Make Recommendations on the Most Powerful Test to Use

Description

Performs simulations to compare the power of different tests

Usage

choose.test(Xpaired, Ypaired, Xextra = NULL, Yextra = NULL, mc.rep = 1000)

Arguments

Examples

# There are unpaired observations from both samples
dat=sim.partially.matched(m=20,n.x=40,n.y=5,distr="normal",
    params=c(loc.2=.8,rho=.1,scale.2=1),seed=1)
choose.test(dat$X, dat$Y, dat$Xprime, dat$Yprime)

## There are unpaired observations from only one sample
#dat=sim.partially.matched(m=20,n.x=0,n.y=10,distr="normal",
#    params=c(loc.2=.5,rho=.8,scale.2=1),seed=1)
#choose.test(dat$X, dat$Y, dat$Xprime, dat$Yprime)

Example Dataset

Description

from MTCT correlates study, C-section only

Usage

data("dat.mtct.rob")

Format

A data frame with 55 observations on the following 2 variables.

y

a numeric vector

V3_BioV3B_500

a numeric vector

References

Fong and Huang (2016) Modified Wilcoxon-Mann-Whitney Test and Power against Strong Null.

Modified Wilcoxon-Mann-Whitney Test

Description

Also known as the Fligner-Policello test.

Usage

mod.wmw.test(X, Y, alternative = c("two.sided", "less", "greater"),
         correct = TRUE, perm = NULL, mc.rep = 10000, method =
         c("combine", "comb2", "fp", "wmw", "fplarge", "nsm3"),
         verbose = FALSE, mode = c("test", "var"), useC = TRUE)

Arguments

Details

When perm is null, we will compute permutation-based p values if either sample size is less than 20 and compute normal approximation-based p values otherwise. When doing permuation, if the possible number of combinations is less than mc.rep, every possible configuration is done.

Value

A p value for now.

References

manuscript in preperation

Examples

# Example 4.1, Hollander, Wolfe and Chicken (2014) Nonparameteric Statistics
X <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46)
Y <- c(1.15, 0.88, 0.90, 0.74, 1.21)
mod.wmw.test(X, Y, method="wmw", alternative="greater")
mod.wmw.test(X, Y, method="combine", alternative="greater", verbose=1)

# Section 4.1 Problem 1, Hollander et al. 
X=c(1651,1112,102.4,100,67.6,65.9,64.7,39.6,31.0)
Y=c(48.1,48.0,45.5,41.7,35.4,34.3,32.4,29.1,27.3,18.9,6.6,5.2,4.7)
mod.wmw.test(X, Y, method="wmw")
mod.wmw.test(X, Y, method="combine", verbose=1)

# Section 4.1 Problem 5, Hollander et al. 
X=c(12 ,44 ,34 ,14 ,9  ,19 ,156,23 ,13 ,11 ,47 ,26 ,14 ,33 ,15 ,62 ,5  ,8  ,0  ,154,146)
Y=c(37,39,30,7,13,139, 45,25,16,146,94,16,23,1,290,169,62,145,36, 20, 13)
mod.wmw.test(X, Y, method="wmw", alternative="less")
mod.wmw.test(X, Y, method="combine", alternative="less", verbose=1)

# Section 4.1 Problem 15, Hollander et al. 
X=c(0.19,0.14,0.02,0.44,0.37)
Y=c(0.89,0.76,0.63,0.69,0.58,0.79,0.02,0.79)
mod.wmw.test(X, Y, method="wmw")
mod.wmw.test(X, Y, method="combine", verbose=1)

# Table 4.7, Hollander et al. 
X=c(297,340,325,227,277,337,250,290)
Y=c(293,291,289,430,510,353,318)
mod.wmw.test(X, Y, method="wmw", alternative="less")
mod.wmw.test(X, Y, method="combine", alternative="less", verbose=1)

Multinom Test

Description

Perform multinom test.

Usage

multinom.test(X, Y, alternative = c("two.sided", "less", "greater"),
         correct = FALSE, perm = NULL, mc.rep = 10000, method =
         c("exact.2", "large.0", "large", "exact", "exact.0",
         "exact.1", "exact.3"), verbose = FALSE, mode =
         c("test", "var"), useC = TRUE)

Arguments

A Test that Combines WMW for Paired Data and WMW for Unpaired Data

Description

Use permutation-based reference distribution to obtain p values for a test that combines WMW for paired data and WMW for unpaired data

Usage

mw.mw.2.perm(X, Y, Xprime, Yprime, .corr, mc.rep = 10000, 
    alternative = c("two.sided", "less", "greater"), verbose = FALSE)

Arguments

WMW test for paired data

Description

Performs a WMW-type test of the strong null for paired data.

Usage

pair.wmw.test(X, Y, alternative = c("two.sided", "less", "greater"),
 correct = TRUE, perm = NULL, mc.rep = 10000, method =
 c("exact.2", "large.0", "large", "exact", "exact.0",
 "exact.1", "exact.3"), verbose = FALSE, mode =
 c("test", "var"), p.method = NULL, useC = TRUE)

Arguments

Details

When perm is NULL, if (min(m,n)>=20) normal approximatino is used to find p value, otherwise permutation test is used. When permutation test is used, if the number of possible permutations is less than mc.rep, a test statistic is computed for all permutations; otherwise, Monte Carlo is done.

Value

P value for now.

References

Under prep.

Examples

dat=sim.partially.matched(m=15,n.x=0,n.y=20,distr="mixnormal",params=c(p.1=0.3,p.2=0.3),seed=1)
X=dat$X; Y=dat$Y
pair.wmw.test(X, Y, perm=TRUE,  method="large.0", verbose=1)
pair.wmw.test(X, Y, perm=FALSE, method="large.0", verbose=1)

Wilcoxon test for Partially Matched Two Sample Data

Description

Performs rank-based two sample test for partially matched two sample data by combining information from matched and unmatched data

Usage

pm.wilcox.test(Xpaired, Ypaired, Xextra = NULL, Yextra = NULL,
 alternative = c("two.sided", "less", "greater"),
 method = c("SR-MW", "MW-MW", "all"), mode = c("test",
 "var", "power.study"), useC = FALSE, correct = NULL,
 verbose=FALSE)

Arguments

Details

If Xpaired and Ypaired have NAs, the corresponding unpaired data in Ypaired and Xpaired will be combined with Yextra and Xextra.

Value

An htest object.

Examples

set.seed(1)
z=rnorm(20, sd=0.5) # induces correlation between X and Y
X=rnorm(20)+z
Y=rnorm(20,mean=0.8)+z
X[1:10]=NA
boxplot(X,Y,names=c("X","Y"))

pm.wilcox.test(X,Y)
# for comparison
wilcox.test(X,Y,paired=TRUE)
wilcox.test(X,Y,paired=FALSE)# often a conservative test due to the correlation

# no paired data
Y1=Y
Y1[11:20]=NA
pm.wilcox.test(X,Y1)
# should match the following
wilcox.test(X,Y1,paired=FALSE)

# only 1 pair of matched data
Y1=Y
Y1[12:20]=NA
pm.wilcox.test(X,Y1)

robustrank

Description

Please see the Index link below for a list of available functions.

Simulate Paired, Independent, or Partially Matched Two-Sample Data

Description

sim.partially.matched generates partially matched two-sample data. for Monte Carlo studies. r2sample is a wrapper for sim.partially.matched and generates indepenent two-sample data.

Usage

sim.partially.matched(m, n.x, n.y, 
distr = c("normal","logistic","student","mixnormal","gamma","lognormal","beta",
    "uniform","hybrid1","hybrid2","doublexp"), params, seed)

r2sample(m, n, 
distr = c("normal", "logistic", "student", "mixnormal"), params, seed)

sim.paired.with.replicates(m, meanRatio, sdRatio, within.sd, type, hyp, distr, seed)

Arguments

Details

If the distribution is in c("normal","student","logistic"), params should have three fields: loc.2, rho and scale.2; loc.1 is set to 0 and scale.1 is set to 1.

If the distribution is mixnormal, params should have three fields: p.1, p.2 and sd.n.

If the distribution is gamma, params should have fix fields: loc.2, shape.1, shape.2, rate.1, rate.2 and rho.

For details on bivariate logistic distribution, see rbilogistic

Value

sim.partially.matched return a list with the following components:

r2sample returns a list with the following components:

Examples

dat=sim.partially.matched(m=10,n.x=5,n.y=4,distr="normal",
    params=c("loc.2"=0,"rho"=0,"scale.2"=1),seed=1)
X=dat$X; Y=dat$Y; Yprime=dat$Yprime    

#dat=sim.partially.matched(m=10,n.x=5,n.y=4,distr="logistic",
#    params=c("loc.2"=0,"rho"=0,"scale.2"=1),seed=1)
#X=dat$X; Y=dat$Y; Yprime=dat$Yprime    

WMW Paired Replicates Test

Description

Perform WMW paired replicates test.

Usage

wmw.paired.replicates.test(X, Y, alternative = c("two.sided", "less", "greater"),
 correct = FALSE, perm = NULL, mc.rep = 10000, method =
 c("exact.2", "large.0", "large", "exact", "exact.0",
 "exact.1", "exact.3"), verbose = FALSE, mode =
 c("test", "var"), useC = TRUE)

Arguments