[R] help with lme

ONKELINX, Thierry Thierry.ONKELINX at inbo.be
Tue Oct 30 10:39:47 CET 2012

Dear Sylvia,

R-sig-mixed-models is a better list for questions about mixed models.

The summary gives you the standard error for the fixed effects. See the output in your mail. E.g. AGQ has a standard error of 0.044

Have a look at http://glmm.wikidot.com/faq, it covers some topics on mixed models.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx at inbo.be

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] Namens Sylvia Opriessnig
Verzonden: dinsdag 30 oktober 2012 9:08
Aan: r-help at R-project.org
Onderwerp: [R] help with lme

Dear Madam or Sir

I am writing you hoping, that you can help me with a problem concerning the output of regressions done with the function lme in R.

I would need the standard deviations for intercepts and predictors, but in the output I can only find those for the intercepts. Could it be, that this is my fault? (I am just a beginner with R and multilevel modeling).

I am sorry to annoy you with this problem but I could not deal with the problem with the help of books, internet or friends. So my hope is that you would be so kind and you find some minutes to look through one of my examples.
I would be deeply greatful.

My R script:

library (nlme)  #Datei laden
randomInterceptDIQAGQ <- lme(NoteD ~ IQ + AGQ, data = Gind, random = ~1|Klnr, method = "ML", na.action = na.exclude) summary (randomInterceptDIQAGQ) intervals (randomInterceptDIQAGQ)

my Output:

Final model, : 2 predictors, no RandomSlope

> randomInterceptDIQAGQ <- lme(NoteD ~ IQ + AGQ, data = Gind, random =
> ~1|Klnr, method = "ML", na.action = na.exclude)

> summary (randomInterceptDIQAGQ)

Linear mixed-effects model fit by maximum likelihood

 Data: Gind

       AIC      BIC    logLik

  943.9653 964.7289 -466.9826

Random effects:

 Formula: ~1 | Klnr

(Intercept)  Residual

StdDev:   0.3208885 0.6210003

 Fixed effects: NoteD ~ IQ + AGQ

Value  Std.Error  DF    t-value p-value

(Intercept)  1.8093739 0.06661912 439  27.159979  0.0000

IQ          -0.1565731 0.04810124 439  -3.255074  0.0012

AGQ         -0.4987539 0.04430031 439 -11.258476  0.0000


(Intr) IQ

IQ   0.001

AGQ  0.005 -0.503

Standardized Within-Group Residuals:

        Min          Q1         Med          Q3         Max

-2.40082246 -0.62266936 -0.07491225  0.54494014  3.77426037

Number of Observations: 470

Number of Groups: 29

With best regards from Austria
Sylvia Opriessnig

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