MetGen (version 0.5)

fit.lasso: Lasso regression

Description

Fit a generalized linear model via penalized maximum likelihood. LASSO regression is applied to perform a preliminary screening for a large covariate set. It solves a regularized least squares problem recognized for its potential to perform simultaneously variable selection and parameter estimation. It might be used only for gaussian cases to yield parsimonious solutions, i.e. with few covariates.

Usage

fit.lasso(cov.mat, var.name)

Arguments

cov.mat

data frame that contains only the variable to be fitted, and a large set of covariates that will be tested to choose the parsimonious ones relying on Lasso coefficient estimates

var.name

character object that forms the name of the variable to be fitted

Value

an object of class "cv.glmnet" is returned, which is a list with the ingredients of the cross-validation fit.

See Also

glmnet, cv.glmnet

Examples

# NOT RUN {
##Create a new data that contains climatic series and all effects that will be
##used as covariates for the variable to be computed
mat_effects <- seasonal.effect(myclimatic_data, period=c(365,183))
mat_effects <- diurnal.effect(mat_effects, period=24)
mat_effects <- spatave.effect(mat_effects, "temp", nstat = 3, na.proc = TRUE)
mat_effects <- lagged.effect(mat_effects, "temp",2, nstat=3)
mat_effects$t2m <- rnorm(nrow(myclimatic_data), mean=25, sd=1)

fit.lasso(mat_effects[,3:length(mat_effects)], "temp")
# }