| Type: | Package |
| Title: | Category Variable Encodings |
| Encoding: | UTF-8 |
| Version: | 1.4.3 |
| Date: | 2020-1-30 |
| BugReports: | https://github.com/JSzitas/categoryEncodings/issues |
| Description: | Simple, fast, and automatic encodings for category data using a data.table backend. Most of the methods are an implementation of "Sufficient Representation for Categorical Variables" by Johannemann, Hadad, Athey, Wager (2019) <doi:10.48550/arXiv.1908.09874>, particularly their mean, sparse principal component analysis, low rank representation, and multinomial logit encodings. |
| License: | GPL-3 |
| RoxygenNote: | 6.1.1 |
| Suggests: | testthat (≥ 2.1.0), covr |
| URL: | https://github.com/JSzitas/categoryEncodings |
| Imports: | glmnet, sparsepca, data.table |
| NeedsCompilation: | no |
| Packaged: | 2020-02-22 10:42:43 UTC; juraj |
| Author: | Juraj Szitas [aut, cre] |
| Maintainer: | Juraj Szitas <szitas.juraj13@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2020-03-02 11:20:06 UTC |
Encode a given factor variable using a sparse PCA representation
Description
Transforms the original design matrix using a sPCA encoding.
Usage
encode_SPCA(X, fact, keep_factor = FALSE, encoding_only = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
fact |
The factor variable to encode by - either a positive integer specifying the column number, or the name of the column. |
keep_factor |
Whether to keep the original factor column(defaults to **FALSE**). |
encoding_only |
Whether to return the full transformed dataset or only the new columns. Defaults to FALSE and returns the full dataset. |
Details
Uses the method from Johannemann et al.(2019) 'Sufficient Representations for Categorical Variables' - sPCA.
Value
A new data.table X which contains the new columns and optionally the old factor.
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_SPCA(X = design_mat, fact = "factor_var", keep_factor = FALSE)
Encode a given factor variable automatically
Description
Transforms the original design matrix automatically, using the appropriate encoding.
Usage
encode_categories(X, Y = NULL, fact = NULL, method = NULL,
keep = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
Y |
Optional: The dependent variable to ignore in the transformation. |
fact |
Optional: The factor variable(s) to encode by - either positive integer(s) specifying the column number, or the name(s) of the column. If left empty a heuristic is used to determine the factor variable(s), and a warning is written with the names of the variables converted. |
method |
Optional: A character string indicating which encoding method to use, either of the following: * "mean" * "median" * "deviation" * "lowrank" * "SPCA" * "mnl" * "dummy" * "difference" * "helmert" * "simple_effect" * "repeated_effect" If only a single method is specified, it is taken to encode either all of the variables supplied through *fact*, or variables which have been flagged as factors automatically. If multiple methods are specified, the number of methods must match the number of factor variables in *fact* - and these are applied to correspond in the order in which they were supplied. In case a missmatch occurs, an error is raised. If left empty, the appriopriate method is selected on a case by case basis (and the selected methods are written out to console). |
keep |
Whether to keep the original factor column(s), defaults to **FALSE**. |
Details
Automatically selects the appropriate method given the number of anticipated newly created variables, based on the results in Johannemann et al.(2019) 'Sufficient Representations for Categorical Variables', and a simple heuristic - where
Value
A new data.table X which contains the new columns and optionally the old factor(s).
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_categories( design_mat, method = "mean" )
Encode a given factor variable using deviation encoding
Description
Transforms the original design matrix using a deviation dummy encoding.
Usage
encode_deviation(X, fact, keep_factor = FALSE, encoding_only = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
fact |
The factor variable to encode by - either a positive integer specifying the column number, or the name of the column. |
keep_factor |
Whether to keep the original factor column(defaults to **FALSE**). |
encoding_only |
Whether to return the full transformed dataset or only the new columns. Defaults to FALSE and returns the full dataset. |
Details
The deviation dummy variable encoding, with reference class level set to -1. The reference class is always the last class observed.
Value
A new data.table X which contains the new columns and optionally the old factor.
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_deviation(X = design_mat, fact = "factor_var", keep_factor = FALSE)
Encode a given factor variable using difference encoding
Description
Transforms the original design matrix using a difference encoding.
Usage
encode_difference(X, fact, keep_factor = FALSE, encoding_only = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
fact |
The factor variable to encode by - either a positive integer specifying the column number, or the name of the column. |
keep_factor |
Whether to keep the original factor column(defaults to **FALSE**). |
encoding_only |
Whether to return the full transformed dataset or only the new columns. Defaults to FALSE and returns the full dataset. |
Value
A new data.table X which contains the new columns and optionally the old factor.
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_difference(X = design_mat, fact = "factor_var", keep_factor = FALSE)
Encode a given factor variable using dummy variables
Description
Transforms the original design matrix using a dummy variable encoding.
Usage
encode_dummy(X, fact, keep_factor = FALSE, encoding_only = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
fact |
The factor variable to encode by - either a positive integer specifying the column number, or the name of the column. |
keep_factor |
Whether to keep the original factor column(defaults to **FALSE**). |
encoding_only |
Whether to return the full transformed dataset or only the new columns. Defaults to FALSE and returns the full dataset. |
Details
The basic dummy variable encoding, with reference class level set to 0. The reference class is always the first class observed.
Value
A new data.table X which contains the new columns and optionally the old factor.
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_dummy(X = design_mat, fact = "factor_var", keep_factor = FALSE)
Encode a given factor variable using helmert encoding
Description
Transforms the original design matrix using a helmert (reverse difference) encoding.
Usage
encode_helmert(X, fact, keep_factor = FALSE, encoding_only = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
fact |
The factor variable to encode by - either a positive integer specifying the column number, or the name of the column. |
keep_factor |
Whether to keep the original factor column(defaults to **FALSE**). |
encoding_only |
Whether to return the full transformed dataset or only the new columns. Defaults to FALSE and returns the full dataset. |
Value
A new data.table X which contains the new columns and optionally the old factor.
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_helmert(X = design_mat, fact = "factor_var", keep_factor = FALSE)
Encode a given factor variable using low rank encoding
Description
Transforms the original design matrix using a low rank encoding.
Usage
encode_lowrank(X, fact, keep_factor = FALSE, encoding_only = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
fact |
The factor variable to encode by - either a positive integer specifying the column number, or the name of the column. |
keep_factor |
Whether to keep the original factor column(defaults to **FALSE**). |
encoding_only |
Whether to return the full transformed dataset or only the new columns. Defaults to FALSE and returns the full dataset. |
Details
Uses the method from Johannemann et al.(2019) 'Sufficient Representations for Categorical Variables' - Low rank.
Value
A new data.table X which contains the new columns and optionally the old factor.
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_lowrank(X = design_mat, fact = "factor_var", keep_factor = FALSE)
Encode a given factor variable using means encoding
Description
Transforms the original design matrix using a means encoding.
Usage
encode_mean(X, fact, keep_factor = FALSE, encoding_only = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
fact |
The factor variable to encode by - either a positive integer specifying the column number, or the name of the column. |
keep_factor |
Whether to keep the original factor column(defaults to **FALSE**). |
encoding_only |
Whether to return the full transformed dataset or only the new columns. Defaults to FALSE and returns the full dataset. |
Details
Uses the method from Johannemann et al.(2019) 'Sufficient Representations for Categorical Variables' - Means Encoding.
Value
A new data.table X which contains the new columns and optionally the old factor.
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_mean(X = design_mat, fact = "factor_var", keep_factor = FALSE)
Encode a given factor variable using median encoding
Description
Transforms the original design matrix using a median encoding.
Usage
encode_median(X, fact, keep_factor = FALSE, encoding_only = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
fact |
The factor variable to encode by - either a positive integer specifying the column number, or the name of the column. |
keep_factor |
Whether to keep the original factor column(defaults to **FALSE**). |
encoding_only |
Whether to return the full transformed dataset or only the new columns. Defaults to FALSE and returns the full dataset. |
Details
This might be somewhat lacking in theory (to the author's best knowledge), but feel free to try it and publish the results if they turn out interesting on some particular problem.
Value
A new data.table X which contains the new columns and optionally the old factor.
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_median(X = design_mat, fact = "factor_var", keep_factor = FALSE)
Encode a given factor variable using a multinomial logit representation
Description
Transforms the original design matrix using a mnl encoding.
Usage
encode_mnl(X, fact, keep_factor = FALSE, encoding_only = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
fact |
The factor variable to encode by - either a positive integer specifying the column number, or the name of the column. |
keep_factor |
Whether to keep the original factor column(defaults to **FALSE**). |
encoding_only |
Whether to return the full transformed dataset or only the new columns. Defaults to FALSE and returns the full dataset. |
Details
Uses the method from Johannemann et al.(2019) 'Sufficient Representations for Categorical Variables' - mnl.
Value
A new data.table X which contains the new columns and optionally the old factor.
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_mnl(X = design_mat, fact = "factor_var", keep_factor = FALSE)
Encode a given factor variable using a repeated effect encoding
Description
Transforms the original design matrix using a repeated effect encoding.
Usage
encode_repeated_effect(X, fact, keep_factor = FALSE,
encoding_only = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
fact |
The factor variable to encode by - either a positive integer specifying the column number, or the name of the column. |
keep_factor |
Whether to keep the original factor column(defaults to **FALSE**). |
encoding_only |
Whether to return the full transformed dataset or only the new columns. Defaults to FALSE and returns the full dataset. |
Value
A new data.table X which contains the new columns and optionally the old factor.
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_repeated_effect(X = design_mat, fact = "factor_var", keep_factor = FALSE)
Encode a given factor variable using a simple effect encoding
Description
Transforms the original design matrix using a simple effect encoding.
Usage
encode_simple_effect(X, fact, keep_factor = FALSE,
encoding_only = FALSE)
Arguments
X |
The data.frame/data.table to transform. |
fact |
The factor variable to encode by - either a positive integer specifying the column number, or the name of the column. |
keep_factor |
Whether to keep the original factor column(defaults to **FALSE**). |
encoding_only |
Whether to return the full transformed dataset or only the new columns. Defaults to FALSE and returns the full dataset. |
Value
A new data.table X which contains the new columns and optionally the old factor.
Examples
design_mat <- cbind( data.frame( matrix(rnorm(5*100),ncol = 5) ),
sample( sample(letters, 10), 100, replace = TRUE)
)
colnames(design_mat)[6] <- "factor_var"
encode_simple_effect(X = design_mat, fact = "factor_var", keep_factor = FALSE)