mice.1chain: Multiple Imputation by Chained Equations using One Chain

In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

Multiple Imputation by Chained Equations using One Chain

Description

This function modifies the mice::mice function to multiply impute a dataset using a long chain instead of multiple parallel chains which is the approach employed in mice::mice.

Usage

mice.1chain(data, burnin=10, iter=20, Nimp=10, method=NULL,
     where=NULL, visitSequence=NULL, blots=NULL, post=NULL,
     defaultMethod=c("pmm", "logreg", "polyreg", "polr"),
     printFlag=TRUE, seed=NA, data.init=NULL, ...)

## S3 method for class 'mids.1chain'
summary(object,...)

## S3 method for class 'mids.1chain'
print(x, ...)

## S3 method for class 'mids.1chain'
plot(x, plot.burnin=FALSE, ask=TRUE, ...)

Arguments

data	Numeric matrix
burnin	Number of burn-in iterations
iter	Total number of imputations (larger than burnin)
Nimp	Number of imputations
method	See mice::mice
where	See mice::mice
visitSequence	See mice::mice
blots	See mice::mice
post	See mice::mice
defaultMethod	See mice::mice
printFlag	See mice::mice
seed	See mice::mice
data.init	See mice::mice
object	Object of class mids.1chain
x	Object of class mids.1chain
plot.burnin	An optional logical indicating whether burnin iterations should be included in the traceplot
ask	An optional logical indicating a user request for viewing next plot
...	See mice::mice

Value

A list with following entries

midsobj	Objects of class mids
datlist	List of multiply imputed datasets
datalong	Original and imputed dataset in the long format
implist	List of mids objects for every imputation
chainMpar	Trace of means for all imputed variables
chainVarpar	Trace of variances for all imputed variables

Note

Multiple imputation can also be used for determining causal effects (see Example 3; Schafer & Kang, 2008).

See Also

mice::mice

Examples

## Not run: 

#############################################################################
# EXAMPLE 1: One chain nhanes data
#############################################################################

library(mice)
data(nhanes, package="mice")
set.seed(9090)

# nhanes data in one chain
imp.mi1 <- miceadds::mice.1chain( nhanes, burnin=5, iter=40, Nimp=4,
                 method=rep("norm", 4 ) )
summary(imp.mi1)       # summary of mids.1chain
plot( imp.mi1 ) # trace plot excluding burnin iterations
plot( imp.mi1, plot.burnin=TRUE ) # trace plot including burnin iterations

# select mids object
imp.mi2 <- imp.mi1$midsobj
summary(imp.mi2)    # summary of mids

# apply mice functionality lm.mids
mod <- with( imp.mi2, stats::lm( bmi ~ age ) )
summary( mice::pool( mod ) )

#############################################################################
# EXAMPLE 2: One chain (mixed data: numeric and factor)
#############################################################################

library(mice)
data(nhanes2, package="mice")
set.seed(9090)

# nhanes2 data in one chain
imp.mi1 <- miceadds::mice.1chain( nhanes2, burnin=5, iter=25, Nimp=5 )
# summary
summary( imp.mi1$midsobj )

#############################################################################
# EXAMPLE 3: Multiple imputation with counterfactuals for estimating
#            causal effects (average treatment effects)
# Schafer, J. L., & Kang, J. (2008). Average causal effects from nonrandomized
#    studies: a practical guide and simulated example.
#    Psychological Methods, 13, 279-313.
#############################################################################

data(data.ma01)
dat <- data.ma01[, 4:11]

# define counterfactuals for reading score for students with and
# without migrational background
dat$read.migrant1 <- ifelse( paste(dat$migrant)==1, dat$read, NA )
dat$read.migrant0 <- ifelse( paste(dat$migrant)==0, dat$read, NA )

# define imputation method
impmethod <- rep("pls", ncol(dat) )
names(impmethod) <- colnames(dat)

# define predictor matrix
pm <- 4*(1 - diag( ncol(dat) ) )    # 4 - use all interactions
rownames(pm) <- colnames(pm) <- colnames(dat)
pm[ c( "read.migrant0", "read.migrant1"), ] <- 0
# do not use counterfactuals for 'read' as a predictor
pm[, "read.migrant0"] <- 0
pm[, "read.migrant1"] <- 0
# define control variables for creation of counterfactuals
pm[ c( "read.migrant0", "read.migrant1"), c("hisei","paredu","female","books") ] <- 4
  ##   > pm
  ##                 math read migrant books hisei paredu female urban read.migrant1 read.migrant0
  ##   math             0    4       4     4     4      4      4     4             0             0
  ##   read             4    0       4     4     4      4      4     4             0             0
  ##   migrant          4    4       0     4     4      4      4     4             0             0
  ##   books            4    4       4     0     4      4      4     4             0             0
  ##   hisei            4    4       4     4     0      4      4     4             0             0
  ##   paredu           4    4       4     4     4      0      4     4             0             0
  ##   female           4    4       4     4     4      4      0     4             0             0
  ##   urban            4    4       4     4     4      4      4     0             0             0
  ##   read.migrant1    0    0       0     4     4      4      4     0             0             0
  ##   read.migrant0    0    0       0     4     4      4      4     0             0             0

# imputation using mice function and PLS imputation with
# predictive mean matching method 'pmm6'
imp <- mice::mice( dat, method=impmethod, predictorMatrix=pm,
            maxit=4, m=5, pls.impMethod="pmm5" )

#*** Model 1: Raw score difference
mod1 <- with( imp, stats::lm( read ~ migrant ) )
smod1 <- summary( mice::pool(mod1) )
  ##   > smod1
  ##                  est    se      t     df Pr(>|t|)  lo 95  hi 95 nmis    fmi lambda
  ##   (Intercept) 510.21 1.460 349.37 358.26        0 507.34 513.09   NA 0.1053 0.1004
  ##   migrant     -43.38 3.757 -11.55  62.78        0 -50.89 -35.87  404 0.2726 0.2498

#*** Model 2: ANCOVA - regression adjustment
mod2 <- with( imp, stats::lm( read ~ migrant + hisei + paredu + female + books) )
smod2 <- summary( mice::pool(mod2) )
  ##   > smod2
  ##                    est      se      t      df  Pr(>|t|)    lo 95   hi 95 nmis      fmi   lambda
  ##   (Intercept) 385.1506 4.12027 93.477 3778.66 0.000e+00 377.0725 393.229   NA 0.008678 0.008153
  ##   migrant     -29.1899 3.30263 -8.838   87.46 9.237e-14 -35.7537 -22.626  404 0.228363 0.210917
  ##   hisei         0.9401 0.08749 10.745  160.51 0.000e+00   0.7673   1.113  733 0.164478 0.154132
  ##   paredu        2.9305 0.79081  3.706   41.34 6.190e-04   1.3338   4.527  672 0.339961 0.308780
  ##   female       38.1719 2.26499 16.853 1531.31 0.000e+00  33.7291  42.615    0 0.041093 0.039841
  ##   books        14.0113 0.88953 15.751  154.71 0.000e+00  12.2541  15.768  423 0.167812 0.157123

#*** Model 3a: Estimation using counterfactuals
mod3a <- with( imp, stats::lm( I( read.migrant1 - read.migrant0) ~ 1 ) )
smod3a <- summary( mice::pool(mod3a) )
  ##   > smod3a
  ##                  est    se      t    df Pr(>|t|)  lo 95  hi 95 nmis    fmi lambda
  ##   (Intercept) -22.54 7.498 -3.007 4.315  0.03602 -42.77 -2.311   NA 0.9652 0.9521

#*** Model 3b: Like Model 3a but using student weights
mod3b <- with( imp, stats::lm( I( read.migrant1 - read.migrant0) ~ 1,
                        weights=data.ma01$studwgt ) )
smod3b <- summary( mice::pool(mod3b) )
  ##   > smod3b
  ##                  est    se      t  df Pr(>|t|)  lo 95  hi 95 nmis    fmi lambda
  ##   (Intercept) -21.88 7.605 -2.877 4.3  0.04142 -42.43 -1.336   NA 0.9662 0.9535

#*** Model 4: Average treatment effect on the treated (ATT, migrants)
#             and non-treated (ATN, non-migrants)
mod4 <- with( imp, stats::lm( I( read.migrant1 - read.migrant0) ~ 0 + as.factor( migrant) )   )
smod4 <- summary( mice::pool(mod4) )
  ##   > smod4
  ##                          est    se      t    df Pr(>|t|)  lo 95   hi 95 nmis    fmi lambda
  ##   as.factor(migrant)0 -23.13 8.664 -2.669  4.27 0.052182 -46.59  0.3416   NA 0.9682 0.9562
  ##   as.factor(migrant)1 -19.95 5.198 -3.837 19.57 0.001063 -30.81 -9.0884   NA 0.4988 0.4501
# ATN=-23.13 and ATT=-19.95

## End(Not run)

miceadds documentation built on Jan. 7, 2023, 1:09 a.m.