I am looking for suggestions for an installation strategy generalized linear mixed-effects modelsfor a relatively large data set.
I think that I have data on 8 millionUS basketballs for about 300 teams in 10 years. The data looks something like this:
data <- data.frame(count = c(1,1,2,1,1,5),
length_pass= c(1,2,5,7,1,3),
year= c(1,1,1,2,2,2),
mean_length_pass_team= c(15,15,9,14,14,8),
team= c('A', 'A', 'B', 'A', 'A', 'B'))
data
count length_pass year mean_length_pass_team team
1 1 1 1 15 A
2 1 2 1 15 A
3 2 5 1 9 B
4 1 7 2 14 A
5 1 1 2 14 A
6 5 3 2 8 B
I want to explain the countsteps a player takes before passing the ball. I have theoretical motives to suggest that there are differences at the team level between countand length_pass, therefore, it seems appropriate multilevel (i.e. Mixed effects).
My variables are level controls length_passand year.
mean_length_pass_team. , Snijders, 2011.
lme4 brms , / 12- 128 .
library(lme4)
model_a <- glmer(count ~ length_pass + year + mean_length_pass_team + (1 | team),
data=data,
family= "poisson",
control=glmerControl(optCtrl=list(maxfun=2e8)))
library(brms)
options (mc.cores=parallel::detectCores ())
model_b <- brm(count ~ length_pass + year + mean_length_pass_team + (1 | team),
data=data,
family= "poisson")
, , :
- ()
lme4 brms ? - ?
step-wise, ?R, ?
!