miceadds (version 2.2-0)

pool_mi: Statistical Inference for Multiply Imputed Datasets

Description

Statistical inference for multiply imputed datasets. See mitools::MIcombine or mice::pool for functions of the same functionality.

Usage

pool_mi(qhat, u = NULL, se = NULL, dfcom = 1e+07, method = "smallsample")
"summary"(object, alpha=0.05, ...)
"coef"(object, ...)
"vcov"(object, ...)

Arguments

qhat

List of parameter vectors

u

List of covariance matrices

se

List of vector of standard errors. Either u or se must be provided.

dfcom

Degrees of freedom of statistical analysis

method

The default is the small sample inference ("smallsample"). Any other input provides large sample inference.

object

Object of class pool_mi

alpha

Confidence level

...

Further arguments to be passed

Value

Object of with similar output as produced by the mice::pool function.

See Also

mitools::MIcombine, mice::pool, mitml::testEstimates

For statistical inference for nested multiply imputed datasets see NMIcombine.

Examples

## Not run: 
# #############################################################################
# # EXAMPLE 1: Statistical inference for models based on imputationList
# #############################################################################
# 
# library(mitools)
# library(mice)
# library(Zelig)
# library(mitml)
# library(lavaan)
# library(semTools)
# data(data.ma02)
# 
# # save dataset as imputation list
# imp <- mitools::imputationList( data.ma02 )
# # mids object
# imp0 <- datlist2mids( imp )
# # datlist object
# imp1 <- datlist_create(data.ma02)
# 
# #--- apply linear model based on imputationList
# mod <- with( imp , stats::lm( read ~ hisei + female ) )
# #--- apply linear model for mids object
# mod0 <- with( imp0 , stats::lm( read ~ hisei + female ) )
# # extract coefficients
# cmod <- mitools::MIextract( mod , fun = coef)
# # extract standard errors
# semod <- lapply( mod , FUN = function(mm){
#     smm <- summary(mm)
#     smm$coef[,"Std. Error"]
# } )
# # extract covariance matrix
# vmod <- mitools::MIextract( mod , fun = vcov)
# 
# #*** pooling based on covariance matrices
# res1 <- pool_mi( qhat=cmod , u = vmod )
# summary(res1)
# coef(res1)
# vcov(res1)
# 
# #*** pooling based on standard errors
# res2 <- pool_mi( qhat=cmod , se = semod )
# 
# #*** pooling with MIcombine
# res3 <- mitools::MIcombine( results=cmod , variances=vmod )
# 
# #*** pooling with pool function in mice
# res4 <- mice::pool( mod0 )
# 
# #*** analysis in Zelig
# # convert datalist into object of class amelia
# mi02 <- list( "imputations"=data.ma02)
# class(mi02) <- "amelia"
# res5 <- Zelig::zelig( read ~ hisei + female , model="ls" , data= mi02 )
# 
# #*** analysis in lavaan
# lavmodel <- "
#      read ~ hisei + female
#      read ~~ a*read
#      read ~ 1
#      # residual standard deviation
#      sde := sqrt(a)
#        "
# # analysis for first imputed dataset
# mod6a <- lavaan::sem( lavmodel , data = imp1[[1]] )
# summary(mod6a)
# # analysis based on all datasets using with
# mod6b <- lapply( imp1 , FUN = function(data){
#            res <- lavaan::sem( lavmodel , data = data ) 
#            return(res)
#                 } )
# # extract parameters and covariance matrices
# qhat0 <- lapply( mod6b , FUN = function(ll){  coef(ll) } )
# u0 <- lapply( mod6b , FUN = function(ll){  vcov(ll) } )
# res6b <- mitools::MIcombine( results = qhat0 , variances = u0 )
# 
# # extract informations for all parameters
# qhat <- lapply( mod6b , FUN = function(ll){
#         h1 <- lavaan::parameterEstimates(ll)
#         parnames <- paste0( h1$lhs , h1$op , h1$rhs )
#         v1 <- h1$est
#         names(v1) <- parnames
#         return(v1)    
#      } )
# se <- lapply( mod6b , FUN = function(ll){
#         h1 <- lavaan::parameterEstimates(ll)
#         parnames <- paste0( h1$lhs , h1$op , h1$rhs )
#         v1 <- h1$se
#         names(v1) <- parnames
#         return(v1)    
#      } ) 
# res6c <- pool_mi( qhat = qhat , se = se )     
# 
# # function runMI in semTools package
# res6d <- semTools::runMI(model=lavmodel , data = imp1 , m = length(imp1) )
#   # semTools version 0.4-9 provided an error message
# # perform inference with mitml package
# se2 <- lapply( se , FUN = function(ss){  ss^2  } )  # input variances
# res6e <- mitml::testEstimates(qhat=qhat, uhat=se2)
#          
# #*** complete model estimation and inference in mitml
# 
# # convert into object of class mitml.list
# ml02 <- mitml::as.mitml.list( data.ma02)    
# # estimate regression
# mod7 <- with( ml02 , stats::lm( read ~ hisei + female ) ) 
# # inference
# res7 <- mitml::testEstimates( mod7 )
#             
# #*** model comparison
# summary(res1)
# summary(res2)
# summary(res3)
# summary(res4)
# summary(res5)
# summary(res6b)
# summary(res6c)
# print(res6e)
# print(res7)
# ## End(Not run)