Stata 15 help for mi impute pmm

[MI] mi impute pmm -- Impute using predictive mean matching


mi impute pmm ivar [indepvars] [if] [weight] , knn(#) [impute_options options]

options Description ------------------------------------------------------------------------- Main noconstant suppress constant term * knn(#) specify # of closest observations (nearest neighbors) to draw from conditional(if) perform conditional imputation bootstrap estimate model parameters using sampling with replacement ------------------------------------------------------------------------- * knn(#) is required. You must mi set your data before using mi impute pmm; see [MI] mi set. You must mi register ivar as imputed before using mi impute pmm; see [MI] mi set. indepvars may contain factor variables; see fvvarlist. aweights, fweights, iweights, and pweights are allowed; see weight.


Statistics > Multiple imputation


mi impute pmm fills in missing values of a continuous variable by using the predictive mean matching imputation method. You can perform separate imputations on different subsets of the data by specifying the by() option. You can also account for analytic, frequency, importance, and sampling weights.


+------+ ----+ Main +-------------------------------------------------------------

noconstant; see [R] estimation options.

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

knn(#) specifies the number of closest observations (nearest neighbors) from which to draw imputed values. This option is required. The closeness is determined based on the absolute difference between the linear prediction for the missing value and that for the complete values. The closest observation is the observation with the smallest difference. This option regulates the correlation among multiple imputations that affects the bias and the variability of the resulting multiple-imputation point estimates; see Remarks and examples in [MI] mi impute pmm for details.

conditional(if) specifies that the imputation variable be imputed conditionally on observations satisfying exp. That is, missing values in a conditional sample, the sample identified by the exp expression, are imputed based only on data in that conditional sample. Missing values outside the conditional sample are replaced with a conditional constant, the value of the imputation variable in observations outside the conditional sample. As such, the imputation variable is required to be constant outside the conditional sample. Also, if any conditioning variables (variables involved in the conditional specification if exp) contain soft missing values (.), their missing values must be nested within missing values of the imputation variable. See Conditional imputation under Remarks and examples in [MI] mi impute.

bootstrap specifies that posterior estimates of model parameters be obtained using sampling with replacement; that is, posterior estimates are estimated from a bootstrap sample. The default is to sample the estimates from the posterior distribution of model parameters or from the large-sample normal approximation of the posterior distribution. This option is useful when asymptotic normality of parameter estimates is suspect.

+-----------+ ----+ Reporting +--------------------------------------------------------

dots, noisily, nolegend; see [MI] mi impute. noisily specifies that the output from the linear regression fit to the observed data be displayed. nolegend suppresses all legends that appear before the imputation table. Such legends include a legend about conditional imputation that appears when the conditional() option is specified and group legends that may appear when the by() option is specified.

+----------+ ----+ 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.


Setup . webuse mheart0

Declare data and register variable bmi as imputed . mi set mlong . mi register imputed bmi

Impute bmi using predictive mean matching using 5 nearest neighbors . mi impute pmm bmi attack smokes age hsgrad female, add(20) knn(5)

Impute bmi using 10 nearest neighbors . mi impute pmm bmi attack smokes age hsgrad female, replace knn(10)

Video example

Multiple imputation: Setup, imputation, estimation -- predictive mean matching

Stored results

mi impute pmm 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 r(by) names of variables specified within by()

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

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