I am running a conditional logistic regression on a case/control dataset and would like to use lasso for variable selection. I am currently using to use the clogitL1, but I do not see a way to force the inclusion of my control variables.
(1) Am I missing an option within the package that would allow me to force include specific parameters?
(2) Are there other packages of methodologies that I could accomplish this with?
'''
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(Knp, 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)
plot(clcvObj)
'''