS[1:6], each = 8)),

litter = paste('Litter', rep(11:22, each = 4)),

tx = rep(rep(c('vac','con'), each = 2), 12)) %>%

mutate(lung = ifelse(tx == 'vac', rnorm(24, 5, 1.3), rnorm(24, 7, 1.3)))

formula <- lung ~ tx + room/pen/litter

nboot <- 10000

boot.cluster <- TRUE

boot.unit <- TRUE

which.factors <- c('All','room','pen','litter')

system.time(test1 <- MFhBoot(formula, a,

nboot = 10000,

boot.cluster = TRUE, boot.unit = TRUE, seed = 12345))

test1$bootmfh

mfhierdata-class 19

mfhierdata-class Class mfhierdata

Description

Class mfhierdata is created from output of function MFh

Usage

mfhierdata$new(coreTbl, data)

Fields

•coreTbl: data.frame with one row for each unique core level showing values for nx,ny,N,

w,u, and median observed response.

•data: data.frame is the restructured input data used for calculations in MFh and MFnest.

•compare: character vector naming groups being compared.

•formula: formula that was called by user.

Author(s)

Marie Vendettuoli <marie.c.vendettuoli@aphis.usda.gov>

See Also

MFh

mfhlboot-class Class mfhlboot

Description

class for data objects produced by HLBoot, contains class mf with additional ﬁelds MFstat, HLstat,

QDIFstat, QXstat, QYstat

Usage

mfhlboot$new(nboot, alpha, seed, compare, rng, sample, MFstat, HLstat,

QDIFstat, QXstat, QYstat)

Fields

•nboot: Numeric value specifying number of samples.

•alpha: Numeric value specifying complement of conﬁdence interval.

•seed: Vector of integers specifying seed for pseudo-random number generator used.

•compare: Vector of character strings naming groups compared.

•rng: Character string naming type of random number generator.

•sample: The bootstrapped values.

20 MFmp

•MFstatMatrix with columns observed, median, lower, upper for Equal Tailed and Highest

Density estimates of mitigated fraction (MF).

•HLstatMatrix with columns observed, median, lower, upper for Equal Tailed and Highest

Density estimates of Hodge-Lehmann estimator (HL).

•QDIFstatMatrix with columns observed, median, lower, upper for estimates of Quartile Dif-

ferences.

•QXstatMatrix with columns observed, median, lower, upper for quartiles of treatments, equal

tailed.

•QYstatMatrix with columns observed, median, lower, upper for quartiles of response, equal

tailed.

Contains

mf-class

Author(s)

Marie Vendettuoli <marie.c.vendettuoli@aphis.usda.gov>

See Also

HLBoot

Other mf: mf-class,mfboot-class,mfbootcluster-class

MFmp Mitigated fraction from matched pairs

Description

Estimates mitigated fraction from matched pairs.

Usage

MFmp(formula=NULL, data=NULL, compare = c("con", "vac"), x=NULL, alpha=0.05, df=NULL, tdist=T)

Arguments

formula Formula of the form y ~ x + cluster(w), where y is a continuous response, x

is a factor with two levels of treatment, and w is a factor indicating the clusters.

data Data frame

compare Text vector stating the factor levels - compare[1] is the control or reference

group to which compare[2] is compared

xTrinomial vector {ΣI(x<y),ΣI(x=y),ΣI(x>y)}

alpha Complement of the conﬁdence level.

df Degrees of freedom. Default N-2

tdist Use quantiles of t or Gaussian distribution for conﬁdence interval? Default t

distribution.

mfmp-class 21

Details

Estimates MF from matched pairs by the difference of multinomial fractions (ΣI(x<y)−ΣI(x >

y))/N . The trinomial vector is {ΣI(x<y),ΣI(x=y),ΣI(x>y)}

Value

amfmp-class data object

Note

upper conﬁdence interval is truncated to 1; lower conﬁdence interval is truncated to -1. Point

estimate of 1.0 indicates complete separation.

Author(s)

David Siev <david.siev@aphis.usda.gov>

References

Siev D. (2005). An estimator of intervention effect on disease severity. Journal of Modern Applied

Statistical Methods. 4:500–508

See Also

mfmp-class

Examples

MFmp(les ~ tx + cluster(cage), mlesions, compare = c('con','vac'))

MFmp(x = c(12, 12, 2))

mfmp-class Class mfmp

Description

Class mfmp is created from output of function MFmp

Usage

mfmp$new(ci, x, what, alpha, tdist, df)

Fields

•ci: numeric vector of point and interval estimates

•x: numeric vector of length three holding data

•what: text string describing interval type

•alpha: numeric value specifying complement of conﬁdence interval

•tdist: Logical indicating if t distribution(TRUE) or gaussian (FALSE)

•df: numeric value indicating degrees freedom

22 MFnest

Author(s)

Marie Vendettuoli <marie.c.vendettuoli@aphis.usda.gov>

See Also

MFmp

MFnest Summations to calculate the MF for nested data from a rank table.

Usage

MFnest(Y, which.factor = "All")

Arguments

Yrank table (tibble or data.frame), structured as $coreTbl output from MFh or

output list from MFh.

which.factor one or more variable(s) of interest. This can be any of the core or nest variables

from the data set. If none or All is speciﬁed, MF will be calculated for the

whole tree.

Value

A tibble with each unique level of a variable as a row. Other values include:

MF Mitigated fraction for the particular level of the variable in this row.

N1N2 Sum of the n1n2 variable in $coreTbl ﬁeld of mfhierdata object output by MFh for this

particular variable-level combination.

USum of u variable in $coreTbl ﬁeld of mfhierdata object output by MFh for this particular

variable-level combination.

_N Sum of the _n variable in $coreTbl ﬁeld of mfhierdata object output by MFh for this particular

variable-level combination.

_medResp Median of responses for each comparison group for this particular variable-level combi-

nation.

Note

Core variable is the variable corresponding to the lowest nodes of the hierarchial tree. Nest variables

are those above the core. All refers to a summary of the entire tree.

See Also

MFh

MFnest 23

Examples

a <- data.frame(

room = paste('Room',rep(c('W','Z'),each=24)),

pen = paste('Pen',rep(LETTERS[1:6],each=8)),

litter = paste('Litter',rep(11:22,each=4)),

tx = rep(rep(c('vac','con'),each=2),12),

stringsAsFactors = FALSE

)

set.seed(76153)

a$lung[a$tx=='vac'] <- rnorm(24,5,1.3)

a$lung[a$tx=='con'] <- rnorm(24,7,1.3)

aCore <- MFh(lung ~ tx + room/pen/litter,a)

MFnest(aCore)

# # A tibble: 1 x 9

# variable level MF N1N2 U con_N vac_N con_medResp vac_medResp

# <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

# 1 All All 0.875 48 45 24 24 7.24 4.91

MFnest(aCore$coreTbl)

# Skipping median summary, no response data provided.

# # A tibble: 1 x 7

# variable level MF N1N2 U con_N vac_N

# <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>

# 1 All All 0.875 48 45 24 24

MFnest(aCore, 'room')

# # A tibble: 2 x 9

# variable level MF N1N2 U con_N vac_N con_medResp vac_medResp

# <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

# 1 room Room W 0.833 24 22 12 12 7.79 4.85

# 2 room Room Z 0.917 24 23 12 12 6.71 4.98

MFnest(aCore, 'pen')

# Complete separation observed for variable(s): pen

# # A tibble: 6 x 9

# variable level MF N1N2 U con_N vac_N con_medResp vac_medResp

# <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

# 1 pen Pen A 0.5 8 6 4 4 6.79 4.24

# 2 pen Pen B 1 8 8 4 4 8.11 5.59

# 3 pen Pen C 1 8 8 4 4 7.69 4.85

# 4 pen Pen D 0.75 8 7 4 4 6.10 4.98

# 5 pen Pen E 1 8 8 4 4 6.86 4.86

# 6 pen Pen F 1 8 8 4 4 6.88 5.13

MFnest(aCore, c('All','litter'))

# Complete separation observed for variable(s): litter

# # A tibble: 13 x 9

# variable level MF N1N2 U con_N vac_N con_medResp vac_medResp

# <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

# 1 All All 0.875 48 45 24 24 7.24 4.91

# 2 litter Litter 11 1 4 4 2 2 8.24 5.13

# 3 litter Litter 12 0 4 2 2 2 4.91 3.81

# 4 litter Litter 13 1 4 4 2 2 8.10 5.23

# 5 litter Litter 14 1 4 4 2 2 8.11 5.59

# 6 litter Litter 15 1 4 4 2 2 8.09 5.26

24 MFnestBoot

# 7 litter Litter 16 1 4 4 2 2 6.77 4.50

# 8 litter Litter 17 1 4 4 2 2 5.58 4.26

# 9 litter Litter 18 0.5 4 3 2 2 7.44 6.33

# 10 litter Litter 19 1 4 4 2 2 7.98 4.58

# 11 litter Litter 20 1 4 4 2 2 6.78 4.86

# 12 litter Litter 21 1 4 4 2 2 6.82 5.36

# 13 litter Litter 22 1 4 4 2 2 7.27 5.13

MFnest(aCore, 'litter')

# Complete separation observed for variable(s): litter

# # A tibble: 12 x 9

# variable level MF N1N2 U con_N vac_N con_medResp vac_medResp

# <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

# 1 litter Litter 11 1 4 4 2 2 8.24 5.13

# 2 litter Litter 12 0 4 2 2 2 4.91 3.81

# 3 litter Litter 13 1 4 4 2 2 8.10 5.23

# 4 litter Litter 14 1 4 4 2 2 8.11 5.59

# 5 litter Litter 15 1 4 4 2 2 8.09 5.26

# 6 litter Litter 16 1 4 4 2 2 6.77 4.50

# 7 litter Litter 17 1 4 4 2 2 5.58 4.26

# 8 litter Litter 18 0.5 4 3 2 2 7.44 6.33

# 9 litter Litter 19 1 4 4 2 2 7.98 4.58

# 10 litter Litter 20 1 4 4 2 2 6.78 4.86

# 11 litter Litter 21 1 4 4 2 2 6.82 5.36

# 12 litter Litter 22 1 4 4 2 2 7.27 5.13

MFnest(aCore, c('room','pen','litter'))

# # A tibble: 20 x 9

# variable level MF N1N2 U con_N vac_N con_medResp vac_medResp

# <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

# 1 room Room W 0.833 24 22 12 12 7.79 4.85

# 2 room Room Z 0.917 24 23 12 12 6.71 4.98

# 3 pen Pen A 0.5 8 6 4 4 6.79 4.24

# 4 pen Pen B 1 8 8 4 4 8.11 5.59

# 5 pen Pen C 1 8 8 4 4 7.69 4.85

# 6 pen Pen D 0.75 8 7 4 4 6.10 4.98

# 7 pen Pen E 1 8 8 4 4 6.86 4.86

# 8 pen Pen F 1 8 8 4 4 6.88 5.13

# 9 litter Litter 11 1 4 4 2 2 8.24 5.13

# 10 litter Litter 12 0 4 2 2 2 4.91 3.81

# 11 litter Litter 13 1 4 4 2 2 8.10 5.23

# 12 litter Litter 14 1 4 4 2 2 8.11 5.59

# 13 litter Litter 15 1 4 4 2 2 8.09 5.26

# 14 litter Litter 16 1 4 4 2 2 6.77 4.50

# 15 litter Litter 17 1 4 4 2 2 5.58 4.26

# 16 litter Litter 18 0.5 4 3 2 2 7.44 6.33

# 17 litter Litter 19 1 4 4 2 2 7.98 4.58

# 18 litter Litter 20 1 4 4 2 2 6.78 4.86

# 19 litter Litter 21 1 4 4 2 2 6.82 5.36

# 20 litter Litter 22 1 4 4 2 2 7.27 5.13

MFnestBoot MFnestBoot

MFnestBoot 25

Description

MFnest using bootstrapping

Usage

MFnestBoot(x, which.factor = "All", alpha = 0.05)

Arguments

xoutput from MFhBoot

which.factor Which variables to include in the mitigated fraction summation. Default is

âC™AllâC™, to sum over entire tree.

alpha Passed to emp.hpd to calculate high tailed upper and high tailed lower of miti-

gated fraction

Value

A list with the following elements:

mfnest_details The MF and summary statistics as calculated for each bootstrap event. Variables

as in MFnest output.

mfnest_summary Statistical summary of bootstrapped MF with each unique level of a core or nest

variable passed to which.factor as a row. Other variables include:

•median Median of MFs from all of the bootstrap events.

•etlower Lower value of equal tailed range.

•etupper Upper value of equal tailed range.

•htlower Lower value of the high tailed range.

•htupper Upper value of the high tailed range.

•mf.obs MF calculated from data using MFh.

following variables for each

a table with one row for each level of the variable speciﬁed in which.factor and including the

following variables:

median median mitigated fraction across all bootstrapping instances.

etlower equal tailed lower of the mitigated fraction across all bootstrapping instances.

etupper equal tailed upper of the mitigated fraction across all bootstrapping instances.

htlower high tailed lower of the mitigated fraction across all bootstrapping instances.

htupper high tailed upper of the mitigated fraction across all bootstrapping instances.

mf.obs mitigated fraction using MFnest(x$mfh, which.factor), no bootstrapping.

See Also

MFClusBootHier,MFhBoot

26 MFr

Examples

set.seed(76153)

a <- data_frame(room = paste('Room', rep(c('W','Z'), each = 24)),

pen = paste('Pen', rep(LETTERS[1:6], each = 8)),

litter = paste('Litter', rep(11:22, each = 4)),

tx = rep(rep(c('vac','con'), each = 2), 12)) %>%

mutate(lung = ifelse(tx == 'vac', rnorm(24, 5, 1.3), rnorm(24, 7, 1.3)))

formula <- lung ~ tx + room/pen/litter

nboot <- 10000

boot.cluster <- TRUE

boot.unit <- TRUE

which.factors <- c('All','room','pen','litter')

#################

test1 <- MFhBoot(formula, a,

nboot = 10000,

boot.cluster = TRUE, boot.unit = TRUE, seed = 12345)

MFnestBoot(test1, c('All','litter'))

## Not run:

system.time(test2 <- MFnestBoot(test1, which.factors))

test2

system.time(test3 <- MFnestBoot(test1, which.factors[1]))

test3

system.time(test4 <- MFnestBoot(test1, which.factors[2]))

test4

system.time(test5 <- MFnestBoot(test1, which.factors[2:3]))

test5

system.time(test6 <- MFnestBoot(test1, which.factors[2:4]))

test6

## End(Not run)

MFr Mitigated fraction

Description

Mitigated fraction comparing treatment to control.

Usage

MFr(formula, data, compare = c("con", "vac"))

Arguments

formula Formula of the form y~x, where y is a continuous response and x is a factor

with two levels

data Data frame

compare Text vector stating the factor levels – compare[1] is the control or reference

group to which compare[2] is compared

MFSubj 27

Details

The mitigated fraction is an estimator that quantiﬁes an intervention’s effect on reducing the severity

of a condition. Since its units are on the probability scale, it is often a good idea to accompany it

with an estimator on the original scale of measurement.

Value

The estimated mitigated fraction.

Author(s)

David Siev

References

Siev D, 2005. An estimator of intervention effect on disease severity. Journal of Modern Applied

Statistical Methods. 4:500-508

Examples

MFr(lesion~group,calflung)

# [1] 0.44

MFSubj Subject components of mitigated fraction

Description

Estimates the subject components of the mitigated fraction.

Usage

MFSubj(formula, data, compare = c("con", "vac"))

Arguments

formula Formula of the form y~x, where y is a continuous response and x is a factor

with two levels

data Data frame

compare Text vector stating the factor levels - compare[1] is the control or reference

group to which compare[2] is compared

Details

The mitigated fraction is an estimator that quantiﬁes an intervention’s effect on reducing the sever-

ity of a condition. Since its units are on the probability scale, it is often a good idea to accompany

it with an estimator on the original scale of measurement.

The subject components are the individual contributions of the treated subjects to MF, which is

the average of the subject components.

28 mlesions

Value

amfcomponents-class data object

Author(s)

David Siev <david.siev@aphis.usda.gov>

References

Siev D. (2005). An estimator of intervention effect on disease severity. Journal of Modern Applied

Statistical Methods. 4:500–508

Examples

x <- MFSubj(lesion ~ group, calflung)

# MF = 0.44 comparing vac to con

# MF Subject Components

# mf.j freq min.y max.y

# 1.00 6 0.000030 0.00970

# 0.84 1 0.012500 0.01250

# 0.76 3 0.016650 0.02030

# 0.68 6 0.023250 0.03190

# 0.04 1 0.132100 0.13210

# -0.04 3 0.144575 0.16325

# -0.20 2 0.210000 0.21925

# -0.36 1 0.292000 0.29200

# -0.52 1 0.356500 0.35650

# -0.84 1 0.461500 0.46150

mean(x$subj[,'mf.j'])

# [1] 0.44

mlesions mlesions dataset

Description

Post-mortem examination of the lungs of dogs housed in cages by pairs.

Format

A data frame with 52 observations of the following 3 variables, no NAs.

cage Cage ID. 1 - 26.

tx Treatment. One of ’con’ or ’vac’.

les Percent gross lung lesions.

piglung 29

piglung piglung dataset

Description

Post-mortem examination of the lungs of pigs in litters.

Format

A data frame with 102 observations of the following 3 variables, no NAs.

lesion Percent gross lung lesions.

group Treatment group. One of ’con’ or ’vac’.

litter Litter ID.

Index

∗Topic datasets

calflung,3

mlesions,28

piglung,29

∗Topic documentation

mf-class,5

mfboot-class,7

mfbootcluster-class,8

mfcluster-class,15

mfcomponents-class,15

mfhierdata-class,19

mfhlboot-class,19

mfmp-class,21

calflung,3

calflung-data (calflung),3

emp.hpd,12,25

HLBoot,3,20

MF (MF-package),2

mf (mf-class),5

mf-class,5

MF-package,2

MFBoot,6,7

mfboot (mfboot-class),7

mfboot-class,7

mfbootcluster (mfbootcluster-class),8

mfbootcluster-class,8

MFClus,9,15

MFClusBoot,8,10

MFClusBootHier,12,18,25

MFClusHier,13,17

mfcluster (mfcluster-class),15

mfcluster-class,15

mfcomponents (mfcomponents-class),15

mfcomponents-class,15

MFh,13,14,16,18,19,22,25

MFhBoot,12,13,17,25

mfhierdata,16,17,22

mfhierdata (mfhierdata-class),19

mfhierdata-class,19

mfhlboot (mfhlboot-class),19

mfhlboot-class,19

MFmp,20,22

mfmp (mfmp-class),21

mfmp-class,21

MFnest,13,14,17,22,25

MFnestBoot,12,13,18,24

MFr,26

MFSubj,16,27

mlesions,28

mlesions-data (mlesions),28

piglung,29

piglung-data (piglung),29

tibble,16

MF Manual

Navigation menu

Versions of this User Manual:

Views

Navigation