gic.DMR: gic.DMR

In DMRnet: Delete or Merge Regressors Algorithms for Linear and Logistic Model Selection and High-Dimensional Data

gic.DMR

Description

Computes values of Generalized Information Criterion for the entire sequence of models from a DMR object.

Usage

gic.DMR(
  x,
  c = ifelse(x$arguments$family == "gaussian", constants()$RIC_gaussian_constant,
    constants()$RIC_binomial_constant)
)

Arguments

x

Fitted DMR object.

c

Parameter controlling amount of penalization for complexity of the model in the generalized information criterion (GIC). For linear regression GIC for model M is defined as GIC_M = RSS_M + df_M*c* log{p}*s^2, where RSS_M is the residual sum of squares and df_M is the number of parameters in the model M; s^2 is an estimator of sigma^2 based on the model in the DMR object with the largest number of parameters. For logistic regression GIC for model M is defined as GIC_M = -2*loglik_M + |M|*c* log{p}, where loglik_M is the logarithm of the likelihood function and df_M is the number of parameters in the model M. Recommended values are c=2.5 for linear regression and c=2 for logistic regression.

Value

An object of class "gic.DMR" is returned, which is a list with the ingredients of the gic fit.

df.min

df (number of parameters) for the model with minimal GIC.

dmr.fit

Fitted DMR object.

gic

Vector of GIC values for the entire sequence of models.

See Also

plot.gic.DMR for plotting, coef.gic.DMR for extracting coefficients and predict.gic.DMR for prediction.

Examples

data(miete)
y <- miete[,1]
X <- miete[,-1]
m <- DMR(X, y)
(g <- gic.DMR(m, c = 2.5))

DMRnet documentation built on Aug. 7, 2023, 5:11 p.m.