Hi,
I want to impute the missing values in my data set multiple times, and then
combine the results (like multiple imputation, but manually) to get a mean
of the parameter(s) from the multiple imputations. Does anyone know how to
do this?
I have the following script:
y1 <- rnorm(20,0,3)
y2 <- rnorm(20,3,3)
y3 <- rnorm(20,3,3)
y4 <- rnorm(20,6,3)
y <- c(y1,y2,y3,y4)
x1 <- 1+2*y1+ rnorm(20,0,8)
x2 <- 1+2*y2+ rnorm(20,0,8)
x3 <- 1+2*y3+ rnorm(20,0,8)
x4 <- 1+2*y4+ rnorm(20,0,8)
x <- c(x1,x2,x3,x4)
mcar.y <- rep(NA,80)
y.mis <- rep(NA,80)
df <- data.frame(y=y, y.mis=y.mis, mcar.y=mcar.y, x=x)
df$y.mis <- df$y
for (j in 1:80)
{
df$mcar.y <- rbinom(80,1,0.15)
}
ind0 <- which(df$mcar.y==0)
ind1 <- which(df$mcar.y==1)
if (length(ind0) > 68) {
df$mcar.y[sample(ind0, length(ind0) - 68)] <- 1
} else {
df$mcar.y[sample(ind1, 68 - length(ind0))] <- 0
}
df$y.mis[df$mcar.y==1] <- NA
This gives me data sets with missing values completely at random. Now I
would like to apply single imputation:
library(Hmisc)
lm.y <- lm(df$y.mis~df$x,data=df); lm.y
library(arm)
pred.y <- rnorm(length(df$y), predict (lm.y, df), sigma.hat(lm.y))
y.imp<- df$y.mis
impute <- function (y, y.impute)
{
ifelse (is.na(y), y.impute, y)
}
y.imp <- impute (y.imp, pred.y)
df <- data.frame(df$y, df$y.mis, pred.y, y.imp, x)
and repeat this imputation process a couple of times (say, 5 times) for each
data set. If I, however, have run this imputation-script (for 1 incomplete
data set), my data set is already complete. I would like to get back to the
incompleted data set used before, and repeat the single imputation process
four times with the same incomplete data set (so I can calculate some mean
of parameters from the 5 imputed data sets later on). But how?
Thanks.
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