miceadds (version 1.5-0)

mice.1chain: 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 = vector("character", length = ncol(data)), 
   predictorMatrix = (1 - diag(1, ncol(data))), 
   visitSequence = (1:ncol(data))[apply(is.na(data), 2, any)], 
   form = vector("character", length = ncol(data)), 
   post = vector("character", length = ncol(data)), 
   defaultMethod = c("pmm", "logreg", "polyreg", "polr"), 
   diagnostics = TRUE, printFlag = TRUE, seed = NA, imputationMethod = NULL, 
   defaultImputationMethod = NULL, data.init = NULL, ...)
   
## S3 method for class 'mids.1chain':
summary(object,...)

## 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

predictorMatrix

See mice::mice

visitSequence

See mice::mice

form

See mice::mice

post

See mice::mice

defaultMethod

See mice::mice

diagnostics

See mice::mice

printFlag

See mice::mice

seed

See mice::mice

imputationMethod

See mice::mice

defaultImputationMethod

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

See Also

mice::mice

Examples

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

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

# nhanes data in one chain
imp.mi1 <- mice.1chain( nhanes , burnin=5 , iter=40 , Nimp=4 ,
    imputationMethod=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 , lm( bmi ~ age ) )
summary( 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 <- 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("2l.pls2" , 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( dat , imputationMethod=impmethod , predictorMatrix=pm ,
            maxit=4 , m=5 , pls.impMethod="pmm5" )

#*** Model 1: Raw score difference
mod1 <- with( imp , lm( read ~ migrant ) )
smod1 <- summary( 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 , lm( read ~ migrant + hisei + paredu + female + books) )
smod2 <- summary( 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 , lm( I( read.migrant1 - read.migrant0) ~ 1 ) )
smod3a <- summary( 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 , lm( I( read.migrant1 - read.migrant0) ~ 1 , weights= data.ma01$studwgt ) )
smod3b <- summary( 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 , lm( I( read.migrant1 - read.migrant0) ~ 0 + as.factor( migrant) )   )
smod4 <- summary( 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