polyreg (version 0.4.0)

getPoly: Get polynomial terms

Description

Generate polynomial terms of predictor variables for a data frame or data matrix.

Usage

getPoly(xdata = NULL, deg = 1, maxInteractDeg = deg,
                     Xy = NULL, modelFormula = NULL, standardize = FALSE,
                     noisy = TRUE, intercept = FALSE, ...)

Arguments

xdata

Data matrix or data frame without response variable. Categorical variables (> 2 levels) should be passed as factors, not dummy variables or integers, to ensure the polynomial matrix is constructed properly.

deg

The max degree of power terms. Default 1 so just returns model matrix by default.

maxInteractDeg

The max degree of nondummy interaction terms. x1 * x2 is degree 2. x1^3 * x2^2 is degree 5. Implicitly constrained by deg. For example, if deg = 3 and maxInteractDegree = 2, x1^1 * x2^2 (i.e., degree 3) will be included but x1^2 * x2^2 (i.e., degree 4) will not.

Xy

the dataframe with the response in the final column (provide xdata or Xy but not both).Categorical variables (> 2 levels) should be passed as factors, not dummy variables or integers, to ensure the polynomial matrix is constructed properly.

modelFormula

Internal use. Formula used to generate the training model matrix. Note: anticipates that polynomial terms are generated using internal functions of library(polyreg). Also, providing modelFormula bypasses deg and maxInteractDeg.

standardize

standardize all continuous variables? (Default: FALSE.)

noisy

Output progress updates? Defaults to TRUE.

intercept

Include intercept? Default: FALSE.

...

additional arguments to be passed to model.matrix() via polyreg:::model_matrix(). Note na.action = "na.omit".

Value

The return value of getPoly is a polyMatrix object. This is an S3 class containing a data frame xdata of the generated polynomial terms. The predictor variables have column names V1, V2, etc. The object also contains a vector retainedNames, the names of the non-all-0 columns.

Details

The getPoly function takes in a data frame or data matrix and generates polynomial terms of predictor variables.

Note the subtleties involving dummy variables. The square, cubic and so on terms are the same as the original variable, and the various duplicates must be eliminated.

Similarly, after dummy variable are created from a categorical variable having more than two levels, the resulting columns will be orthogonal to each other. In almost all cases, this argument should be set to TRUE at the training stage, and then in predictions one should use the vector of names in the component in the return value; predict.polyFit does the latter automatically.

Examples

# NOT RUN {
x1 <- 1:4
z1 <- c(0,1,0,0)
z2 <- c(1,0,0,0)
z3 <- c(0,0,1,0)
xz <- cbind(x1,z1,z2,z3)
getPoly(xz,2)
# xdata component of output:
#   V1 V2 V3 V4 V5 V9 V10 V11
# 1  1  0  1  0  1  0   1   0
# 2  2  1  0  0  4  2   0   0
# 3  3  0  0  1  9  0   0   3
# 4  4  0  0  0 16  0   0   0
# V1-V4 are copies of x1,z1,z2,z3, 1st-degree terms; V5 is x1^2; V9 is
# product x1 * z2; etc.; note that, V6-V8 were  not retained

# }