midoc (version 1.0.0)

proposeMI: Suggests multiple imputation options

Description

Suggests the mice options to perform multiple imputation, based on the proposed set of imputation models (one for each partially observed variable) and specified dataset.

Usage

proposeMI(mimodobj, data, plot = TRUE, plotprompt = TRUE, message = TRUE)

Value

An object of type 'miprop', which can be used to run 'mice' using the proposed options, plus, optionally, a message and diagnostic plots describing the proposed 'mice' options

Arguments

mimodobj

An object, or list of objects, of type 'mimod', which stands for 'multiple imputation model', created by a call to checkModSpec

data

A data frame containing all the variables required for imputation and the substantive analysis

plot

If TRUE (the default), displays diagnostic plots for the proposed 'mice' call; use plot=FALSE to disable the plots

plotprompt

If TRUE (the default), the user is prompted before the second plot is displayed; use plotprompt=FALSE to remove the prompt

message

If TRUE (the default), displays a message describing the proposed 'mice' options; use message=FALSE to suppress the message

Examples

# First specify each 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)
mimod_pregsize <- checkModSpec(
                           formula="pregsize~bmi7+matage+I(matage^2)+mated",
                           family="binomial(logit)",
                           data=bmi,
                           message=FALSE)

# Display the proposed 'mice' options (suppressing the plot prompt)
## When specifying a single imputation model
proposeMI(mimodobj=mimod_bmi7,
          data=bmi,
          plotprompt = FALSE)
## When specifying more than one imputation model (suppressing the plots)
proposeMI(mimodobj=list(mimod_bmi7,mimod_pregsize),
          data=bmi,
          plot = FALSE)