handleNA: Handle Missing Values
In matchMulti: Optimal Multilevel Matching using a Network Algorithm
View source: R/matchMulti-internal.R
handleNA R Documentation
Handle Missing Values
Description
Preprocesses a dataframe of matching covariates so the Mahalanobis distance
can be calculated.
Usage
handleNA(X, verbose = FALSE)
Arguments
X
a matrix or dataframe of covariates to be used for matching
verbose
logical value indicating whether detailed output should be
provided.
Details
Preprocessing involves three main steps: (1) converting factors to matrices
of dummy variables (2) for any variable with NAs, adding an additional
binary variable indicating whether it is missing (3) imputing all NAs with
the column mean. This follows the recommendations of Rosenbaum in section
9.4 of the referenced text.
Value
a matrix containing the preprocessed data.
Author(s)
Luke Keele, Penn State University, ljk20@psu.edu
Sam Pimentel, University of Pennsylvania, spi@wharton.upenn.edu
References
Rosenbaum, Paul R. (2010). Design of Observational
Studies. Springer-Verlag.
matchMulti documentation built on May 31, 2023, 9:13 p.m.
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.
View source: R/matchMulti-internal.R
| handleNA | R Documentation |
Handle Missing Values
Description
Preprocesses a dataframe of matching covariates so the Mahalanobis distance can be calculated.
Usage
handleNA(X, verbose = FALSE)
Arguments
X |
a matrix or dataframe of covariates to be used for matching |
verbose |
logical value indicating whether detailed output should be provided. |
Details
Preprocessing involves three main steps: (1) converting factors to matrices of dummy variables (2) for any variable with NAs, adding an additional binary variable indicating whether it is missing (3) imputing all NAs with the column mean. This follows the recommendations of Rosenbaum in section 9.4 of the referenced text.
Value
a matrix containing the preprocessed data.
Author(s)
Luke Keele, Penn State University, ljk20@psu.edu
Sam Pimentel, University of Pennsylvania, spi@wharton.upenn.edu
References
Rosenbaum, Paul R. (2010). Design of Observational Studies. Springer-Verlag.
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.