summary.cv.clogitL1 {clogitL1} | R Documentation |
Provides summary of conditional logistic regression models after cross validation
## S3 method for class 'cv.clogitL1' summary(object, ...)
object |
an object of type |
... |
additional arguments to |
Extracts pertinent information from the supplied cv.clogitL1
objects. See below for details on output 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. |
http://www.jstatsoft.org/v58/i12/
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)