add_propensity_weights: Calculates inverse probability of treatment weights using a...
In MethodsConsultants/tibbletest: Clean Descriptives Tables with Significance Tests by Group
Description
Usage
Arguments
Value
View source: R/propensity-weighting.R
Description
Calculates inverse probability of treatment weights using a random forest.
Usage
1
add_propensity_weights(df, treatment, ivs, impute_missing = FALSE)
Arguments
df
<tbl_df> Data frame on which to perform weighting.
treatment
<tidy-select> Treatment column to use in propensity score model.
ivs
<tidy-select> Columns of independent variables to use in propensity score model.
impute_missing
<lgl> If TRUE will internally impute missing values in IVs for propensity score fit.
Value
<tbl_df> Data frame with new column for propensity weights.
MethodsConsultants/tibbletest documentation built on June 28, 2021, 5:22 p.m.
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Description Usage Arguments Value
View source: R/propensity-weighting.R
Description
Calculates inverse probability of treatment weights using a random forest.
Usage
1 | add_propensity_weights(df, treatment, ivs, impute_missing = FALSE)
|
Arguments
df |
< |
treatment |
< |
ivs |
< |
impute_missing |
< |
Value
<tbl_df> Data frame with new column for propensity weights.
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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