clogitboost: Boosting conditional logit model

In clogitboost: Boosting Conditional Logit Model

Description

Fit a boosting conditional logit model using componentwise smoothing spline.

Usage

clogitboost(y, x, strata, iter, rho)

Arguments

y	vector of binary outcomes.
x	matrix or data frame with each column being a covariate.
strata	vector of group membership, i.e., items in the same group have the same value.
iter	number of iterations.
rho	learning rate parameter in the boosting algorithm.

Value

The function clogitboost returns the following list of values:

call	original function call.
func	list of fitted spline functions.
index	list of indices indicating which covariate is used as input for the smoothing spline.
theta	list of fitted coefficients in the conditional logit models.
loglike	sequence of fitted values of log-likelihood.
infscore	relative influence score for each covariate.
rho	learning rate parameter, which typically takes a value of 0.05 or 0.1.
xmax	maximal element of each covariate.
xmin	minimal element of each covariate.

Author(s)

Haolun Shi [e-mail]

Guosheng Yin [e-mail]

See Also

plot.clogitboost

predict.clogitboost

Examples

data(travel)
train <- 1:504
y <- travel$MODE[train]
x <- travel[train, 3:6]
strata <- travel$Group[train]
fit <- clogitboost(y = y, x = x, strata = strata, iter = 10, rho = 0.05)

Example output

clogitboost documentation built on May 2, 2019, 6:28 a.m.