clogitL1 (version 1.5)

summary.cv.clogitL1: Summary after cross validation of conditional logistic regression with elastic net penalties

Description

Provides summary of conditional logistic regression models after cross validation

Usage

# S3 method for cv.clogitL1
summary (object, ...)

Arguments

object

an object of type cv.clogitL1 for which the summary is to be produced.

...

additional arguments to summary method.

Value

A list with the following fields:

lambda_minCV

value of regularisation parameter minimising CV deviance

beta_minCV

coefficient profile at the minimising value of the regularisation parameter. Whole dataset used to compute estimates.

nz_beta_minCV

number of non-zero coefficients in the CV deviance minimising coefficient profile.

lambda_minCV1se

value of regularisaion parameter minimising CV deviance (using 1 standard error rule)

beta_minCV1se

coefficient profile at the 1-standard-error-rule value of the regularisation parameter. Whole dataset used to compute estimates.

nz_beta_minCV1se

number of non-zero coefficients in the 1-standard-error-rule coefficient profile.

Details

Extracts pertinent information from the supplied cv.clogitL1 objects. See below for details on output value.

References

http://www.jstatsoft.org/v58/i12/

See Also

clogitL1, plot.cv.clogitL1

Examples

# NOT RUN {
set.seed(145)

# data parameters
K = 10 # number of strata
n = 5 # number in strata
m = 2 # cases per stratum
p = 20 # predictors

# generate data
y = rep(c(rep(1, m), rep(0, n-m)), K)
X = matrix (rnorm(K*n*p, 0, 1), ncol = p) # pure noise
strata = sort(rep(1:K, n))

par(mfrow = c(1,2))
# fit the conditional logistic model
clObj = clogitL1(y=y, x=X, strata)
plot(clObj, logX=TRUE)

# cross validation
clcvObj = cv.clogitL1(clObj)
summary(clcvObj)
# }