```
set.seed(1)
x1 <- rnorm(200)
x2 <- rnorm(200)
x3 <- x1 + x2 + rnorm(200)
x4 <- x2 + rnorm(200)
x <- cbind(x1,x2,x3,x4)
v <- varclus(x, similarity="spear") # spearman is the default anyway
v # invokes print.varclus
print(round(v$sim,2))
plot(v)
# plot(varclus(~ age + sys.bp + dias.bp + country - 1), abbrev=TRUE)
# the -1 causes k dummies to be generated for k countries
# plot(varclus(~ age + factor(disease.code) - 1))
#
#
# use varclus(~., data= fracmiss= maxlevels= minprev=) to analyze all
# "useful" variables - see dataframeReduce for details about arguments
df <- data.frame(a=c(1,2,3),b=c(1,2,3),c=c(1,2,NA),d=c(1,NA,3),
e=c(1,NA,3),f=c(NA,NA,NA),g=c(NA,2,3),h=c(NA,NA,3))
par(mfrow=c(2,2))
for(m in c("ward","complete","median")) {
plot(naclus(df, method=m))
title(m)
}
naplot(naclus(df))
n <- naclus(df)
plot(n); naplot(n)
na.pattern(df)
# plotMultSim example: Plot proportion of observations
# for which two variables are both positive (diagonals
# show the proportion of observations for which the
# one variable is positive). Chance-correct the
# off-diagonals by subtracting the product of the
# marginal proportions. On each subplot the x-axis
# shows month (0, 4, 8, 12) and there is a separate
# curve for females and males
d <- data.frame(sex=sample(c('female','male'),1000,TRUE),
month=sample(c(0,4,8,12),1000,TRUE),
x1=sample(0:1,1000,TRUE),
x2=sample(0:1,1000,TRUE),
x3=sample(0:1,1000,TRUE))
s <- array(NA, c(3,3,4))
opar <- par(mar=c(0,0,4.1,0)) # waste less space
for(sx in c('female','male')) {
for(i in 1:4) {
mon <- (i-1)*4
s[,,i] <- varclus(~x1 + x2 + x3, sim='ccbothpos', data=d,
subset=d$month==mon & d$sex==sx)$sim
}
plotMultSim(s, c(0,4,8,12), vname=c('x1','x2','x3'),
add=sx=='male', slimds=TRUE,
lty=1+(sx=='male'))
# slimds=TRUE causes separate scaling for diagonals and
# off-diagonals
}
par(opar)
```

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