Beta
Table of Contents
The outline of your notebook will show up here. You can include headings in any text cell by starting a line with #
, ##
, ###
, etc., depending on the desired title hierarchy.
Note that this notebook was automatically generated from an RDocumentation page. It depends on the package and the example code whether this code will run without errors. You may need to edit the code to make things work.
if(!require('Hmisc')) {
install.packages('Hmisc')
library('Hmisc')
}
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$sim,2))
plot(v)
# plot(varclus(~ age + sys.bp + dias.bp + country - 1), abbrev=TRUE)
# the -1 causes k dummies to be generated for k countries
# plot(varclus(~ age + factor(disease.code) - 1))
#
#
# use varclus(~., data= fracmiss= maxlevels= minprev=) to analyze all
# "useful" variables - see dataframeReduce for details about arguments
df <- data.frame(a=c(1,2,3),b=c(1,2,3),c=c(1,2,NA),d=c(1,NA,3),
e=c(1,NA,3),f=c(NA,NA,NA),g=c(NA,2,3),h=c(NA,NA,3))
par(mfrow=c(2,2))
for(m in c("ward","complete","median")) {
plot(naclus(df, method=m))
title(m)
}
naplot(naclus(df))
n <- naclus(df)
plot(n); naplot(n)
na.pattern(df)
x <- c(1, rep(2,11), rep(3,9))
combine.levels(x)
x <- c(1, 2, rep(3,20))
combine.levels(x)
# plotMultSim example: Plot proportion of observations
# for which two variables are both positive (diagonals
# show the proportion of observations for which the
# one variable is positive). Chance-correct the
# off-diagonals by subtracting the product of the
# marginal proportions. On each subplot the x-axis
# shows month (0, 4, 8, 12) and there is a separate
# curve for females and males
d <- data.frame(sex=sample(c('female','male'),1000,TRUE),
month=sample(c(0,4,8,12),1000,TRUE),
x1=sample(0:1,1000,TRUE),
x2=sample(0:1,1000,TRUE),
x3=sample(0:1,1000,TRUE))
s <- array(NA, c(3,3,4))
opar <- par(mar=c(0,0,4.1,0)) # waste less space
for(sx in c('female','male')) {
for(i in 1:4) {
mon <- (i-1)*4
s[,,i] <- varclus(~x1 + x2 + x3, sim='ccbothpos', data=d,
subset=d$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)