Package 'ModelMatrixModel'

ModelMatrixModel() function

Description

This function transforms a data.frame to matrix (or sparse matrix) based on a r formula. The mean different from model.matrix() function is that it outputs a class stored with the transformed matrix, as well as the transforming parameters which can be applied to new data.

Usage

ModelMatrixModel(
  rformula,
  data,
  sparse = TRUE,
  center = FALSE,
  scale = FALSE,
  remove_1st_dummy = FALSE,
  verbose = FALSE
)

Arguments

rformula	a formula, e.g. formula("~ 1+x1+x2"),"~ 1+x1+x2",or ~ 1+x1+x2 . Note the interpreting of the formula might be different slightly from model.matrix function. In model.matrix(),intercept column will be included in output matrix with or without "1" in the formula. But in ModelMatrixModel(),intercept column will be included in output matrix only when "1" is present. Moreover "0" or "." in the formula will be ignored.
data	a data.frame.
sparse	boolean, if TRUE return a sparse matrix, i.e. a "dgCMatrix" class.
center	boolean, if center the output.
scale	boolean, if scale the output.
remove_1st_dummy	boolean, if remove the first dummy variable in one hot key transformation.
verbose	boolean, if print out progress.

Details

see vignettes.

Value

A ModelMatrixModel class,which includes the transformed matrix and the transforming parameters.

Examples

library(ModelMatrixModel)
traindf= data.frame(x1 = sample(LETTERS[1:5], replace = TRUE, 20),
                    x2 = rnorm(20, 100, 5),
                    y = rnorm(20, 10, 2))
mm=ModelMatrixModel(~x1+x2,traindf,remove_1st_dummy = FALSE)
data.frame(as.matrix(head(mm$x,2)))

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predict() function

Description

This function transforms new data based on transforming parameters from a ModelMatrixModel object

Usage

## S3 method for class 'ModelMatrixModel'
predict(object, data, handleInvalid = "keep", verbose = FALSE, ...)

Arguments

object	a ModelMatrixModel object.
data	a data.frame.
handleInvalid	a string,'keep' or 'error'. In dummy variable transformation, if categorical variable has a factor level that is unseen before, 'keep' will keep the record, output dummy variables will be all zero.
verbose	boolean, if print out progress.
...	other parameters.

Value

A ModelMatrixModel class,which includes the transformed matrix and the necessary transforming parameters copied from input object.

Examples

library(ModelMatrixModel)
traindf= data.frame(x1 = sample(LETTERS[1:5], replace = TRUE, 20),
                    x2 = rnorm(20, 100, 5),
                    y = rnorm(20, 10, 2))
newdf=data.frame(x1 = sample(LETTERS[1:5], replace = TRUE, 3),
                 x2 = rnorm(3, 100, 5))
mm=ModelMatrixModel(~x1+x2,traindf,remove_1st_dummy = FALSE)
mm_pred=predict(mm,newdf)
data.frame(as.matrix(head(mm_pred$x,2)))

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