makeX {glmnet} | R Documentation |
convert a data frame to a data matrix with one-hot encoding
Description
Converts a data frame to a data matrix suitable for input to glmnet
.
Factors are converted to dummy matrices via "one-hot" encoding. Options deal
with missing values and sparsity.
Usage
makeX(train, test = NULL, na.impute = FALSE, sparse = FALSE, ...)
Arguments
train |
Required argument. A dataframe consisting of vectors, matrices and factors |
test |
Optional argument. A dataframe matching 'train' for use as testing data |
na.impute |
Logical, default |
sparse |
Logical, default |
... |
additional arguments, currently unused |
Details
The main function is to convert factors to dummy matrices via "one-hot"
encoding. Having the 'train' and 'test' data present is useful if some
factor levels are missing in either. Since a factor with k levels leads to a
submatrix with 1/k entries zero, with large k the sparse=TRUE
option
can be helpful; a large matrix will be returned, but stored in sparse matrix
format. Finally, the function can deal with missing data. The current
version has the option to replace missing observations with the mean from
the training data. For dummy submatrices, these are the mean proportions at
each level.
Value
If only 'train' was provided, the function returns a matrix 'x'. If missing values were imputed, this matrix has an attribute containing its column means (before imputation). If 'test' was provided as well, a list with two components is returned: 'x' and 'xtest'.
Author(s)
Trevor Hastie
Maintainer: Trevor Hastie hastie@stanford.edu
See Also
glmnet
Examples
set.seed(101)
### Single data frame
X = matrix(rnorm(20), 10, 2)
X3 = sample(letters[1:3], 10, replace = TRUE)
X4 = sample(LETTERS[1:3], 10, replace = TRUE)
df = data.frame(X, X3, X4)
makeX(df)
makeX(df, sparse = TRUE)
### Single data freame with missing values
Xn = X
Xn[3, 1] = NA
Xn[5, 2] = NA
X3n = X3
X3n[6] = NA
X4n = X4
X4n[9] = NA
dfn = data.frame(Xn, X3n, X4n)
makeX(dfn)
makeX(dfn, sparse = TRUE)
makeX(dfn, na.impute = TRUE)
makeX(dfn, na.impute = TRUE, sparse = TRUE)
### Test data as well
X = matrix(rnorm(10), 5, 2)
X3 = sample(letters[1:3], 5, replace = TRUE)
X4 = sample(LETTERS[1:3], 5, replace = TRUE)
dft = data.frame(X, X3, X4)
makeX(df, dft)
makeX(df, dft, sparse = TRUE)
### Missing data in test as well
Xn = X
Xn[3, 1] = NA
Xn[5, 2] = NA
X3n = X3
X3n[1] = NA
X4n = X4
X4n[2] = NA
dftn = data.frame(Xn, X3n, X4n)
makeX(dfn, dftn)
makeX(dfn, dftn, sparse = TRUE)
makeX(dfn, dftn, na.impute = TRUE)
makeX(dfn, dftn, sparse = TRUE, na.impute = TRUE)