Nothing
wjglm <- function(Cmat, Umat, y, nx, trimming, per, bootstrap, numsim_b, effect.size, numsim_es,
standardizer, scaling, alpha, seed){
opt1 = trimming
opt2 = bootstrap
opt3 = effect.size
if(seed != 0){ set.seed(seed) }
if(is.vector(y)){
y = t(t(y))
}
vars.initial = .initial(Cmat,Umat,y,nx,opt1,per,opt2,opt3,scaling,alpha)
ntot = vars.initial$ntot
wobs = vars.initial$wobs
wobs1 = vars.initial$wobs1
bobs = vars.initial$bobs
r = vars.initial$r
x = vars.initial$x
vars.mnmod = .mnmod(y,x,opt1,per, bobs,wobs,wobs1,ntot,nx)
bhat = vars.mnmod$bhat
bhatw = vars.mnmod$bhatw
muhat = vars.mnmod$muhat
yt = vars.mnmod$yt
dfr = vars.mnmod$dfr
vars.sigmod = .sigmod(yt,x,bhatw,dfr,bobs,wobs,wobs1,ntot,nx)
sigma = vars.sigmod$sigma
stdizer = vars.sigmod$stdizer
vars.testmod = .testmod(sigma,muhat,r,dfr,bobs,wobs,wobs1,ntot,nx)
fstat = vars.testmod$fstat
dfr1 = vars.testmod$dfr1
dfr2 = vars.testmod$dfr2
multp = NULL
effsz = NULL
fmat = NULL
esmat = NULL
if(opt3){ # compute effect size
vars.wjeffsz = .wjeffsz (standardizer,scaling,opt1,per,r,muhat,stdizer, bobs,wobs,wobs1,ntot,nx)
multp = vars.wjeffsz$multp
effsz = vars.wjeffsz$effsz
stdizer = vars.wjeffsz$stdizer
}
if(opt2){ # use bootstrap
fmat = replicate(numsim_b, expr=
{
yb1 = (.bootdat(y,bobs,ntot,nx))$yb1
yb = (.bootcen(yb1,bhat,bobs,ntot,nx))$yb
res = .bootstat(yb,x,opt1,per,r, bobs,wobs,wobs1,ntot,nx) # value obtained by this block of function calls
res$fstat
})
fmat = sort(fmat)
}
if(opt3){ # compute effect size using bootstrap
esmat = replicate(numsim_es, expr =
{
yb1 = (.bootdat(y,bobs,ntot,nx))$yb1
res = .bootes(yb1,x,standardizer,scaling,opt1,per,r, bobs,wobs,wobs1,ntot,nx) # value obtained by this block of function calls
res$effsz
})
esmat = sort(esmat)
}
#*** calculate significance level for welch-james statistic****
result.list = list()
if(!opt2){ # do not use bootstrap estimators
# pf is the (cumulative) distribution function of an F distribution
pval = 1 - pf(fstat,dfr1,dfr2)
result.list$pval = pval
}
else{ # use bootstrap estimators
avec = (fmat >= fstat)
pval = sum(avec)/numsim_b
result.list$pval = pval
}
lcl = NULL
ucl = NULL
if(opt3){ # compute effect size
ind1 = as.integer(numsim_es * (alpha/2)) + 1
ind2 = numsim_es - as.integer((numsim_es * (alpha/2)))
lcl = esmat[ind1]
ucl = esmat[ind2]
}
result.list$Cmatrix = Cmat
result.list$Umatrix = Umat
result.list$welch.T = fstat
result.list$numeratorDF = dfr1
result.list$denominatorDF = dfr2
result.list$contrast.matrix = r
result.list$mean.vector = muhat
result.list$sigma.matrix = sigma
result.list$trimming = as.logical(opt1)
result.list$bootstrap = as.logical(opt2)
result.list$compute.effsz = as.logical(opt3)
result.list$scaling = as.logical(scaling)
result.list$standardize.effsz = as.logical(standardizer)
# ****store results****
if(!opt1){
}
else{
result.list$trimming.per = per
}
if(!opt2 && !opt3){
}
if(opt2 && !opt3){
result.list$boot.samples = numsim_b
result.list$seed = seed
}
if(!opt3){ # don't compute effect size
}
else{
cilev = (1 - alpha)*100
result.list$effsz = effsz
result.list$stdizer
if(!scaling){
}
else{
result.list$scaling.factor = multp
}
result.list$effsz.samples = numsim_es
result.list$seed = seed
result.list$CI.level = cilev
result.list$CI = c(lcl, ucl)
}
return(result.list)
}
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