Nothing
###############
# preliminaries
library(glmmLasso)
data("soccer")
soccer[,c(4,5,9:16)]<-scale(soccer[,c(4,5,9:16)],center=TRUE,scale=TRUE)
soccer<-data.frame(soccer)
###############
## linear mixed model
lm1 <- glmmLasso(points ~ transfer.spendings + ave.unfair.score
+ ball.possession + tackles
+ ave.attend + sold.out, rnd = list(team=~1),
lambda=50, data = soccer)
summary(lm1)
## similar linear model without random effects
lm1b <- glmmLasso(points ~ transfer.spendings + ave.unfair.score
+ ball.possession + tackles
+ ave.attend + sold.out, rnd = NULL,
lambda=50, data = soccer)
summary(lm1b)
## linear mixed model with slope on ave.attend;
## the coefficient of ave.attend is not penalized;
lm2 <- glmmLasso(points~transfer.spendings + ave.unfair.score
+ ball.possession + tackles + ave.attend
+ sold.out, rnd = list(team=~1 + ave.attend), lambda=10,
data = soccer, control = list(index=c(1,2,3,4,NA,5),
method="REML",print.iter=TRUE))
summary(lm2)
## linear mixed model with categorical covariates
## and final Fisher scoring re-estimation step
lm3 <- glmmLasso(points ~ transfer.spendings + as.factor(red.card)
+ as.factor(yellow.red.card) + ball.possession
+ tackles + ave.attend + sold.out, rnd = list(team=~1),
data = soccer, lambda=100, final.re=TRUE,
control = list(print.iter=TRUE,print.iter.final=TRUE))
summary(lm3)
## generalized linear mixed model
## with starting values
glm1 <- glmmLasso(points~transfer.spendings
+ ave.unfair.score + sold.out
+ tackles + ave.attend + ball.possession, rnd = list(team=~1),
family = poisson(link = log), data = soccer, lambda=100,
control = list(print.iter=TRUE,start=c(1,rep(0,29)),q_start=0.7))
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