Nothing
attach(NULL, name = "CheckExEnv")
assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
## This plot.new() patch has no effect yet for persp();
## layout() & filled.contour() are now ok
assign("plot.new", function() { .Internal(plot.new())
pp <- par(c("mfg","mfcol","oma","mar"))
if(all(pp$mfg[1:2] == c(1, pp$mfcol[2]))) {
outer <- (oma4 <- pp$oma[4]) > 0; mar4 <- pp$mar[4]
mtext(paste("help(",..nameEx,")"), side = 4,
line = if(outer)max(1, oma4 - 1) else min(1, mar4 - 1),
outer = outer, adj=1, cex= .8, col="orchid")} },
env = .CheckExEnv)
assign("cleanEx", function(env = .GlobalEnv) {
rm(list = ls(envir = env, all.names = TRUE), envir = env)
RNGkind("Wichmann-Hill", "Kinderman-Ramage")
set.seed(290875)
# assign(".Random.seed", c(0,rep(7654,3)), pos=1)
},
env = .CheckExEnv)
assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) #-- for now
assign("ptime", proc.time(), env = .CheckExEnv)
postscript("multcomp-Examples.ps")
assign("par.postscript", par(no.readonly = TRUE), env = .CheckExEnv)
options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
library('multcomp')
cleanEx(); ..nameEx <- "MultipleEndpoints"
###--- >>> `MultipleEndpoints' <<<----- Multiple Endpoints Data Set
## alias help(MultipleEndpoints)
##___ Examples ___:
cleanEx(); ..nameEx <- "angina"
###--- >>> `angina' <<<----- Dose Response Data Set
## alias help(angina)
##___ Examples ___:
load("angina.rda")
# perform a dose-response analysis using simultaneous confidence
# intervals for Willimas' contrasts
summary(simint(response~dose, data=angina, alternative="greater",
type="Williams"))
# compute now adjusted p-values for McDermott's test on trend
summary(simtest(response~dose, data=angina, type="McDermott",
alternative="greater",ttype="logical"))
## Keywords: 'datasets'.
cleanEx(); ..nameEx <- "cholesterol"
### * cholesterol
### Name: cholesterol
### Title: Cholesterol Reduction Data Set
### Aliases: cholesterol
### Keywords: datasets
### ** Examples
data(cholesterol)
# adjusted p-values for all-pairwise comparisons in a one-way layout
# tests for restricted combinations
simtest(response ~ trt, data=cholesterol, type="Tukey",
ttype="logical")
# adjusted p-values all-pairwise comparisons in a one-way layout
# (tests for free combinations -> p-values will be larger)
simtest(response ~ trt, data=cholesterol, type="Tukey",
ttype="free")
# the following lines illustrate the basic principles of
# parameter estimation used in all functions in this package
# and how the low-level functions can be used with raw parameter
# estimates.
# the full design matrix (with reduced rank!)
x <- cbind(1,
matrix(c(rep(c(rep(1,10), rep(0,50)), 4),
rep(1, 10)), nrow = 50))
y <- cholesterol$response
xpxi <- multcomp:::MPinv(t(x) %*% x)$MPinv
rankx <- sum(diag((xpxi %*% (t(x) %*% x))))
n <- nrow(x)
p <- ncol(x)
df <- round(n-rankx)
# parameter estimates and their correlation
parm <- xpxi %*% t(x) %*% y
mse <- t(y-x %*% parm) %*% (y-x %*% parm)/df
covm <- mse[1,1]*xpxi
# the contrast matrix
contrast <- contrMat(table(cholesterol$trt), type="Tukey")
# use the work-horse directly (and add zero column for the intercept)
csimint(estpar=parm, df=df, covm=covm, cmatrix=cbind(0, contrast))
csimtest(estpar=parm, df=df, covm=covm, cmatrix=cbind(0, contrast),
ttype="logical")
cleanEx(); ..nameEx <- "contrMat"
## Keywords: 'datasets'.
data(detergent)
N <- rep(2, 5)
# BIBD: prepare the contrast matrix = all-pair comparisons for
# the 5 levels of detergent
C <- contrMat(N, type="Tukey")
# the additional 10 columns of are for the 10 blocks
C <- cbind( matrix(0, ncol=10, nrow=10), C )
# numerate the contrasts
colnames(C) <- NULL
rownames(C) <- paste("C", 1:nrow(C), sep="")
# adjusted p-values
summary(simtest(plates ~ block+detergent, data=detergent,
cmatrix = list(detergent = contrMat(table(detergent$detergent), type = "Tukey"))))
# whichf="detergent", type="Tukey", ttype="logical")) # , cmatrix=C))
## Keywords: 'datasets'.
cleanEx(); ..nameEx <- "recovery"
###--- >>> `recovery' <<<----- Recovery Time Data Set
## alias help(recovery)
##___ Examples ___:
data(recovery)
# one-sided simultaneous confidence intervals for Dunnett
# in the one-way layout
simint(minutes~blanket, data=recovery, conf.level=0.9,
alternative="less",eps=0.0001)
# same results, but specifying the contrast matrix by hand
C <- c(0, 0, 0, -1, -1, -1, 1, 0, 0, 0, 1, 0, 0, 0, 1)
C <- matrix(C, ncol=5)
# numerate the contrasts
rownames(C) <- paste("C", 1:nrow(C), sep="")
test <- simint(minutes~blanket, data=recovery, conf.level=0.9,
alternative="less",eps=0.0001, cmatrix=C[,-1])
print(test)
# same results, but more detailed information using the summary method
summary(test)
## Keywords: 'datasets'.
cleanEx(); ..nameEx <- "simint"
###--- >>> `simint' <<<----- Simultaneous Intervals
## alias help(simint)
## alias help(simint.default)
## alias help(simint.formula)
##___ Examples ___:
data(recovery)
# one-sided simultaneous confidence intervals for Dunnett
# in the one-way layout
summary(simint(minutes~blanket, data=recovery, type="Dunnett", conf.level=0.9,
alternative="less",eps=0.0001))
## Keywords: 'htest'.
cleanEx(); ..nameEx <- "simtest"
###--- >>> `simtest' <<<----- Simultaneous comparisons
## alias help(simtest.default)
## alias help(simtest.formula)
## alias help(simtest)
##___ Examples ___:
data(cholesterol)
# adjusted p-values for all-pairwise comparisons in a onw-way
# layout (tests for restricted combinations)
simtest(response ~ trt, data=cholesterol, type="Tukey", ttype="logical")
## Keywords: 'htest'.
cleanEx(); ..nameEx <- "tire"
###--- >>> `tire' <<<----- Tire Wear Data Set
## alias help(tire)
##___ Examples ___:
#tire <- read.csv("tire.csv", header = TRUE)
#C <- c(0,1,-1,0,10,-10)
#for ( x in seq(15,70,5) ) { C <- rbind( C,c(0,1,-1,0,x,-x) ) }
## numerate the contrasts
#rownames(C) <- paste("C", 1:nrow(C), sep="")
#
## simultaneous confidence intervals of two regression functions
#summary(simint(cost ~ make + mph + make:mph, data=tire,
# cmatrix=C, eps=0.001, whichf = NULL))
## Keywords: 'datasets'.
cleanEx(); ..nameEx <- "waste"
###--- >>> `waste' <<<----- Industrial Waste Data Set
## alias help(waste)
##___ Examples ___:
data(waste)
summary(aov(waste ~ envir + temp + envir*temp, data=waste))
#summary(simint(waste ~ envir:temp, data=waste,
# type="Tetrade", eps = 0.01))
## Keywords: 'datasets'.
cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
dev.off(); quit('no')
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