Two items YA and YB measuring walking disability in samples A, B and E.
Format
A data frame with 890 rows on the following 5 variables:
sex
Sex of respondent (factor)
age
Age of respondent
YA
Item administered in samples A and E (factor)
YB
Item administered in samples B and E (factor)
src
Source: Sample A, B or E (factor)
Details
Example dataset to demonstrate imputation of two items (YA and YB). Item YA
is administered to sample A and sample E, item YB is administered to sample B
and sample E, so sample E acts as a bridge study. Imputation using a bridge
study is better than simple equating or than imputation under independence.
Item YA corresponds to the HAQ8 item, and item YB corresponds to the GAR9
items from Van Buuren et al (2005). Sample E (as well as sample B) is the
Euridiss study (n=292), sample A is the ERGOPLUS study (n=306).
See Van Buuren (2012) chapter 7 for more details on the imputation
methodology.
References
van Buuren, S., Eyres, S., Tennant, A., Hopman-Rock, M. (2005).
Improving comparability of existing data by Response Conversion.
Journal of Official Statistics, 21(1), 53-72.
van Buuren, S. (2012). Flexible Imputation of Missing Data. Boca
Raton, FL: Chapman & Hall/CRC.
Examples
md.pattern(walking)
micemill <- function(n) {
for (i in 1:n) {
imp <<- mice.mids(imp) # global assignment
cors <- with(imp, cor(as.numeric(YA),
as.numeric(YB),
method="kendall"))
tau <<- rbind(tau, getfit(cors, s=TRUE)) # global assignment
}
}
plotit <- function()
matplot(x=1:nrow(tau),y=tau,
ylab=expression(paste("Kendall's ",tau)),
xlab="Iteration", type="l", lwd=1,
lty=1:10,col="black")
tau <- NULL
imp <- mice(walking, max=0, m=10, seed=92786)
pred <- imp$pred
pred[,c("src","age","sex")] <- 0
imp <- mice(walking, max=0, m=3, seed=92786, pred=pred)
micemill(5)
plotit()
### to get figure 7.8 van Buuren (2012) use m=10 and micemill(20)