[R] anova statistics in lmer

[Doran, Harold]

The issue is not unresolved within lmer, but with the statistical model itself. SAS gives you alternatives for the ddf such as Kenward-Roger. But, as I have noted on the list before, this makes the assumption that the ratio of the variances follow an F distribution and that the only remaining challenge is to then estimate the ddf. Then, one can get all the p-values you want.

If you believe that is true, then the SAS options will give you some statistics to use--not to say that they are correct, though. 

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Diego Vázquez
> Sent: Monday, May 15, 2006 11:53 AM
> To: r-help at stat.math.ethz.ch
> Subject: [R] anova statistics in lmer
> 
> Dear list members,
> 
> I am new to R and to the R-help list. I am trying to perform 
> a mixed-model analysis using the lmer() function. I have a 
> problem with the output anova table when using the anova() 
> function on the lmer output object: I only get the numerator 
> d.f., the sum of squares and the mean squares, but not the 
> denominator d.f., F statistics and P values.
> Below is a sample output, following D. Bates' SASmixed 
> example in his paper "Fitting linear mixed models in R" 
> (R-News 5: 27-30).
> 
> By reading the R-help archive, I see that this problem has 
> come up before (e.g., 
> http://tolstoy.newcastle.edu.au/R/help/06/04/25013.html).
> What I understand from the replies to this message is that 
> this incomplete output results from some unresolved issues 
> with lmer, and that it is currently not possible to use it to 
> obtain full anova statistics. Is this correct? And is this 
> still unresolved? If so, what is the best current alternative 
> to conduct a mixed model analysis, other than going back to SAS?
> 
> I would greatly appreciate some help.
> 
> Diego
> 
> ----
> 
> Example using SASmixed "HR" data (see D. Bates, "Fitting 
> linear mixed models in R", R-News 5: 27-30)
> 
> > data("HR",package="SASmixed")
> > library(lme4)
> Loading required package: Matrix
> Loading required package: lattice
> 
> Attaching package: 'lattice'
> 
> 
>         The following object(s) are masked from package:Matrix :
> 
>          qqmath
> 
> > (fm1<-lmer(HR~baseHR+Time*Drug+(1|Patient),HR))
> Linear mixed-effects model fit by REML
> Formula: HR ~ baseHR + Time * Drug + (1 | Patient)
>           Data: HR
>       AIC      BIC    logLik MLdeviance REMLdeviance
>  788.6769 810.9768 -386.3384   791.8952     772.6769
> Random effects:
>  Groups   Name        Variance Std.Dev.
>  Patient  (Intercept) 44.541   6.6739
>  Residual             29.780   5.4571
> number of obs: 120, groups: Patient, 24
> 
> Fixed effects:
>              Estimate Std. Error t value
> (Intercept)  33.96209    9.93059  3.4199
> baseHR        0.58819    0.11846  4.9653
> Time        -10.69835    2.42079 -4.4194
> Drugb         3.38013    3.78372  0.8933
> Drugp        -3.77824    3.80176 -0.9938
> Time:Drugb    3.51189    3.42352  1.0258
> Time:Drugp    7.50131    3.42352  2.1911
> 
> Correlation of Fixed Effects:
>            (Intr) baseHR Time   Drugb  Drugp  Tm:Drgb
> baseHR     -0.963
> Time       -0.090  0.000
> Drugb      -0.114 -0.078  0.237
> Drugp      -0.068 -0.125  0.236  0.504
> Time:Drugb  0.064  0.000 -0.707 -0.335 -0.167 Time:Drugp  
> 0.064  0.000 -0.707 -0.167 -0.333  0.500
> > anova(fm1)
> Analysis of Variance Table
>           Df Sum Sq Mean Sq
> baseHR     1 745.99  745.99
> Time       1 752.86  752.86
> Drug       2  86.80   43.40
> Time:Drug  2 143.17   71.58
> 
> 
> --
> Diego Vázquez
> Instituto Argentino de Investigaciones de las Zonas Áridas 
> Centro Regional de Investigaciones Científicas y Tecnológicas 
> CC 507, (5500) Mendoza, Argentina 
> http://www.cricyt.edu.ar/interactio/dvazquez/
> 
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