miceadds (version 1.5-0)

micombine.cor: Combination of Correlations for Multiply Imputed Data Sets

Description

Statistical inference for correlation coefficients for multiply imputed datasets

Usage

micombine.cor(mi.res, variables = 1:(ncol(mi.list[[1]])), conf.level = 0.95,
     method="pearson")

Arguments

mi.res

Object of class mids or mids.1chain

variables

Indices of variables for selection

conf.level

Confidence level

method

Method for calculating correlations. Must be one of "pearson" or "spearman". The default is the calculation of the Pearson correlation.

Value

• A data frame containing the correlation coefficient (r) and its corresponding standard error (rse), fraction of missing information (fmi) and a $t$ value (t).

Examples

#############################################################################
# EXAMPLE 1: nhanes data | combination of correlation coefficients
#############################################################################

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

# nhanes data in one chain
imp.mi <- mice.1chain( nhanes , burnin=5 , iter=20 , Nimp=4 ,
        imputationMethod=rep("norm" , 4 ) )
# correlation coefficients of variables 4,2 and 3 (indexed in nhanes data)
res <- micombine.cor(mi.res=imp.mi, variables = c(4,2,3) )
  ##     variable1 variable2       r    rse fisher_r fisher_rse    fmi       t      p
  ##   1       chl       bmi  0.2458 0.2236   0.2510     0.2540 0.3246  0.9879 0.3232
  ##   2       chl       hyp  0.2286 0.2152   0.2327     0.2413 0.2377  0.9643 0.3349
  ##   3       bmi       hyp -0.0084 0.2198  -0.0084     0.2351 0.1904 -0.0358 0.9714
  ##     lower95 upper95
  ##   1 -0.2421  0.6345
  ##   2 -0.2358  0.6080
  ##   3 -0.4376  0.4239
  
#############################################################################
# EXAMPLE 2: nhanes data | comparing different correlation coefficients
#############################################################################  
  
library(psych)
library(mitools)

# imputing data
imp1 <- mice( nhanes ,  imputationMethod=rep("norm" , 4 ) )
summary(imp1)

#*** Pearson correlation
res1 <- micombine.cor(mi.res=imp1, variables = c(4,2) )

#*** Spearman rank correlation
res2 <- micombine.cor(mi.res=imp1, variables = c(4,2) ,  method="spearman")

#*** Kendalls tau
# test of computation of tau for first imputed dataset
dat1 <- complete(imp1, action=1)
tau1 <- psych::corr.test(x=dat1[,c(4,2)], method = "kendall")
tau1$r[1,2]    # estimate
tau1$se     # standard error

# results of Kendalls tau for all imputed datasets
res3 <- with( data=imp1 , 
        expr = psych::corr.test( x = cbind( chl , bmi ) , method="kendall") )
# extract estimates
betas <- lapply( res3$analyses , FUN = function(ll){ ll$r[1,2] } )
# extract variances
vars <- lapply( res3$analyses , FUN = function(ll){ ll$se^2 } )
# Rubin inference
tau_comb <- mitools::MIcombine( betas , vars )
summary(tau_comb)