imp_cat_single: The function to impute categorical variables
In matthiasspeidel/hmi: Hierarchical Multiple Imputation
Description
Usage
Arguments
Value
View source: R/hmi_imp_cat_single_2018-04-17.R
View source: R/hmi_imp_cat_single_2018-02-23.R
View source: R/hmi_imp_cat_single_2018-02-05.R
View source: R/hmi_imp_cat_single_2018-01-12.R
View source: R/hmi_imp_cat_single_2017-04-13.R
View source: R/hmi_imp_cat_single_2016-12-23.R
View source: R/hmi_imp_cat_single.R
Description
The function uses regression trees for imputation implemented in mice.
The principle is the following:
For each observation it is calculated at wich leave it would end.
Then one (randomly selected) observation of the other observations found on this leave,
functions as a donor.
Usage
1
imp_cat_single(y_imp_multi, X_imp_multi)
Arguments
y_imp_multi
A Vector with the variable to impute.
X_imp_multi
A data.frame with the fixed effects variables.
Value
A n x 1 data.frame
matthiasspeidel/hmi documentation built on Aug. 18, 2020, 4:37 p.m.
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Description Usage Arguments Value
View source: R/hmi_imp_cat_single_2018-04-17.R View source: R/hmi_imp_cat_single_2018-02-23.R View source: R/hmi_imp_cat_single_2018-02-05.R View source: R/hmi_imp_cat_single_2018-01-12.R View source: R/hmi_imp_cat_single_2017-04-13.R View source: R/hmi_imp_cat_single_2016-12-23.R View source: R/hmi_imp_cat_single.R
Description
The function uses regression trees for imputation implemented in mice.
The principle is the following:
For each observation it is calculated at wich leave it would end.
Then one (randomly selected) observation of the other observations found on this leave,
functions as a donor.
Usage
1 | imp_cat_single(y_imp_multi, X_imp_multi)
|
Arguments
y_imp_multi |
A Vector with the variable to impute. |
X_imp_multi |
A data.frame with the fixed effects variables. |
Value
A n x 1 data.frame
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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