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### multiple comparison procedures for levels of
### factors in AN(C)OVA models
mcp <- function(...) {
linfct <- list(...)
linfct <- lapply(linfct, function(x) {
if (is.numeric(x) && !is.matrix(x)) {
return(matrix(x, nrow = 1))
} else {
return(x)
}})
if (is.null(names(linfct)))
stop(sQuote("linfct"), " doesn't have a ", sQuote("names"),
" attribute")
classes <- sapply(linfct, function(x) inherits(x, "matrix") || inherits(x, "character"))
if (all(classes)) {
class(linfct) <- "mcp"
return(linfct)
}
stop("Arguments don't consist of either matrices or characters")
}
### extract factors and contrast matrices used in `model'
factor_contrasts <- function(model) {
### extract model matrix, frame and terms
mm <- try(model.matrix(model))
if (inherits(mm, "try-error"))
stop("no ", sQuote("model.matrix"), " method for ",
sQuote("model"), " found!")
mf <- try(model.frame(model))
if (inherits(mf, "try-error"))
stop("no ", sQuote("model.frame"), " method for ",
sQuote("model"), " found!")
tm <- try(terms(model))
if (inherits(tm, "try-error"))
stop("no ", sQuote("terms"), " method for ",
sQuote("model"), " found!")
list(contrasts = attr(mm, "contrasts"),
factors = attr(tm, "factors"),
intercept = attr(tm, "intercept") != 0,
mm = mm,
mf = mf)
}
### convert linear hypotheses supplied as single matrices,
### type arguments or expressions into one matrix
mcp2matrix <- function(model, linfct) {
### extract factors and contrasts
fc <- factor_contrasts(model)
contrasts <- fc$contrasts
factors <- fc$factors
intercept <- fc$intercept
mf <- fc$mf
mm <- fc$mm
alternative <- NULL
### linear hypotheses
if (!is.list(linfct) || is.null(names(linfct)))
stop(sQuote("linfct"), "is not a named list")
nhypo <- names(linfct)
checknm <- nhypo %in% rownames(factors) & sapply(mf[nhypo], is.factor)
if (!all(checknm))
stop("Factor(s) ", nhypo[!checknm], " have been specified in ",
sQuote("linfct"), " but cannot be found in ", sQuote("model"), "!")
m <- c()
ctype <- c()
for (nm in nhypo) {
if (is.character(linfct[[nm]])) {
Kchr <- function(kch) {
### check if kch is suitable as `type' argument to `contrMat'
types <- eval(formals(contrMat)$type)
pm <- pmatch(kch, types)
### if yes, compute K from `contrMat'
if (!is.na(pm)) {
tmpK <- contrMat(table(mf[[nm]]), type = types[pm])
ctype <<- c(ctype, types[pm])
} else {
### if not, interpret kch as an expression
tmp <- chrlinfct2matrix(kch, levels(mf[[nm]]))
tmpK <- tmp$K
m <<- c(m, tmp$m)
alternative <<- tmp$alternative
}
if (is.null(rownames(tmpK)))
rownames(tmpK) <- paste(kch, 1:nrow(tmpK), sep = "_")
if (length(nhypo) > 1)
rownames(tmpK) <- paste(nm, rownames(tmpK), sep = ": ")
list(K = tmpK)
}
tmp <- lapply(linfct[[nm]], Kchr)
linfct[[nm]] <- do.call("rbind", lapply(tmp, function(x) x$K))
}
}
### transform linear hypotheses using model contrasts
hypo <- vector(mode = "list", length = length(nhypo))
names(hypo) <- nhypo
for (nm in nhypo) {
### extract contrast matrix for each factor from model fit
if (is.character(contrasts[[nm]])) {
C <- do.call(contrasts[[nm]],
list(n = nlevels(mf[[nm]])))
} else {
C <- contrasts[[nm]]
}
### and transform the original linear hypotheses
### K beta to K C beta^*
if (intercept) {
Kstar <- linfct[[nm]] %*% C
} else {
### model.matrix has `contrasts' argument even if no intercept
### was fitted and the contrast actually hasn't been applied
Kstar <- linfct[[nm]]
}
pos <- factors[nm,] == 1
### average over interaction terms (if any)
if (sum(pos) > 1) {
Kinter <- c()
for (i in which(pos)[-1]) {
k <- sum(attr(mm, "assign") == i) / ncol(Kstar)
if (sum(factors[1:which(rownames(factors) == nm), i]) == 1) {
Kinter <- cbind(Kinter,
Kstar[,rep(1:ncol(Kstar), k), drop = FALSE] / (k + 1))
} else {
Kinter <- cbind(Kinter,
Kstar[,rep(1:ncol(Kstar), rep(k, ncol(Kstar))),
drop = FALSE] / (k + 1))
}
}
Kstar <- cbind(Kstar, Kinter)
}
hypo[[nm]] <- list(K = Kstar,
where = attr(mm, "assign") %in% which(factors[nm,] == 1))
}
### combine all single matrices computed so far into
### one matrix of all linear hypoheses
Ktotal <- matrix(0, nrow = sum(sapply(hypo, function(x) nrow(x$K))),
ncol = ncol(mm))
colnames(Ktotal) <- colnames(mm)
count <- 1
for (h in hypo) {
Ktotal[count:(count + nrow(h$K) - 1), h$where] <- h$K
count <- count + nrow(h$K)
}
if (!is.matrix(Ktotal)) Ktotal <- matrix(Ktotal, nrow = 1)
rownames(Ktotal) <- unlist(lapply(hypo, function(x) rownames(x$K)))
if (is.null(ctype))
ctype <- "User-defined"
ctype <- paste(unique(ctype), collapse = ", ")
if (length(m) == 0) m <- 0
list(K = Ktotal, m = m, alternative = alternative, type = ctype)
}
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