Is it possible to incorporate lasso variable selection in the high dimensional state space model. If yes, is there any code or package available in R
Thanks for your question!
There are 2 packages in R (to my knowledge) that allow you to use LASSO variable selection.
How to use it?
glmnet(x, y, family=c("gaussian","binomial","poisson","multinomial","cox","mgaussian"),
weights, offset=NULL, alpha = 1, nlambda = 100,
lambda.min.ratio = ifelse(nobs))
x is an input matrix,
y is a response,
alpha = 1 means it is using LASSO method. If you set
alpha = 0 it will use Ridge Regularization method.
lars(x, y, type = c("lasso", "lar", "forward.stagewise", "stepwise"), trace = FALSE, normalize = TRUE, intercept = TRUE, Gram, eps = .Machine$double.eps, max.steps, use.Gram = TRUE)
lars(x, y, type = lasso) where
x is a matrix of predictors, and
y is a response.