robustrank (version 2024.1-28)

sim.partially.matched: 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)

Value

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

X

m sample 1 that pair with Y

Y

m sample 2 that pair with X

Xprime

n.x sample 1

Yprime

n.y sample 2

r2sample returns a list with the following components:

X

m sample 1 that are independent of Y

Y

n sample 2 that are independent of X

Arguments

m

Number of pairs.

n

Number of Ys.

n.x

Number of extra Xs.

n.y

Number of extra Ys.

distr

Distributions.

params

Named vector. See details.

seed

Seed for random number generator.

meanRatio

meanRatio

sdRatio

sdRatio

within.sd

within.sd

type

type

hyp

hyp

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

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