Nothing
bwtrim <- function(formula, id, data, tr = 0.2, ...){
if (missing(data)) {
mf <- model.frame(formula)
} else {
mf <- model.frame(formula, data)
}
cl <- match.call()
mf1 <- match.call()
m <- match(c("formula", "data", "id"), names(mf1), 0L)
mf1 <- mf1[c(1L, m)]
mf1$drop.unused.levels <- TRUE
mf1[[1L]] <- quote(stats::model.frame)
mf1 <- eval(mf1, parent.frame())
random1 <- mf1[, "(id)"]
depvar <- colnames(mf)[1]
## check which one is the within subjects factor
if (all(length(table(random1)) == table(mf[,3]))) {
ranvar <- colnames(mf)[3]
fixvar <- colnames(mf)[2]
} else {
ranvar <- colnames(mf)[2]
fixvar <- colnames(mf)[3]
}
K <- length(table(mf[, ranvar])) ## number of repeated measurements
J <- length(table(mf[, fixvar])) ## number of levels
p <- J*K
grp <- 1:p
fixsplit <- split(mf[,depvar], mf[,fixvar])
indsplit <- split(mf[,ranvar], mf[,fixvar])
dattemp <- mapply(split, fixsplit, indsplit, SIMPLIFY = FALSE)
data <- do.call(c, dattemp)
tmeans<-0
h<-0
v<-matrix(0,p,p)
klow<-1-K
kup<-0
for (i in 1:p)tmeans[i]<-mean(data[[grp[i]]],tr,na.rm=TRUE)
for (j in 1:J){
h[j]<-length(data[[grp[j]]])-2*floor(tr*length(data[[grp[j]]]))
# h is the effective sample size for the jth level of factor A
# Use covmtrim to determine blocks of squared standard errors and
# covariances.
klow<-klow+K
kup<-kup+K
sel<-c(klow:kup)
v[sel,sel]<-covmtrim(data[grp[klow:kup]],tr)
}
ij<-matrix(c(rep(1,J)),1,J)
ik<-matrix(c(rep(1,K)),1,K)
jm1<-J-1
cj<-diag(1,jm1,J)
for (i in 1:jm1)cj[i,i+1]<-0-1
km1<-K-1
ck<-diag(1,km1,K)
for (i in 1:km1)ck[i,i+1]<-0-1
# Do test for factor A
cmat<-kron(cj,ik) # Contrast matrix for factor A
Qa<-johansp(cmat,tmeans,v,h,J,K)
# Do test for factor B
cmat<-kron(ij,ck) # Contrast matrix for factor B
Qb<-johansp(cmat,tmeans,v,h,J,K)
# Do test for factor A by B interaction
cmat<-kron(cj,ck) # Contrast matrix for factor A by B
Qab<-johansp(cmat,tmeans,v,h,J,K)
result <- list(Qa=Qa$teststat, A.p.value=as.numeric(Qa$siglevel), A.df = Qa$df, Qb=Qb$teststat, B.p.value=as.numeric(Qb$siglevel), B.df = Qb$df,
Qab=Qab$teststat, AB.p.value=as.numeric(Qab$siglevel), AB.df = Qab$df,
call = cl, varnames = c(depvar, fixvar, ranvar))
class(result) <- c("bwtrim")
result
}
tsplit <- bwtrim
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.