[R] Extracting random parameters from summary lme and lmer
Chuck Cleland
ccleland at optonline.net
Tue Jul 31 12:58:21 CEST 2007
Rense Nieuwenhuis wrote:
> LS,
>
> I'm estimating multilevel regression models, using the lme-function
> from the nlme-package. Let's say that I estimated a model and stored
> it inside the object named 'model'. The summary of that model is
> shown below:
>
> Using summary(model)$tTable , I receive the following output:
>
> > summary(model)$tTable
> Value Std.Error DF t-value p-value
> (Intercept) 0.23268607 0.09350662 3990 2.4884449 1.287080e-02
> sexM -0.15338225 0.03169762 3990 -4.8389206 1.354802e-06
> standLRT 0.38593558 0.01677195 3990 23.0107762 4.005182e-110
> vrmid 50% 0.07606394 0.09389376 61 0.8101064 4.210281e-01
> vrtop 25% 0.24561327 0.10483374 61 2.3428838 2.241317e-02
> intakemid 50% -0.41469716 0.03177240 3990 -13.0521199 3.698344e-38
> intaketop 25% -0.75920783 0.05357980 3990 -14.1696648 1.666780e-44
> typeSngl 0.15680532 0.07173835 61 2.1857949 3.267903e-02
>
>
> All looks fine to me. The output above is simply a section from the
> full summary shown below. Now, I want to extract from the summary (or
> the full model) the part stating the random parameters. More
> specifically, I want to extract from the summary the following:
>
> (Intercept) 0.2869401 (Intr)
> typeSngl 0.2791040 -0.617
> Residual 0.7302233
>
> How could this be done, and how can the same be done using the lmer-
> function from the lme4-package?
?VarCorr
> library(nlme)
> fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age
> summary(fm1)
Linear mixed-effects model fit by REML
Data: Orthodont
AIC BIC logLik
454.6367 470.6173 -221.3183
Random effects:
Formula: ~age | Subject
Structure: General positive-definite
StdDev Corr
(Intercept) 2.3270339 (Intr)
age 0.2264276 -0.609
Residual 1.3100399
Fixed effects: distance ~ age
Value Std.Error DF t-value p-value
(Intercept) 16.761111 0.7752461 80 21.620375 0
age 0.660185 0.0712533 80 9.265334 0
Correlation:
(Intr)
age -0.848
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-3.223106017 -0.493760867 0.007316632 0.472151090 3.916032743
Number of Observations: 108
Number of Groups: 27
> VarCorr(fm1)
Subject = pdSymm(age)
Variance StdDev Corr
(Intercept) 5.41508688 2.3270339 (Intr)
age 0.05126947 0.2264276 -0.609
Residual 1.71620459 1.3100399
> library(lme4)
> (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
Linear mixed-effects model fit by REML
Formula: Reaction ~ Days + (Days | Subject)
Data: sleepstudy
AIC BIC logLik MLdeviance REMLdeviance
1754 1770 -871.8 1752 1744
Random effects:
Groups Name Variance Std.Dev. Corr
Subject (Intercept) 610.835 24.7151
Days 35.056 5.9208 0.067
Residual 655.066 25.5943
number of obs: 180, groups: Subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 251.405 6.820 36.86
Days 10.467 1.546 6.77
Correlation of Fixed Effects:
(Intr)
Days -0.137
> VarCorr(fm1)
$Subject
2 x 2 Matrix of class "dpoMatrix"
(Intercept) Days
(Intercept) 610.834546 9.738707
Days 9.738707 35.056337
attr(,"sc")
scale
25.59426
> Thanks for the effort,
>
> Rense Nieuwenhuis
>
> Linear mixed-effects model fit by REML
> Data: Exam
> AIC BIC logLik
> 9158.56 9234.241 -4567.28
>
> Random effects:
> Formula: ~type | school
> Structure: General positive-definite, Log-Cholesky parametrization
> StdDev Corr
> (Intercept) 0.2869401 (Intr)
> typeSngl 0.2791040 -0.617
> Residual 0.7302233
>
> Fixed effects: normexam ~ sex + standLRT + vr + intake + type
> Value Std.Error DF t-value p-value
> (Intercept) 0.2326861 0.09350662 3990 2.488445 0.0129
> sexM -0.1533822 0.03169762 3990 -4.838921 0.0000
> standLRT 0.3859356 0.01677195 3990 23.010776 0.0000
> vrmid 50% 0.0760639 0.09389376 61 0.810106 0.4210
> vrtop 25% 0.2456133 0.10483374 61 2.342884 0.0224
> intakemid 50% -0.4146972 0.03177240 3990 -13.052120 0.0000
> intaketop 25% -0.7592078 0.05357980 3990 -14.169665 0.0000
> typeSngl 0.1568053 0.07173835 61 2.185795 0.0327
> Correlation:
> (Intr) sexM stnLRT vrm50% vrt25% int50% int25%
> sexM -0.201
> standLRT -0.125 0.028
> vrmid 50% -0.742 0.028 -0.035
> vrtop 25% -0.652 0.051 -0.065 0.649
> intakemid 50% -0.246 -0.011 0.541 -0.002 0.007
> intaketop 25% -0.218 -0.018 0.676 0.014 0.013 0.660
> typeSngl -0.421 0.080 0.007 0.033 -0.027 -0.001 0.001
>
> Standardized Within-Group Residuals:
> Min Q1 Med Q3 Max
> -3.59074329 -0.63776965 0.03829878 0.67303837 3.33952680
>
> Number of Observations: 4059
> Number of Groups: 65
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>
--
Chuck Cleland, Ph.D.
NDRI, Inc.
71 West 23rd Street, 8th floor
New York, NY 10010
tel: (212) 845-4495 (Tu, Th)
tel: (732) 512-0171 (M, W, F)
fax: (917) 438-0894
More information about the R-help
mailing list