numeric.encoder() creates an encoder function for a quantitative variable.
This encoder can then be used to convert a numeric vector into a design matrix using either piecewise linear or one-hot interval encoding, which are core components for modeling effects in a MID model.
numeric.frame() is a helper function to create a "numeric.frame" object that defines the encoding scheme.
Arguments
- x
a numeric vector to be encoded.
- k
an integer specifying the coarseness of the encoding. If not positive, all unique values of
xare used as knots or bins.- type
a character string or an integer specifying the encoding method:
"linear"/1(default) or"constant"/0.- split
a character string specifying the splitting strategy:
"quantile"(default) creates bins/knots based on data density;"uniform"creates equally spaced bins/knots over the data range.- digits
an integer specifying the rounding digits for the piecewise linear encoding (
type = "linear").- weights
an optional numeric vector of sample weights for
x.- frame
a "numeric.frame" object or a numeric vector that explicitly defines the knots or breaks for the encoding.
- tag
the name of the variable.
- reps
a numeric vector to be used as the representative values (knots).
- breaks
a numeric vector to be used as the binning breaks.
Value
numeric.encoder() returns an object of class "encoder". This is a list containing the following components:
- frame
a "numeric.frame" object containing the encoding information.
- n
the number of encoding levels (i.e., columns in the design matrix).
- type
a character string describing the encoding type: "linear", "constant", or "null".
- envir
an environment for the
transformandencodefunctions.- transform
a function
transform(x, ...)(identity by default).- encode
a function
encode(x, ...)that converts a numeric vector into a dummy matrix.
numeric.frame() returns a "numeric.frame" object containing the encoding information.
Details
The primary purpose of the encoder is to transform a single numeric variable into a design matrix for the MID model's linear system formulation.
The output of the encoder depends on the type argument.
When type = 1, the variable's effect is modeled as a piecewise linear function with k knots including both ends.
For each value, the encoder finds the two nearest knots and assigns a weight to each, based on its relative position.
This results in a design matrix where each row has at most two non-zero values that sum to 1.
This approach creates a smooth, continuous representation of the effect.
When type = 0, the variable's effect is modeled as a step function by dividing its range into k intervals (bins).
The encoder determines which interval each value falls into and assigns a 1 to the corresponding column in the design matrix, with all other columns being 0.
This results in a standard one-hot encoded matrix and creates a discrete, bin-based representation of the effect.
Examples
# Create an encoder for a quantitative variable
data(iris, package = "datasets")
enc <- numeric.encoder(x = iris$Sepal.Length, k = 5L, tag = "Sepal.Length")
enc
#>
#> Linear encoder with 5 knots
#>
#> Frame:
#> Sepal.Length
#> 1 4.3
#> 2 5.1
#> 3 5.8
#> 4 6.4
#> 5 7.9
#>
# Encode a numeric vector with NA and Inf
enc$encode(x = c(4:8, NA, Inf))
#> 4.3 5.1 5.8 6.4 7.9
#> [1,] 1.000 0.000 0.0000000 0.0000000 0.0
#> [2,] 0.125 0.875 0.0000000 0.0000000 0.0
#> [3,] 0.000 0.000 0.6666667 0.3333333 0.0
#> [4,] 0.000 0.000 0.0000000 0.6000000 0.4
#> [5,] 0.000 0.000 0.0000000 0.0000000 1.0
#> [6,] 0.000 0.000 0.0000000 0.0000000 0.0
#> [7,] 0.000 0.000 0.0000000 0.0000000 1.0
# Create an encoder with a pre-defined encoding frame
frm <- numeric.frame(breaks = c(3, 5, 7, 9), type = 0L)
enc <- numeric.encoder(x = iris$Sepal.Length, frame = frm)
enc$encode(x = c(4:8, NA, Inf))
#> [-Inf, 5) [5, 7) [7, Inf)
#> [1,] 1 0 0
#> [2,] 0 1 0
#> [3,] 0 1 0
#> [4,] 0 0 1
#> [5,] 0 0 1
#> [6,] 0 0 0
#> [7,] 0 0 1
# Create an encoder with a numeric vector specifying the knots
enc <- numeric.encoder(x = iris$Sepal.Length, frame = c(3, 5, 7, 9))
enc$encode(x = c(4:8, NA, Inf))
#> 3 5 7 9
#> [1,] 0.5 0.5 0.0 0.0
#> [2,] 0.0 1.0 0.0 0.0
#> [3,] 0.0 0.5 0.5 0.0
#> [4,] 0.0 0.0 1.0 0.0
#> [5,] 0.0 0.0 0.5 0.5
#> [6,] 0.0 0.0 0.0 0.0
#> [7,] 0.0 0.0 0.0 1.0