R: Walking disability data

walking {mice} | R Documentation

## Walking disability data

### Description

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. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.

### 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)

[Package mice version 3.8.0 Index]