Dear Madam or Sir

Thank you very much for your replies of my former question concerning the calculating of standard deviation/variance in lme.

I would have one further question, maybe anyone can give me an answer of it?

Do you know whether there exists a way to calculate a value similar to R² in R or do you also think, that this does not make sense for mixed linear models?

I am looking forward to your replies and thank you in advance

Yours, sincerely
Sylvia

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

 Correlation:

(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






	[[alternative HTML version deleted]]

