[Lme4-commits] r1865 - pkg/mlmRev/tests
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Sep 23 21:06:08 CEST 2013
Author: walker
Date: 2013-09-23 21:06:08 +0200 (Mon, 23 Sep 2013)
New Revision: 1865
Removed:
pkg/mlmRev/tests/guImmun.Rout.save
pkg/mlmRev/tests/lmerTest.Rout.save
Log:
removed Rout.save files to comply with CRAN
Deleted: pkg/mlmRev/tests/guImmun.Rout.save
===================================================================
--- pkg/mlmRev/tests/guImmun.Rout.save 2013-09-23 14:42:09 UTC (rev 1864)
+++ pkg/mlmRev/tests/guImmun.Rout.save 2013-09-23 19:06:08 UTC (rev 1865)
@@ -1,187 +0,0 @@
-
-R version 3.0.1 (2013-05-16) -- "Good Sport"
-Copyright (C) 2013 The R Foundation for Statistical Computing
-Platform: x86_64-apple-darwin10.8.0 (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-> library(mlmRev)
-Loading required package: lme4
-Loading required package: lattice
-Loading required package: Matrix
-> options(digits=6, useFancyQuotes = FALSE)# signif.stars for once..
->
-> fm <- glmer(immun ~ kid2p + mom25p + ord + ethn + momEd +
-+ husEd + momWork + rural + pcInd81 + (1|mom) + (1|comm),
-+ data = guImmun, family = binomial)
-Warning message:
-In (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
- failure to converge in 10000 evaluations
-> print(fm, symbolic.cor = TRUE)
-Generalized linear mixed model fit by maximum likelihood ['glmerMod']
- Family: binomial ( logit )
-Formula: immun ~ kid2p + mom25p + ord + ethn + momEd + husEd + momWork + rural + pcInd81 + (1 | mom) + (1 | comm)
- Data: guImmun
- AIC BIC logLik deviance
- 2747.48 2849.67 -1355.74 2711.48
-Random effects:
- Groups Name Std.Dev.
- mom (Intercept) 1.148
- comm (Intercept) 0.727
-Number of obs: 2159, groups: mom, 1595; comm, 161
-Fixed Effects:
-(Intercept) kid2pY mom25pY ord23 ord46 ord7p
- -0.879711 1.268196 -0.125451 -0.158003 0.155659 0.265419
- ethnN ethnS momEdP momEdS husEdP husEdS
- -0.147083 -0.050289 0.276992 0.277592 0.386208 0.346964
- husEdU momWorkY ruralY pcInd81
- -0.000177 0.264580 -0.663442 -0.865817
->
-> fm.h <- update(fm, ~ . - husEd)
-> print(fm.h, corr = FALSE)
-Generalized linear mixed model fit by maximum likelihood ['glmerMod']
- Family: binomial ( logit )
-Formula: immun ~ kid2p + mom25p + ord + ethn + momEd + momWork + rural + pcInd81 + (1 | mom) + (1 | comm)
- Data: guImmun
- AIC BIC logLik deviance
- 2748.60 2833.76 -1359.30 2718.60
-Random effects:
- Groups Name Std.Dev.
- mom (Intercept) 1.119
- comm (Intercept) 0.723
-Number of obs: 2159, groups: mom, 1595; comm, 161
-Fixed Effects:
-(Intercept) kid2pY mom25pY ord23 ord46 ord7p
- -0.609 1.261 -0.147 -0.149 0.151 0.230
- ethnN ethnS momEdP momEdS momWorkY ruralY
- -0.247 -0.101 0.337 0.352 0.267 -0.715
- pcInd81
- -0.824
-> fm.ho <- update(fm.h, ~ . - ord)
-> ## FIXME: shows 53 outer iterations (+ probably IRLS ones) --
-> ## but no such info is kept stored
-> print(fm.ho, corr = FALSE)
-Generalized linear mixed model fit by maximum likelihood ['glmerMod']
- Family: binomial ( logit )
-Formula: immun ~ kid2p + mom25p + ethn + momEd + momWork + rural + pcInd81 + (1 | mom) + (1 | comm)
- Data: guImmun
- AIC BIC logLik deviance
- 2746.30 2814.43 -1361.15 2722.30
-Random effects:
- Groups Name Std.Dev.
- mom (Intercept) 1.111
- comm (Intercept) 0.711
-Number of obs: 2159, groups: mom, 1595; comm, 161
-Fixed Effects:
-(Intercept) kid2pY mom25pY ethnN ethnS momEdP
- -0.6809 1.2645 0.0262 -0.2113 -0.0792 0.3306
- momEdS momWorkY ruralY pcInd81
- 0.3229 0.2487 -0.6804 -0.8417
->
-> anova(fm, fm.h, fm.ho)
-Data: guImmun
-Models:
-fm.ho: immun ~ kid2p + mom25p + ethn + momEd + momWork + rural + pcInd81 +
-fm.ho: (1 | mom) + (1 | comm)
-fm.h: immun ~ kid2p + mom25p + ord + ethn + momEd + momWork + rural +
-fm.h: pcInd81 + (1 | mom) + (1 | comm)
-fm: immun ~ kid2p + mom25p + ord + ethn + momEd + husEd + momWork +
-fm: rural + pcInd81 + (1 | mom) + (1 | comm)
- Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
-fm.ho 12 2746 2814 -1361 2722
-fm.h 15 2749 2834 -1359 2719 3.701 3 0.2956
-fm 18 2748 2850 -1356 2712 7.118 3 0.0682 .
----
-Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
->
-> (fm.hoe <- update(fm.ho, ~ . - ethn))
-Generalized linear mixed model fit by maximum likelihood ['glmerMod']
- Family: binomial ( logit )
-Formula: immun ~ kid2p + mom25p + momEd + momWork + rural + pcInd81 + (1 | mom) + (1 | comm)
- Data: guImmun
- AIC BIC logLik deviance
- 2742.77 2799.54 -1361.38 2722.77
-Random effects:
- Groups Name Std.Dev.
- mom (Intercept) 1.093
- comm (Intercept) 0.698
-Number of obs: 2159, groups: mom, 1595; comm, 161
-Fixed Effects:
-(Intercept) kid2pY mom25pY momEdP momEdS momWorkY
- -0.7017 1.2660 0.0252 0.3530 0.3604 0.2588
- ruralY pcInd81
- -0.6708 -0.9535
->
-> (fm.hoem <- update(fm.hoe, ~ . - mom25p))
-Generalized linear mixed model fit by maximum likelihood ['glmerMod']
- Family: binomial ( logit )
-Formula: immun ~ kid2p + momEd + momWork + rural + pcInd81 + (1 | mom) + (1 | comm)
- Data: guImmun
- AIC BIC logLik deviance
- 2740.8 2791.9 -1361.4 2722.8
-Random effects:
- Groups Name Std.Dev.
- mom (Intercept) 1.114
- comm (Intercept) 0.699
-Number of obs: 2159, groups: mom, 1595; comm, 161
-Fixed Effects:
-(Intercept) kid2pY momEdP momEdS momWorkY ruralY
- -0.698 1.258 0.358 0.353 0.278 -0.672
- pcInd81
- -0.945
->
-> (AN <- anova(fm, fm.h, fm.ho, fm.hoe, fm.hoem))
-Data: guImmun
-Models:
-fm.hoem: immun ~ kid2p + momEd + momWork + rural + pcInd81 + (1 | mom) +
-fm.hoem: (1 | comm)
-fm.hoe: immun ~ kid2p + mom25p + momEd + momWork + rural + pcInd81 +
-fm.hoe: (1 | mom) + (1 | comm)
-fm.ho: immun ~ kid2p + mom25p + ethn + momEd + momWork + rural + pcInd81 +
-fm.ho: (1 | mom) + (1 | comm)
-fm.h: immun ~ kid2p + mom25p + ord + ethn + momEd + momWork + rural +
-fm.h: pcInd81 + (1 | mom) + (1 | comm)
-fm: immun ~ kid2p + mom25p + ord + ethn + momEd + husEd + momWork +
-fm: rural + pcInd81 + (1 | mom) + (1 | comm)
- Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
-fm.hoem 9 2741 2792 -1361 2723
-fm.hoe 10 2743 2800 -1361 2723 0.030 1 0.8622
-fm.ho 12 2746 2814 -1361 2722 0.471 2 0.7903
-fm.h 15 2749 2834 -1359 2719 3.701 3 0.2956
-fm 18 2748 2850 -1356 2712 7.118 3 0.0682 .
----
-Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
->
-> AN[, "logLik"] + 1362 # an inversion in the first two models
-[1] 0.600013 0.615079 0.850417 2.701099 6.260151
-> ## FIXME: AN doesn't have a deviance column!
-> ## AN[, "deviance"] - 2711 # deviance scale shows this more clearly
-> stopifnot(AN[,"Df"] == c(9,10,12,15,18),
-+ # all.equal(AN[,"logLik"] + 1362,
-+ # c(0.6072186497422, 0.6289103306312, 0.8541186984307,
-+ # 2.725550814599, 6.299084917162), tol = 1e-6),
-+ # all.equal(fixef(fm.hoem)[-1],
-+ # c("kid2pY" = 1.2662536, "momEdP"= 0.35116180,
-+ # "momEdS"= 0.3487824136, "momWorkY"=0.2672759992340,
-+ # "ruralY"=-0.678846606719, "pcInd81"=-0.9612710104134),
-+ # tol = 1e-4),
-+ TRUE
-+ )
->
->
-> cat('Time elapsed: ', proc.time(),'\n') # "stats"
-Time elapsed: 110.759 0.204 110.957 0.001 0.002
->
-> proc.time()
- user system elapsed
-110.760 0.206 110.957
Deleted: pkg/mlmRev/tests/lmerTest.Rout.save
===================================================================
--- pkg/mlmRev/tests/lmerTest.Rout.save 2013-09-23 14:42:09 UTC (rev 1864)
+++ pkg/mlmRev/tests/lmerTest.Rout.save 2013-09-23 19:06:08 UTC (rev 1865)
@@ -1,482 +0,0 @@
-
-R version 2.10.0 RC (2009-10-25 r50206)
-Copyright (C) 2009 The R Foundation for Statistical Computing
-ISBN 3-900051-07-0
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-> #### LMER: Put all the small data set tests into one file
-> library(mlmRev)
-Loading required package: lme4
-Loading required package: Matrix
-Loading required package: lattice
-
-> options(digits=6, show.signif.stars = FALSE)
->
-> ## bdf ---------------- Data ---------------------
-> (fm01 <- lmer(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen + (1|schoolNR), bdf))
-Linear mixed model fit by REML
-Formula: langPOST ~ IQ.ver.cen + avg.IQ.ver.cen + (1 | schoolNR)
- Data: bdf
- AIC BIC logLik deviance REMLdev
- 15242 15271 -7616 15228 15232
-Random effects:
- Groups Name Variance Std.Dev.
- schoolNR (Intercept) 7.886 2.808
- Residual 42.172 6.494
-Number of obs: 2287, groups: schoolNR, 131
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) 40.7410 0.2866 142.2
-IQ.ver.cen 2.4148 0.0717 33.7
-avg.IQ.ver.cen 1.5892 0.3148 5.0
-
-Correlation of Fixed Effects:
- (Intr) IQ.vr.
-IQ.ver.cen 0.000
-avg.IQ.vr.c 0.077 -0.228
-> (fm02 <- lmer(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen +(IQ.ver.cen|schoolNR), bdf))
-Linear mixed model fit by REML
-Formula: langPOST ~ IQ.ver.cen + avg.IQ.ver.cen + (IQ.ver.cen | schoolNR)
- Data: bdf
- AIC BIC logLik deviance REMLdev
- 15232 15272 -7609 15214 15218
-Random effects:
- Groups Name Variance Std.Dev. Corr
- schoolNR (Intercept) 8.076 2.8418
- IQ.ver.cen 0.208 0.4561 -0.642
- Residual 41.350 6.4304
-Number of obs: 2287, groups: schoolNR, 131
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) 40.7496 0.2881 141.5
-IQ.ver.cen 2.4598 0.0836 29.4
-avg.IQ.ver.cen 1.4089 0.3237 4.4
-
-Correlation of Fixed Effects:
- (Intr) IQ.vr.
-IQ.ver.cen -0.274
-avg.IQ.vr.c 0.029 -0.213
-> ##
-> anova(fm01, fm02)
-Data: bdf
-Models:
-fm01: langPOST ~ IQ.ver.cen + avg.IQ.ver.cen + (1 | schoolNR)
-fm02: langPOST ~ IQ.ver.cen + avg.IQ.ver.cen + (IQ.ver.cen | schoolNR)
- Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
-fm01 5 15238 15266 -7614
-fm02 7 15228 15268 -7607 14.01 2 0.000908
-> cat('Time elapsed: ', (.pt <- proc.time()),'\n') # "stats"
-Time elapsed: 7.259 0.141 7.552 0 0
->
-> ## egsingle ----------- Data ---------------------
-> (fm1 <- lmer(math ~ year + (1|childid) + (1|schoolid), egsingle))
-Linear mixed model fit by REML
-Formula: math ~ year + (1 | childid) + (1 | schoolid)
- Data: egsingle
- AIC BIC logLik deviance REMLdev
- 16769 16804 -8380 16747 16759
-Random effects:
- Groups Name Variance Std.Dev.
- childid (Intercept) 0.6699 0.8185
- schoolid (Intercept) 0.1869 0.4323
- Residual 0.3470 0.5891
-Number of obs: 7230, groups: childid, 1721; schoolid, 60
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) -0.7805 0.0611 -12.8
-year 0.7461 0.0054 138.3
-
-Correlation of Fixed Effects:
- (Intr)
-year -0.031
-> (fm2 <- lmer(math ~ year + (1|childid) + (year|schoolid), egsingle))
-Linear mixed model fit by REML
-Formula: math ~ year + (1 | childid) + (year | schoolid)
- Data: egsingle
- AIC BIC logLik deviance REMLdev
- 16496 16545 -8241 16472 16482
-Random effects:
- Groups Name Variance Std.Dev. Corr
- childid (Intercept) 0.67230 0.8199
- schoolid (Intercept) 0.16683 0.4084
- year 0.01158 0.1076 0.443
- Residual 0.32452 0.5697
-Number of obs: 7230, groups: childid, 1721; schoolid, 60
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) -0.7771 0.0582 -13.3
-year 0.7630 0.0153 49.8
-
-Correlation of Fixed Effects:
- (Intr)
-year 0.358
-> (fm3 <- lmer(math ~ year + (year|childid) + (1|schoolid), egsingle))
-Linear mixed model fit by REML
-Formula: math ~ year + (year | childid) + (1 | schoolid)
- Data: egsingle
- AIC BIC logLik deviance REMLdev
- 16531 16580 -8259 16505 16517
-Random effects:
- Groups Name Variance Std.Dev. Corr
- childid (Intercept) 0.64811 0.8051
- year 0.02152 0.1467 0.463
- schoolid (Intercept) 0.15369 0.3920
- Residual 0.30120 0.5488
-Number of obs: 7230, groups: childid, 1721; schoolid, 60
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) -0.79307 0.05609 -14.1
-year 0.74716 0.00636 117.5
-
-Correlation of Fixed Effects:
- (Intr)
-year 0.065
-> (fm4 <- lmer(math ~ year + (year|childid) + (year|schoolid), egsingle))
-Linear mixed model fit by REML
-Formula: math ~ year + (year | childid) + (year | schoolid)
- Data: egsingle
- AIC BIC logLik deviance REMLdev
- 16355 16417 -8168 16326 16337
-Random effects:
- Groups Name Variance Std.Dev. Corr
- childid (Intercept) 0.64047 0.8003
- year 0.01126 0.1061 0.551
- schoolid (Intercept) 0.16856 0.4106
- year 0.01126 0.1061 0.398
- Residual 0.30143 0.5490
-Number of obs: 7230, groups: childid, 1721; schoolid, 60
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) -0.7792 0.0583 -13.4
-year 0.7631 0.0154 49.6
-
-Correlation of Fixed Effects:
- (Intr)
-year 0.356
-> ##
-> anova(fm1, fm2, fm3, fm4)
-Data: egsingle
-Models:
-fm1: math ~ year + (1 | childid) + (1 | schoolid)
-fm2: math ~ year + (1 | childid) + (year | schoolid)
-fm3: math ~ year + (year | childid) + (1 | schoolid)
-fm4: math ~ year + (year | childid) + (year | schoolid)
- Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
-fm1 5 16757 16791 -8374
-fm2 7 16486 16534 -8236 275.2 2 <2e-16
-fm3 7 16519 16567 -8253 0.0 0 <2e-16
-fm4 9 16344 16406 -8163 179.0 2 <2e-16
-> cat('Time elapsed: ', {.ot <- .pt; (.pt <- proc.time()) - .ot},'\n') # "stats"
-Time elapsed: 7.913 0.039 7.954 0 0
->
-> ## Early -------------- Data ---------------------
-> Early$tos <- Early$age - 0.5
-> (fm1E <- lmer(cog ~ tos * trt + (tos|id), Early))
-Linear mixed model fit by REML
-Formula: cog ~ tos * trt + (tos | id)
- Data: Early
- AIC BIC logLik deviance REMLdev
- 2375 2405 -1179 2370 2359
-Random effects:
- Groups Name Variance Std.Dev. Corr
- id (Intercept) 165.51 12.865
- tos 10.32 3.213 -1.000
- Residual 75.49 8.689
-Number of obs: 309, groups: id, 103
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) 118.41 2.76 43.0
-tos -21.13 1.89 -11.2
-trtY 4.22 3.67 1.1
-tos:trtY 5.27 2.52 2.1
-
-Correlation of Fixed Effects:
- (Intr) tos trtY
-tos -0.819
-trtY -0.750 0.615
-tos:trtY 0.615 -0.750 -0.819
->
-> ## Exam --------------- Data ---------------------
-> (fm05 <- lmer(normexam ~ standLRT + sex + schgend + (1|school), Exam))
-Linear mixed model fit by REML
-Formula: normexam ~ standLRT + sex + schgend + (1 | school)
- Data: Exam
- AIC BIC logLik deviance REMLdev
- 9362 9406 -4674 9326 9348
-Random effects:
- Groups Name Variance Std.Dev.
- school (Intercept) 0.08583 0.2930
- Residual 0.56253 0.7500
-Number of obs: 4059, groups: school, 65
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) -0.00105 0.05556 0.0
-standLRT 0.55976 0.01245 45.0
-sexM -0.16739 0.03410 -4.9
-schgendboys 0.17769 0.11346 1.6
-schgendgirls 0.15900 0.08939 1.8
-
-Correlation of Fixed Effects:
- (Intr) stnLRT sexM schgndb
-standLRT -0.014
-sexM -0.316 0.061
-schgendboys -0.395 -0.003 -0.145
-schgendgrls -0.622 0.009 0.197 0.245
->
-> ## Chem97 ------------- Data ---------------------
-> (fm06 <- lmer(score ~ gcsecnt + (1|school) + (1|lea), Chem97))
-Linear mixed model fit by REML
-Formula: score ~ gcsecnt + (1 | school) + (1 | lea)
- Data: Chem97
- AIC BIC logLik deviance REMLdev
- 141707 141749 -70848 141686 141697
-Random effects:
- Groups Name Variance Std.Dev.
- school (Intercept) 1.16620 1.0799
- lea (Intercept) 0.01478 0.1216
- Residual 5.15420 2.2703
-Number of obs: 31022, groups: school, 2410; lea, 131
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) 5.6354 0.0312 180
-gcsecnt 2.4726 0.0169 146
-
-Correlation of Fixed Effects:
- (Intr)
-gcsecnt 0.058
->
-> cat('Time elapsed: ', {.ot <- .pt; (.pt <- proc.time()) - .ot},'\n') # "stats"
-Time elapsed: 2.508 0.051 2.559 0 0
->
-> ## Hsb82 -------------- Data ---------------------
-> lmer(mAch ~ meanses*cses + sector*cses + (cses|school), Hsb82)
-Linear mixed model fit by REML
-Formula: mAch ~ meanses * cses + sector * cses + (cses | school)
- Data: Hsb82
- AIC BIC logLik deviance REMLdev
- 46524 46592 -23252 46496 46504
-Random effects:
- Groups Name Variance Std.Dev. Corr
- school (Intercept) 2.3796 1.5426
- cses 0.1012 0.3182 0.391
- Residual 36.7212 6.0598
-Number of obs: 7185, groups: school, 160
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) 12.128 0.199 60.9
-meanses 5.333 0.369 14.4
-cses 2.945 0.156 18.9
-sectorCatholic 1.227 0.306 4.0
-meanses:cses 1.039 0.299 3.5
-cses:sectorCatholic -1.643 0.240 -6.9
-
-Correlation of Fixed Effects:
- (Intr) meanss cses sctrCt mnss:c
-meanses 0.256
-cses 0.075 0.019
-sectorCthlc -0.699 -0.356 -0.052
-meanses:css 0.019 0.074 0.293 -0.026
-css:sctrCth -0.052 -0.027 -0.696 0.077 -0.351
->
-> ## Oxford ------------- Data ---------------------
-> (fm07 <- lmer(height ~ age + I(age^2) + I(age^3) + I(age^4) +
-+ (age + I(age^2)|Subject), data = Oxboys))
-Linear mixed model fit by REML
-Formula: height ~ age + I(age^2) + I(age^3) + I(age^4) + (age + I(age^2) | Subject)
- Data: Oxboys
- AIC BIC logLik deviance REMLdev
- 652 693 -314 625 628
-Random effects:
- Groups Name Variance Std.Dev. Corr
- Subject (Intercept) 64.0345 8.0022
- age 2.8642 1.6924 0.614
- I(age^2) 0.6743 0.8212 0.215 0.658
- Residual 0.2174 0.4662
-Number of obs: 234, groups: Subject, 26
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) 149.019 1.570 94.9
-age 6.174 0.357 17.3
-I(age^2) 1.128 0.351 3.2
-I(age^3) 0.454 0.162 2.8
-I(age^4) -0.377 0.300 -1.3
-
-Correlation of Fixed Effects:
- (Intr) age I(g^2) I(g^3)
-age 0.572
-I(age^2) 0.076 0.264
-I(age^3) -0.001 -0.340 0.025
-I(age^4) 0.021 0.016 -0.857 -0.021
-> (fm08 <- lmer(height ~ poly(age,4) +
-+ (age + I(age^2)|Subject), data = Oxboys))
-Linear mixed model fit by REML
-Formula: height ~ poly(age, 4) + (age + I(age^2) | Subject)
- Data: Oxboys
- AIC BIC logLik deviance REMLdev
- 641 682 -308 625 617
-Random effects:
- Groups Name Variance Std.Dev. Corr
- Subject (Intercept) 64.0346 8.0022
- age 2.8642 1.6924 0.614
- I(age^2) 0.6743 0.8212 0.215 0.658
- Residual 0.2174 0.4662
-Number of obs: 234, groups: Subject, 26
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) 149.520 1.590 94.0
-poly(age, 4)1 64.541 3.328 19.4
-poly(age, 4)2 4.203 1.024 4.1
-poly(age, 4)3 1.291 0.466 2.8
-poly(age, 4)4 -0.585 0.466 -1.3
-
-Correlation of Fixed Effects:
- (Intr) p(,4)1 p(,4)2 p(,4)3
-poly(ag,4)1 0.631
-poly(ag,4)2 0.230 0.583
-poly(ag,4)3 0.000 0.000 0.000
-poly(ag,4)4 0.000 0.000 0.000 0.000
-> anova(fm07, fm08)
-Data: Oxboys
-Models:
-fm07: height ~ age + I(age^2) + I(age^3) + I(age^4) + (age + I(age^2) |
-fm07: Subject)
-fm08: height ~ poly(age, 4) + (age + I(age^2) | Subject)
- Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
-fm07 12 649.4 690.8 -312.7
-fm08 12 649.4 690.8 -312.7 1.68e-06 0 <2e-16
-> stopifnot(all.equal(logLik(fm07, REML=FALSE),
-+ logLik(fm08, REML=FALSE)))
-> cat('Time elapsed: ', {.ot <- .pt; (.pt <- proc.time()) - .ot},'\n') # "stats"
-Time elapsed: 1.259 0 1.259 0 0
->
-> ## ScotsSec ----------- Data ---------------------
-> cntr <- list()
-> (fmS1 <- lmer(attain ~ verbal * sex + (1|primary) + (1|second), ScotsSec,
-+ verbose = 1))
- 0: 14876.878: 0.338963 0.121450
- 1: 14869.350: 0.271130 0.0962183
- 2: 14869.120: 0.234540 0.0337758
- 3: 14868.448: 0.257104 0.0405381
- 4: 14868.361: 0.249359 0.0627845
- 5: 14868.329: 0.253832 0.0616944
- 6: 14868.328: 0.255792 0.0575285
- 7: 14868.326: 0.254891 0.0578338
- 8: 14868.325: 0.254282 0.0585648
- 9: 14868.325: 0.254614 0.0587516
- 10: 14868.325: 0.254463 0.0591014
- 11: 14868.325: 0.254509 0.0589167
- 12: 14868.325: 0.254490 0.0588844
-Linear mixed model fit by REML
-Formula: attain ~ verbal * sex + (1 | primary) + (1 | second)
- Data: ScotsSec
- AIC BIC logLik deviance REMLdev
- 14882 14925 -7434 14843 14868
-Random effects:
- Groups Name Variance Std.Dev.
- primary (Intercept) 0.27545 0.5248
- second (Intercept) 0.01475 0.1214
- Residual 4.25311 2.0623
-Number of obs: 3435, groups: primary, 148; second, 19
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) 5.91473 0.07678 77.0
-verbal 0.15836 0.00379 41.8
-sexF 0.12155 0.07241 1.7
-verbal:sexF 0.00259 0.00539 0.5
-
-Correlation of Fixed Effects:
- (Intr) verbal sexF
-verbal 0.177
-sexF -0.482 -0.178
-verbal:sexF -0.122 -0.680 0.161
-> #(fmS2 <- lmer(attain ~ verbal * sex + (1|primary) + (1|second), ScotsSec,
-> # control = c(cntr, list(niterEM = 40))))
-> ## fmS1 and fmS2 should be essentially identical when optimizing with nlminb
-> ## The fits are substantially different when optimizing with optim
-> ##
-> (fmS3 <- lmer(attain ~ verbal + sex + (1|primary) + (1|second), ScotsSec))
-Linear mixed model fit by REML
-Formula: attain ~ verbal + sex + (1 | primary) + (1 | second)
- Data: ScotsSec
- AIC BIC logLik deviance REMLdev
- 14872 14909 -7430 14843 14860
-Random effects:
- Groups Name Variance Std.Dev.
- primary (Intercept) 0.27626 0.5256
- second (Intercept) 0.01449 0.1204
- Residual 4.25195 2.0620
-Number of obs: 3435, groups: primary, 148; second, 19
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) 5.91927 0.07615 77.7
-verbal 0.15959 0.00278 57.5
-sexF 0.11597 0.07146 1.6
-
-Correlation of Fixed Effects:
- (Intr) verbal
-verbal 0.130
-sexF -0.473 -0.095
-> (fmS4 <- lmer(attain ~ verbal + sex + (1|primary) + (sex|second), ScotsSec))
-Linear mixed model fit by REML
-Formula: attain ~ verbal + sex + (1 | primary) + (sex | second)
- Data: ScotsSec
- AIC BIC logLik deviance REMLdev
- 14864 14913 -7424 14832 14848
-Random effects:
- Groups Name Variance Std.Dev. Corr
- primary (Intercept) 0.26860 0.5183
- second (Intercept) 0.04639 0.2154
- sexF 0.14583 0.3819 -0.798
- Residual 4.21851 2.0539
-Number of obs: 3435, groups: primary, 148; second, 19
-
-Fixed effects:
- Estimate Std. Error t value
-(Intercept) 5.92741 0.08660 68.4
-verbal 0.15959 0.00277 57.6
-sexF 0.09844 0.11454 0.9
-
-Correlation of Fixed Effects:
- (Intr) verbal
-verbal 0.115
-sexF -0.624 -0.060
-> ##
-> anova(fmS1, fmS3, fmS4)
-Data: ScotsSec
-Models:
-fmS3: attain ~ verbal + sex + (1 | primary) + (1 | second)
-fmS1: attain ~ verbal * sex + (1 | primary) + (1 | second)
-fmS4: attain ~ verbal + sex + (1 | primary) + (sex | second)
- Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
-fmS3 6 14855 14892 -7421
-fmS1 7 14857 14900 -7421 0.23 1 0.63171
-fmS4 8 14848 14897 -7416 10.46 1 0.00122
->
-> cat('Time elapsed: ', {.ot <- .pt; (.pt <- proc.time()) - .ot},'\n') # "stats"
-Time elapsed: 1.241 0.005 1.247 0 0
->
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