Performs multiple imputation

Creates multiple imputations using mice, based on the options and dataset specified by a call to proposeMI. If stratification variable(s) are included in the 'miprop' object, multiple imputation will be performed for each subset of the data determined by the values of the stratification variable(s) and the resulting imputed datasets will be combined. If a substantive model is specified, the pooled estimates are calculated using pool.

Usage

doMImice(mipropobj, seed, substmod = " ", message = TRUE)

Arguments

mipropobj

An object of type 'miprop', created by a call to 'proposeMI'

seed

An integer that is used to set the seed of the 'mice' call

substmod

Optionally, a symbolic description of the substantive model to be fitted, specified as a string; if supplied, the model will be fitted to each imputed dataset and the results pooled

message

If TRUE (the default), displays a message summarising the analysis that has been performed; use message = FALSE to suppress the message

Value

A 'mice' object of class 'mids' (the multiply imputed datasets). Optionally, a message summarising the analysis that has been performed.

Examples

# First specify the imputation model as a 'mimod' object
## (suppressing the message)
mimod_bmi7 <- checkModSpec(formula="bmi7~matage+I(matage^2)+mated+pregsize",
                           family="gaussian(identity)",
                           data=bmi,
                           message=FALSE)

# Save the proposed 'mice' options as a 'miprop' object
## (suppressing the message)
miprop <- proposeMI(mimodobj=mimod_bmi7,
                    data=bmi,
                    message=FALSE,
                    plot = FALSE)
# Create the set of imputed datasets using the proposed 'mice' options
imp <- doMImice(miprop,123)
#> Now you have created your multiply imputed datasets, you can perform
#> your analysis and pool the results using the 'mice' functions 'with()'
#> and 'pool()'

# Additionally, fit the substantive model to each imputed dataset and display
## the pooled results
doMImice(miprop, 123, substmod="lm(bmi7 ~ matage + I(matage^2) + mated)")
#> Given the substantive model: lm(bmi7 ~ matage + I(matage^2) + mated) ,
#> multiple imputation estimates are as follows:
#> 
#>          term   estimate  std.error  statistic       df       p.value
#> 
#> 1 (Intercept) 17.6607324 0.07126548 247.816079 233.1668 2.116834e-284
#> 
#> 2      matage  1.1504545 0.05230345  21.995769 184.5081  1.863532e-53
#> 
#> 3 I(matage^2)  0.8414975 0.03231752  26.038433 257.1270  4.754845e-74
#> 
#> 4      mated1 -1.0026194 0.10787751  -9.294054 159.1101  1.094881e-16
#> 
#>        2.5 %     97.5 %   conf.low  conf.high
#> 
#> 1 17.5203258 17.8011389 17.5203258 17.8011389
#> 
#> 2  1.0472648  1.2536442  1.0472648  1.2536442
#> 
#> 3  0.7778567  0.9051382  0.7778567  0.9051382
#> 
#> 4 -1.2156760 -0.7895629 -1.2156760 -0.7895629