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Type 'q()' to quit R. > ############### > # 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) Call: glmmLasso(fix = points ~ transfer.spendings + ave.unfair.score + ball.possession + tackles + ave.attend + sold.out, rnd = list(team = ~1), data = soccer, lambda = 50) Fixed Effects: Coefficients: Estimate StdErr z.value p.value (Intercept) 46.34413 NA NA NA transfer.spendings 3.39433 NA NA NA ave.unfair.score 0.00000 NA NA NA ball.possession 0.66286 NA NA NA tackles 0.84893 NA NA NA ave.attend 2.40314 NA NA NA sold.out 4.33194 NA NA NA Random Effects: StdDev: team team 1.92366 > > ## 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) Call: glmmLasso(fix = points ~ transfer.spendings + ave.unfair.score + ball.possession + tackles + ave.attend + sold.out, rnd = NULL, data = soccer, lambda = 50) Fixed Effects: Coefficients: Estimate StdErr z.value p.value (Intercept) 46.5926 NA NA NA transfer.spendings 3.8921 NA NA NA ave.unfair.score 0.0000 NA NA NA ball.possession 1.3857 NA NA NA tackles 0.0000 NA NA NA ave.attend 2.3437 NA NA NA sold.out 4.3338 NA NA NA No random effects included! > > > ## 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)) Iteration 1 Iteration 2 Iteration 3 Iteration 4 Iteration 5 Iteration 6 Iteration 7 Iteration 8 Iteration 9 Iteration 10 Iteration 11 Iteration 12 Iteration 13 Iteration 14 Iteration 15 Iteration 16 Iteration 17 Iteration 18 Iteration 19 Iteration 20 Iteration 21 Iteration 22 Iteration 23 Iteration 24 Iteration 25 Iteration 26 Iteration 27 Iteration 28 Iteration 29 Iteration 30 Iteration 31 Iteration 32 Iteration 33 Iteration 34 Iteration 35 Iteration 36 Iteration 37 Iteration 38 Iteration 39 Iteration 40 Iteration 41 Iteration 42 Iteration 43 Iteration 44 Iteration 45 Iteration 46 Iteration 47 Iteration 48 Iteration 49 Iteration 50 Iteration 51 Iteration 52 Iteration 53 Iteration 54 Iteration 55 Iteration 56 Iteration 57 Iteration 58 Iteration 59 Iteration 60 Iteration 61 Iteration 62 Iteration 63 Iteration 64 Iteration 65Warning message: In est.glmmLasso.RE(fix = fix, rnd = rnd, data = data, lambda = lambda, : Random slopes are not standardized back! > > summary(lm2) Call: glmmLasso(fix = points ~ transfer.spendings + ave.unfair.score + ball.possession + tackles + ave.attend + sold.out, rnd = list(team = ~1 + ave.attend), data = soccer, lambda = 10, control = list(index = c(1, 2, 3, 4, NA, 5), method = "REML", print.iter = TRUE)) Fixed Effects: Coefficients: Estimate StdErr z.value p.value (Intercept) 45.381510 NA NA NA transfer.spendings 2.525178 NA NA NA ave.unfair.score -0.035802 NA NA NA ball.possession -0.566986 NA NA NA tackles 0.000000 NA NA NA sold.out 5.079927 NA NA NA ave.attend 4.537369 NA NA NA Random Effects: StdDev: team team:ave.attend team 5.0000000 -0.4918122 team:ave.attend -0.4918122 5.0000000 > > ## 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)) Iteration 1 Iteration 2 Iteration 3 Iteration 4 Final Re-estimation Iteration 1 Final Re-estimation Iteration 2 Final Re-estimation Iteration 3 Final Re-estimation Iteration 4 Final Re-estimation Iteration 5 Final Re-estimation Iteration 6> > summary(lm3) Call: glmmLasso(fix = 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)) Fixed Effects: Coefficients: Estimate StdErr z.value p.value (Intercept) 46.40635 3.72064 12.4727 < 2.2e-16 *** transfer.spendings 1.68662 0.22881 7.3711 1.692e-13 *** as.factor(red.card)1 4.25562 0.49066 8.6732 < 2.2e-16 *** as.factor(red.card)2 -0.43553 0.59082 -0.7371 0.461032 as.factor(red.card)3 1.93277 0.60883 3.1746 0.001501 ** as.factor(red.card)4 -9.96578 1.04159 -9.5679 < 2.2e-16 *** as.factor(red.card)6 9.79310 1.54008 6.3588 2.033e-10 *** as.factor(yellow.red.card)1 0.00000 NA NA NA as.factor(yellow.red.card)2 0.00000 NA NA NA as.factor(yellow.red.card)3 0.00000 NA NA NA as.factor(yellow.red.card)4 0.00000 NA NA NA as.factor(yellow.red.card)5 0.00000 NA NA NA ball.possession -5.83490 0.46765 -12.4771 < 2.2e-16 *** tackles -3.79208 0.36889 -10.2797 < 2.2e-16 *** ave.attend 22.91610 1.37767 16.6339 < 2.2e-16 *** sold.out 3.23552 0.39285 8.2361 < 2.2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Random Effects: StdDev: team team 17.77996 > > ## 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)) Iteration 1 Iteration 2 Iteration 3 Iteration 4 Iteration 5 Iteration 6 Iteration 7 Iteration 8 Iteration 9 Iteration 10 Iteration 11 Iteration 12 Iteration 13 Iteration 14 Iteration 15 Iteration 16 Iteration 17 Iteration 18 Iteration 19 Iteration 20 Iteration 21 Iteration 22 Iteration 23 Iteration 24 Iteration 25 Iteration 26 Iteration 27 Iteration 28 Iteration 29 Iteration 30 Iteration 31 Iteration 32 Iteration 33 Iteration 34 Iteration 35 Iteration 36 Iteration 37 Iteration 38 Iteration 39 Iteration 40 Iteration 41 Iteration 42 Iteration 43 Iteration 44 Iteration 45 Iteration 46 Iteration 47 Iteration 48 Iteration 49 Iteration 50 Iteration 51 Iteration 52 Iteration 53 Iteration 54 Iteration 55 Iteration 56 Iteration 57 Iteration 58 Iteration 59 Iteration 60> > > proc.time() user system elapsed 4.32 0.53 4.84