sparseRBIC_step {sparseR}

Fit a ranked-sparsity model with forward stepwise RBIC (experimental)

Description

Fit a ranked-sparsity model with forward stepwise RBIC (experimental)

Usage

sparseRBIC_step(
  formula,
  data,
  family = c("gaussian", "binomial", "poisson"),
  k = 1,
  poly = 1,
  ic = c("RBIC", "RAIC", "BIC", "AIC", "EBIC"),
  hier = c("strong", "weak", "none"),
  sequential = (hier[1] != "none"),
  cumulative_k = FALSE,
  cumulative_poly = TRUE,
  pool = FALSE,
  ia_formula = NULL,
  pre_process = TRUE,
  model_matrix = NULL,
  y = NULL,
  poly_prefix = "_poly_",
  int_sep = "\\:",
  pre_proc_opts = c("knnImpute", "scale", "center", "otherbin", "none"),
  filter = c("nzv", "zv"),
  extra_opts = list(),
  trace = 0,
  message = TRUE,
  ...
)

Arguments

formula

Names of the terms

data

Data

family

The family of the model

k

The maximum order of interactions to consider

poly

The maximum order of polynomials to consider

ic

The information criterion to use

hier

Should hierarchy be enforced (weak or strong)? Must be set with sequential == TRUE (see details)

sequential

Should the main effects be considered first, orders sequentially added/considered?

cumulative_k

Should penalties be increased cumulatively as order interaction increases?

cumulative_poly

Should penalties be increased cumulatively as order polynomial increases?

pool

Should interactions of order k and polynomials of order k+1 be pooled together for calculating the penalty?

ia_formula

formula to be passed to step_interact via terms argument

pre_process

Should the data be preprocessed (if FALSE, must provide model_matrix)

model_matrix

A data frame or matrix specifying the full model matrix (used if !pre_process)

y

A vector of responses (used if !pre_process)

poly_prefix

If model_matrix is specified, what is the prefix for polynomial terms?

int_sep

If model_matrix is specified, what is the separator for interaction terms?

pre_proc_opts

List of preprocessing steps (see details)

filter

The type of filter applied to main effects + interactions

extra_opts

A list of options for all preprocess steps (see details)

trace

Should intermediate results of model selection process be output

message

should experimental message be suppressed

...

additional arguments for running stepwise selection

Details

This function mirrors sparseR but uses stepwise selection guided by RBIC.

Additionally, setting cumulative_poly or cumulative_k to TRUE increases the penalty cumulatively based on the order of either polynomial or interaction.

The hier hierarchy enforcement will only work if sequential == TRUE, and notably will only consider the "first gen" hierarchy, that is, that all main effects which make up an interaction are already in the model. It is therefore possible for a third order interaction (x1:x2:x3) to enter a model without x1:x2 or x2:x3, so long as x1, x2, and x3 are all in the model.

The options that can be passed to pre_proc_opts are:

• knnImpute (should missing data be imputed?)

• scale (should data be standardized)?

• center (should data be centered to the mean or another value?)

• otherbin (should factors with low prevalence be combined?)

• none (should no preprocessing be done? can also specify a null object)

The options that can be passed to extra_opts are:

• centers (named numeric vector which denotes where each covariate should be centered)

• center_fn (alternatively, a function can be specified to calculate center such as min or median)

• freq_cut, unique_cut (see ?step_nzv - these get used by the filtering steps)

• neighbors (the number of neighbors for knnImpute)

• one_hot (see ?step_dummy), this defaults to cell-means coding which can be done in regularized regression (change at your own risk)

• raw (should polynomials not be orthogonal? defaults to true because variables are centered and scaled already by this point by default)

Value

an object of class sparseRBIC containing the following:

fit

the final fit object

srprep

a recipes object used to prep the data

pen_info

coefficient-level variable counts, types + names

data

the (unprocessed) data

family

the family argument (for non-normal, eg. poisson)

info

a list containing meta-info about the procedure

stats

the IC for each fit and respective terms included