heplots (version 1.3-8)

WeightLoss: Weight Loss Data

Description

Contrived data on weight loss and self esteem over three months, for three groups of individuals: Control, Diet and Diet + Exercise. The data constitute a double-multivariate design.

Usage

data(WeightLoss)

Arguments

Format

A data frame with 34 observations on the following 7 variables.

group

a factor with levels Control Diet DietEx.

wl1

Weight loss at 1 month

wl2

Weight loss at 2 months

wl3

Weight loss at 3 months

se1

Self esteem at 1 month

se2

Self esteem at 2 months

se3

Self esteem at 3 months

Details

Helmert contrasts are assigned to group, comparing Control vs. (Diet DietEx) and Diet vs. DietEx.

References

Friendly, Michael (2010). HE Plots for Repeated Measures Designs. Journal of Statistical Software, 37(4), 1-40. URL https://www.jstatsoft.org/v37/i04/.

Examples

# NOT RUN {
data(WeightLoss)
str(WeightLoss)
table(WeightLoss$group)

contrasts(WeightLoss$group) <- matrix(c(-2,1,1, 0, -1, 1),ncol=2)
(wl.mod<-lm(cbind(wl1,wl2,wl3,se1,se2,se3)~group, data=WeightLoss))

heplot(wl.mod, hypotheses=c("group1", "group2"))
pairs(wl.mod, variables=1:3)
pairs(wl.mod, variables=4:6)

# within-S variables
within <- data.frame(measure=rep(c("Weight loss", "Self esteem"),each=3), month=rep(ordered(1:3),2))

# doubly-multivariate analysis: requires car 2.0+
# }
# NOT RUN {
if (packageDescription("car")[["Version"]] >= 2) {
imatrix <- matrix(c(
	1,0,-1, 1, 0, 0,
	1,0, 0,-2, 0, 0,
	1,0, 1, 1, 0, 0,
	0,1, 0, 0,-1, 1,
	0,1, 0, 0, 0,-2,
	0,1, 0, 0, 1, 1), 6, 6, byrow=TRUE)
# NB: for heplots the columns of imatrix should have names
colnames(imatrix) <- c("WL", "SE", "WL.L", "WL.Q", "SE.L", "SE.Q")
rownames(imatrix) <- colnames(WeightLoss)[-1]
(imatrix <- list(measure=imatrix[,1:2], month=imatrix[,3:6]))
contrasts(WeightLoss$group) <- matrix(c(-2,1,1, 0,-1,1), ncol=2) 
(wl.mod<-lm(cbind(wl1, wl2, wl3, se1, se2, se3)~group, data=WeightLoss))
(wl.aov <- Anova(wl.mod, imatrix=imatrix, test="Roy"))

heplot(wl.mod, imatrix=imatrix, iterm="group:measure")
}
# }
# NOT RUN {
# do the correct analysis 'manually'
unit <- function(n, prefix="") {
	J <-matrix(rep(1, n), ncol=1)
	rownames(J) <- paste(prefix, 1:n, sep="")
	J
}                

measure <- kronecker(diag(2), unit(3, 'M')/3, make.dimnames=TRUE)
colnames(measure)<- c('WL', 'SE')

between <- as.matrix(WeightLoss[,-1]) 
# }
# NOT RUN {
<!-- %*% measure -->
# }
# NOT RUN {
between.mod <- lm(between ~ group, data=WeightLoss)
Anova(between.mod)

heplot(between.mod, hypotheses=c("group1", "group2"), 
	xlab="Weight Loss", ylab="Self Esteem",
	col=c("red", "blue", "brown"),
	main="Weight Loss & Self Esteem: Group Effect")

month <- kronecker(diag(2), poly(1:3), make.dimnames=TRUE)
colnames(month)<- c('WL', 'SE')
trends <- as.matrix(WeightLoss[,-1]) 
# }
# NOT RUN {
<!-- %*% month -->
# }
# NOT RUN {
within.mod <- lm(trends ~ group, data=WeightLoss)
Anova(within.mod)

heplot(within.mod)
heplot(within.mod, hypotheses=c("group1", "group2"), 
	xlab="Weight Loss", ylab="Self Esteem",
	type="III", remove.intercept=FALSE,
	term.labels=c("month", "group:month"),
	main="Weight Loss & Self Esteem: Within-S Effects")
mark.H0()
# }