# NOT RUN {
##################################################
# simulate some data
# x,y ... level 1 variables
# v,w ... level 2 variables
G <- 250 # number of groups
n <- 20 # number of persons
beta <- .3 # regression coefficient
rho <- .30 # residual intraclass correlation
rho.miss <- .10 # correlation with missing response
missrate <- .50 # missing proportion
y1 <- rep( rnorm( G , sd = sqrt( rho ) ) , each=n ) + rnorm(G*n , sd = sqrt( 1 - rho ))
w <- rep( round( rnorm(G ) , 2 ) , each=n )
v <- rep( round( runif( G , 0 , 3 ) ) , each=n )
x <- rnorm( G*n )
y <- y1 + beta * x + .2 * w + .1 * v
dfr0 <- dfr <- data.frame( "group" = rep(1:G , each=n ) , "x" = x , "y" = y , "w" = w , "v" = v )
dfr[ rho.miss * x + rnorm( G*n , sd = sqrt( 1 - rho.miss ) ) < qnorm( missrate ) , "y" ] <- NA
dfr[ rep( rnorm(G) , each=n ) < qnorm( missrate ) , "w" ] <- NA
dfr[ rep( rnorm(G) , each=n ) < qnorm( missrate ) , "v" ] <- NA
#....
# empty mice imputation
imp0 <- mice( as.matrix(dfr) , maxit=0 )
predM <- imp0$predictorMatrix
impM <- imp0$method
#...
# multilevel imputation
predM1 <- predM
predM1[c("w","y","v"),"group"] <- -2
predM1["y","x"] <- 1 # fixed x effects imputation
impM1 <- impM
impM1[c("y","w","v")] <- c("2l.pan" , "2lonly.norm" , "2lonly.pmm" )
# y ... imputation using pan
# w ... imputation at level 2 using norm
# v ... imputation at level 2 using pmm
imp1 <- mice( as.matrix( dfr ) , m = 1 , predictorMatrix = predM1 ,
method = impM1 , maxit=1 , paniter=500)
#
# Demonstration that 2lonly.norm aborts for partial missing data.
# Better use 2lonly.mean for repair.
data <- data.frame(patid = rep(1:4, each = 5),
sex = rep(c(1, 2, 1, 2), each = 5),
crp = c(68, 78, 93, NA, 143,
5, 7, 9, 13, NA,
97, NA, 56, 52, 34,
22, 30, NA, NA, 45))
pred <- make.predictorMatrix(data)
pred[, "patid"] <- -2
# only missing value (out of five) for patid == 1
data[3, "sex"] <- NA
# }
# NOT RUN {
# The following fails because 2lonly.norm found partially missing
# level-2 data
# imp <- mice(data, method = c("", "2lonly.norm", "2l.pan"),
# predictorMatrix = pred, maxit = 1, m = 2)
# > iter imp variable
# > 1 1 sex crpError in .imputation.level2(y = y, ... :
# > Method 2lonly.norm found the following clusters with partially missing
#> level-2 data: 1
#> Method 2lonly.mean can fix such inconsistencies.
# }
# NOT RUN {
# In contrast, if all sex values are missing for patid == 1, it runs fine,
# except on r-patched-solaris-x86. I used dontrun to evade CRAN errors.
# }
# NOT RUN {
data[1:5, "sex"] <- NA
imp <- mice(data, method = c("", "2lonly.norm", "2l.pan"),
predictorMatrix = pred, maxit = 1, m = 2)
# }
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