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## here the multiple contrast test related functions
## performs multiple contrast test (MCP part of MCPMod)
MCTtest <- function(dose, resp, data = NULL, models, S = NULL,
type = c("normal", "general"),
addCovars = ~1, placAdj = FALSE,
alpha = 0.025, df = NULL, critV = NULL, pVal = TRUE,
alternative = c("one.sided", "two.sided"),
na.action = na.fail, mvtcontrol = mvtnorm.control(),
contMat = NULL){
## perform multiple contrast test
type <- match.arg(type)
alternative <- match.arg(alternative)
## check for valid arguments
cal <- as.character(match.call())
lst <- checkAnalyArgs(dose, resp, data, S, type,
addCovars, placAdj, na.action, cal)
dd <- lst$dd;type <- lst$type;S <- lst$S
doseNam <- lst$doseNam;respNam <- lst$respNam
## calculate optimal contrasts and test-statistics
doses <- unique(dd[[doseNam]])
k <- length(doses)
if(type == "normal"){
dd[, doseNam] <- as.factor(dd[, doseNam])
form <- paste(respNam, "~", doseNam, "+", addCovars[2], "-1", sep="")
lm.fit <- lm(as.formula(form), data = dd)
est <- coef(lm.fit)[1:k]
vc <- vcov(lm.fit)[1:k, 1:k]
df <- lm.fit$df.residual
} else {
est <- dd[[respNam]]
vc <- S
if(is.null(df))
df <- Inf
}
if(is.null(contMat)){ # calculate optimal contrasts
contMat <- optContr(models, doses, S=vc, placAdj=placAdj)$contMat
rownames(contMat) <- doses
} else { # contrast matrix specified
if(inherits(contMat, "optContr"))
contMat <- contMat$contMat
if(nrow(contMat) != length(est))
stop("contMat of incorrect dimensions")
}
ct <- as.vector(est %*% contMat)
covMat <- t(contMat) %*% vc %*% contMat
den <- sqrt(diag(covMat))
tStat <- ct/den
if(alternative == "two.sided"){
tStat <- abs(tStat)
}
corMat <- cov2cor(covMat)
if(is.null(critV)){
if(!pVal){
stop("either p-values or critical value need to be calculated.")
}
} else if(is.logical(critV) & critV == TRUE){
critV <- critVal(corMat, alpha, df, alternative, mvtcontrol)
attr(critV, "Calc") <- TRUE # determines whether cVal was calculated
} else {
pVal <- FALSE # pvals are not calculated if critV is supplied
attr(critV, "Calc") <- FALSE
}
if(pVal){
pVals <- pValues(contMat, corMat, df, tStat,
alternative, mvtcontrol)
}
res <- list(contMat = contMat, corMat = corMat, tStat = tStat,
alpha = alpha, alternative = alternative[1])
if(pVal)
attr(res$tStat, "pVal") <- pVals
res$critVal <- critV
class(res) <- "MCTtest"
res
}
print.MCTtest <- function(x, digits = 3, eps = 1e-3, ...){
cat("Multiple Contrast Test\n")
cat("\n","Contrasts:","\n", sep="")
print(round(x$contMat, digits))
cat("\n","Contrast Correlation:","\n", sep="")
print(round(x$corMat, digits))
cat("\n","Multiple Contrast Test:","\n",sep="")
ord <- rev(order(x$tStat))
if(!any(is.null(attr(x$tStat, "pVal")))){
pval <- format.pval(attr(x$tStat, "pVal"),
digits = digits, eps = eps)
dfrm <- data.frame(round(x$tStat, digits)[ord],
pval[ord])
names(dfrm) <- c("t-Stat", "adj-p")
} else {
dfrm <- data.frame(round(x$tStat, digits)[ord])
names(dfrm) <- c("t-Stat")
}
print(dfrm)
if(!is.null(x$critVal)){
twoSide <- x$alternative == "two.sided"
vec <- c(" one-sided)", " two-sided)")
cat("\n","Critical value: ", round(x$critVal, digits), sep="")
if(attr(x$critVal, "Calc")){
cat(" (alpha = ", x$alpha,",", vec[twoSide+1], "\n", sep="")
} else {
cat("\n")
}
}
}
critVal <- function(corMat, alpha = 0.025, df = NULL,
alternative = c("one.sided", "two.sided"),
control = mvtnorm.control()){
## calculate critical value
alternative <- match.arg(alternative)
if(missing(corMat))
stop("corMat needs to be specified")
if(is.null(df))
stop("degrees of freedom need to be specified")
tail <- ifelse(alternative[1] == "two.sided",
"both.tails", "lower.tail")
if (!missing(control)) {
if(!is.list(control)) {
stop("when specified, 'control' must be a list")
}
ctrl <- do.call("mvtnorm.control", control)
} else {
ctrl <- control
}
if(!is.finite(df)) # normal case
df <- 0
qmvtCall <- c(list(1-alpha, tail = tail, df = df, corr = corMat,
algorithm = ctrl, interval = ctrl$interval))
do.call("qmvt", qmvtCall)$quantile
}
checkAnalyArgs <- function(dose, resp, data, S, type,
addCovars, placAdj, na.action, cal){
if(class(addCovars) != "formula")
stop("addCovars argument needs to be of class formula")
if(class(placAdj) != "logical")
stop("placAdj argument needs to be of class logical")
if(placAdj){
if(type == "normal")
stop("\"placAdj == TRUE\" only allowed for type = \"general\"")
if(any(dose == 0))
stop("If placAdj == TRUE there should be no placebo group")
}
if(!is.null(data)){ # data handed over in data frame
if(!is.data.frame(data))
stop("data argument needs to be a data frame")
nams <- c(cal[2], cal[3], all.vars(addCovars))
ind <- match(nams, names(data))
if (any(is.na(ind)))
stop("variable(s): ", paste(nams[is.na(ind)], collapse= ", "), " not found in ", cal[4])
dd <- na.action(data[,nams])
} else { # data handed over via vectors
if(addCovars != ~1)
stop("need to hand over data and covariates in data frame")
if(length(dose) != length(resp))
stop(cal[2], " and ", cal[3], " not of equal length")
dd <- na.action(data.frame(dose, resp))
cal[2:3] <- gsub("\\$", "", cal[2:3])
cal[2:3] <- gsub("\\[|\\]", "", cal[2:3])
colnames(dd) <- cal[2:3]
}
doseNam <- cal[2];respNam <- cal[3]
if(any(dd[[doseNam]] < -.Machine$double.eps))
stop("dose values need to be non-negative")
if(!is.numeric(dd[[doseNam]]))
stop("dose variable needs to be numeric")
if(!is.numeric(dd[[respNam]]))
stop("response variable needs to be numeric")
## check type related arguments
if(type == "general" & is.null(S))
stop("S argument missing")
if(type == "normal" & !is.null(S))
message("Message: S argument ignored for type == \"normal\"\n")
if(type == "general" & addCovars != ~1)
message("Message: addCovars argument ignored for type == \"general\"")
if(!is.null(S)){
if(!is.matrix(S))
stop("S needs to be of class matrix")
nD <- length(dd[[doseNam]])
if(nrow(S) != nD | ncol(S) != nD)
stop("S and dose have non-confirming size")
}
ord <- order(dd[[doseNam]])
dd <- dd[ord, ]
Sout <- NULL
if(type == "general")
Sout <- S[ord, ord]
return(list(dd=dd, type = type, S = Sout, ord=ord,
doseNam=doseNam, respNam=respNam))
}
pValues <- function(contMat, corMat, df, tStat,
alternative = c("one.sided", "two.sided"),
control = mvtnorm.control()){
## function to calculate p-values
nD <- nrow(contMat)
nMod <- ncol(contMat)
if(missing(corMat))
stop("corMat needs to be specified")
if(missing(df))
stop("degrees of freedom need to be specified")
if(length(tStat) != nMod)
stop("tStat needs to have length equal to the number of models")
alternative <- match.arg(alternative)
ctrl <- mvtnorm.control()
if (!missing(control)) {
control <- as.list(control)
ctrl[names(control)] <- control
}
if(!is.finite(df)) # normal case
df <- 0
lower <- switch(alternative[1],
one.sided = matrix(rep(-Inf, nMod^2), nrow = nMod),
two.sided = matrix(rep(-tStat, each = nMod), nrow = nMod))
upper <- switch(alternative[1],
one.sided = matrix(rep(tStat, each = nMod), nrow = nMod),
two.sided = matrix(rep(tStat, each = nMod), nrow = nMod))
pVals <- numeric(nMod)
for(i in 1:nMod){
tmp <- 1 - pmvt(lower[,i], upper[,i], df = df,
corr = corMat, algorithm = ctrl)
pVals[i] <- tmp
if(attr(tmp,"msg") != "Normal Completion"){
warning(sprintf("Warning from mvtnorm::pmvt: %s.", attr(tmp, "msg")))
if(attr(tmp, "msg") == "Covariance matrix not positive semidefinite"){
warning("Setting calculated p-value to NA")
pVals[i] <- NA
}
}
}
pVals
}
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