# boys: Growth of Dutch boys In mice: Multivariate Imputation by Chained Equations

## Description

Height, weight, head circumference and puberty of 748 Dutch boys.

## Format

A data frame with 748 rows on the following 9 variables:

age

Decimal age (0-21 years)

hgt

Height (cm)

wgt

Weight (kg)

bmi

Body mass index

hc

gen

Genital Tanner stage (G1-G5)

phb

Pubic hair (Tanner P1-P6)

tv

Testicular volume (ml)

reg

Region (north, east, west, south, city)

## Details

Random sample of 10% from the cross-sectional data used to construct the Dutch growth references 1997. Variables `gen` and `phb` are ordered factors. `reg` is a factor.

## Source

Fredriks, A.M,, van Buuren, S., Burgmeijer, R.J., Meulmeester JF, Beuker, R.J., Brugman, E., Roede, M.J., Verloove-Vanhorick, S.P., Wit, J.M. (2000) Continuing positive secular growth change in The Netherlands 1955-1997. Pediatric Research, 47, 316-323.

Fredriks, A.M., van Buuren, S., Wit, J.M., Verloove-Vanhorick, S.P. (2000). Body index measurements in 1996-7 compared with 1980. Archives of Disease in Childhood, 82, 107-112.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55``` ```# create two imputed data sets imp <- mice(boys, m = 1, maxit = 2) z <- complete(imp, 1) # create imputations for age <8yrs plot(z\$age, z\$gen, col = mdc(1:2)[1 + is.na(boys\$gen)], xlab = "Age (years)", ylab = "Tanner Stage Genital" ) # figure to show that the default imputation method does not impute BMI # consistently plot(z\$bmi, z\$wgt / (z\$hgt / 100)^2, col = mdc(1:2)[1 + is.na(boys\$bmi)], xlab = "Imputed BMI", ylab = "Calculated BMI" ) # also, BMI distributions are somewhat different oldpar <- par(mfrow = c(1, 2)) MASS::truehist(z\$bmi[!is.na(boys\$bmi)], h = 1, xlim = c(10, 30), ymax = 0.25, col = mdc(1), xlab = "BMI observed" ) MASS::truehist(z\$bmi[is.na(boys\$bmi)], h = 1, xlim = c(10, 30), ymax = 0.25, col = mdc(2), xlab = "BMI imputed" ) par(oldpar) # repair the inconsistency problem by passive imputation meth <- imp\$meth meth["bmi"] <- "~I(wgt/(hgt/100)^2)" pred <- imp\$predictorMatrix pred["hgt", "bmi"] <- 0 pred["wgt", "bmi"] <- 0 imp2 <- mice(boys, m = 1, maxit = 2, meth = meth, pred = pred) z2 <- complete(imp2, 1) # show that new imputations are consistent plot(z2\$bmi, z2\$wgt / (z2\$hgt / 100)^2, col = mdc(1:2)[1 + is.na(boys\$bmi)], ylab = "Calculated BMI" ) # and compare distributions oldpar <- par(mfrow = c(1, 2)) MASS::truehist(z2\$bmi[!is.na(boys\$bmi)], h = 1, xlim = c(10, 30), ymax = 0.25, col = mdc(1), xlab = "BMI observed" ) MASS::truehist(z2\$bmi[is.na(boys\$bmi)], h = 1, xlim = c(10, 30), ymax = 0.25, col = mdc(2), xlab = "BMI imputed" ) par(oldpar) ```

mice documentation built on Nov. 14, 2020, 5:07 p.m.