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### print methods
print.glht <- function(x, digits = max(3, getOption("digits") - 3), ...)
{
cat("\n\t", "General Linear Hypotheses\n\n")
if (!is.null(x$type))
cat("Multiple Comparisons of Means:", x$type, "Contrasts\n\n\n")
beta <- coef(x)
lh <- matrix(beta, ncol = 1)
colnames(lh) <- "Estimate"
alt <- switch(x$alternative,
"two.sided" = "==", "less" = ">=", "greater" = "<=")
rownames(lh) <- paste(names(beta), alt, x$rhs)
cat("Linear Hypotheses:\n")
print(lh, digits = digits)
cat("\n")
invisible(lh)
}
print.summary.glht <- function(x, digits = max(3, getOption("digits") - 3),
...)
{
cat("\n\t", "Simultaneous Tests for General Linear Hypotheses\n\n")
if (!is.null(x$type))
cat("Multiple Comparisons of Means:", x$type, "Contrasts\n\n\n")
cat("Fit: ")
if (inherits(x$model, "lmer")) {
print(x$model@call)
} else {
print(x$model$call)
}
cat("\n")
pq <- x$test
mtests <- cbind(pq$coefficients, pq$sigma, pq$tstat, pq$pvalues)
error <- attr(pq$pvalues, "error")
colnames(mtests) <- c("Estimate", "Std. Error",
ifelse(x$df == 0, "z value", "t value"), "p value")
type <- pq$type
### print p values according to simulation precision
if (!is.null(error) && error > .Machine$double.eps) {
sig <- which.min(abs(1 / error - (10^(1:10))))
sig <- 1 / (10^sig)
} else {
sig <- .Machine$double.eps
}
cat("Linear Hypotheses:\n")
alt <- switch(x$alternative,
"two.sided" = "==", "less" = ">=", "greater" = "<=")
rownames(mtests) <- paste(rownames(mtests), alt, x$rhs)
printCoefmat(mtests, digits = digits,
has.Pvalue = TRUE, P.values = TRUE, eps.Pvalue = sig)
switch(type,
"univariate" = cat("(Univariate p values reported)"),
"free" = cat("(Adjusted p values reported)"),
"Shaffer" = cat("(Adjusted p values reported -- Shaffer method)"),
"Westfall" = cat("(Adjusted p values reported -- Westfall method)"),
cat("(Adjusted p values reported --", type, "method)")
)
cat("\n\n")
invisible(x)
}
print.confint.glht <- function(x, digits = max(3, getOption("digits") - 3),
...)
{
cat("\n\t", "Simultaneous Confidence Intervals for General Linear Hypotheses\n\n")
if (!is.null(x$type))
cat("Multiple Comparisons of Means:", x$type, "Contrasts\n\n\n")
level <- attr(x$confint, "conf.level")
attr(x$confint, "conf.level") <- NULL
cat("Fit: ")
if (inherits(x$model, "lmer")) {
print(x$model@call)
} else {
print(x$model$call)
}
cat("\n")
error <- attr(x$confint, "error")
if (!is.null(error) && error > .Machine$double.eps)
digits <- min(digits, which.min(abs(1 / error - (10^(1:10)))))
cat("Estimated Quantile =", round(attr(x$confint, "calpha"), digits))
cat("\n\n")
cat("Linear Hypotheses:\n")
alt <- switch(x$alternative,
"two.sided" = "==", "less" = ">=", "greater" = "<=")
rownames(x$confint) <- paste(rownames(x$confint), alt, x$rhs)
print(format(x$confint, nsmall = digits, digits = digits), quote = FALSE)
cat("\n")
cat(paste(level * 100,
"% family-wise confidence level\n", sep = ""), "\n\n")
invisible(x)
}
print.contrMat <- function(x, digits = max(3, getOption("digits") - 3), ...) {
cat("\n\t", "Multiple Comparisons of Means:", attr(x, "type"), "Contrasts\n\n")
attr(x, "type") <- NULL
class(x) <- "matrix"
print(x, digits = digits)
invisible(x)
}
print.summary.glht.global <- function(x,
digits = max(3, getOption("digits") - 3), ...) {
print.glht(x, digits = digits)
cat("Global Test:\n")
if (x$test$type == "Chisq") {
pr <- data.frame(x$test$SSH, x$test$df[1], x$test$pval)
names(pr) <- c("Chisq", "DF", "Pr(>Chisq)")
}
if (x$test$type == "F") {
pr <- data.frame(x$test$fstat, x$test$df[1], x$test$df[2],
x$test$pval)
names(pr) <- c("F", "DF1", "DF2", "Pr(>F)")
}
print(pr, digits = digits)
invisible(x)
}
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