*! version 1.0.0 Orsini N, Bottai M 28jun2016

Title

Impute missing data using quantiles

Syntax

mi impute quantile
ivar [indepvars] [if]
[weight]
[, impute_options]

You must mi set your data before using mi impute quantile;
see [MI] mi set.

You must mi register ivar as imputed before using mi
impute quantile; see [MI] mi set.

aweights, fweights, iweights, and pweights are
allowed; see weight.

Description

mi impute quantile fills in missing values of a
variable by using quantile imputation method. You can also account for analytic, frequency, importance,
and sampling weights.

Options

Main

add(), replace, rseed(), double; see
[MI] mi impute.

Reporting

dots, noisily, nolegend; see [MI] mi impute.
noisily specifies that the output from the linear regression fit to
the observed data be displayed.

Advanced

force; see [MI] mi impute.

The following option is available with mi impute but is not shown in the
dialog box:

noupdate; see [MI] noupdate option.

Examples

webuse mheart0, clear

mi set wide

mi register imputed bmi

mi impute quantile bmi = attack smokes age hsgrad female, add(1)

Authors

Nicola Orsini

Department of Public Health Sciences, Karolinska Institutet

Stockholm, Sweden

nicola.orsini@ki.se

Matteo Bottai

Unit of Biostatistics

Institute of Environmental Medicine, Karolinska Institutet

Stockholm, Sweden

matteo.bottai@ki.se

Stored results

mi impute quantile stores the following in r():

Scalars

r(M) total number of imputations

r(M_add) number of added imputations

r(M_update) number of updated imputations

r(knn) number of k nearest neighbors

r(k_ivars) number of imputed variables (always 1)

r(N_g) number of imputed groups (1 if by() is not specified)

Macros

r(method) name of imputation method (pmm)

r(ivars) names of imputation variables

r(rngstate) random-number state used

Matrices

r(N) number of observations in imputation sample in each group

r(N_complete) number of complete observations in imputation sample in each group

r(N_incomplete) number of incomplete observations in imputation sample in each group

r(N_imputed) number of imputed observations in imputation sample in each group