clogitboost {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)