Dear all,

I am trying to apply the lmer function in R 2.8.0. to some linguistic data, but I am at a loss when it comes to reporting the results (see below). The APA recommendations say that effects should be reported as follows:

F (df1, df2) = ... , p. = ... 

The question is, where do I find all these things? So far I have learned through different sources that df1 and F can be found through using the anova() function (is this correct?), but where do I find df2 and p ? 
I have even bigger problems when my dependent variable has a binomial distribution, because then the anova() and pvals.fnc() functions cannot be applied.

I wonder as well whether there is a commonly approved way of reporting the output of the 'print (xxx.lmer)' and 'xxx.pvals$fixed' commands? I can see that some of the levels of a factor are significantly different from the baseline, and this is of interests for me, but how shal I report it? Or should other tests be applied in order to find the difference between the levels? (and, if yes, what tests?)

Any help/ advice/ references will be greatly appreciated.


Best regards

Liliana

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print (all_v_a_va_vf_vp_vt.lmer , corr = F)
Linear mixed model fit by REML 
Formula: rating ~ verb + angle + verb:angle + verb:type + verb:prec +      verb:fol + (1 | subject) 
   Data: rating_allbegend_no270 
   AIC   BIC logLik deviance REMLdev
 11067 11225  -5507    10941   11015
Random effects:
 Groups   Name        Variance Std.Dev.
 subject  (Intercept) 0.092792 0.30462 
 Residual             1.691374 1.30053 
Number of obs: 3240, groups: subject, 40
Fixed effects:
                               Estimate Std. Error t value
(Intercept)                     4.51944    0.13102   34.49
verbzaobikalia                 -1.42685    0.15830   -9.01
verbzaviva                     -2.82315    0.15830  -17.83
angle180                       -0.88611    0.09694   -9.14
angle360                       -2.84722    0.09694  -29.37
verbzaobikalia:angle180        -0.37778    0.13709   -2.76
verbzaviva:angle180             1.76944    0.13709   12.91
verbzaobikalia:angle360         2.24444    0.13709   16.37
verbzaviva:angle360             4.95833    0.13709   36.17
verbobikalia:typeround          0.23333    0.12466    1.87
verbzaobikalia:typeround       -0.07407    0.12466   -0.59
verbzaviva:typeround            0.35370    0.12466    2.84
verbobikalia:precno_prec       -0.01111    0.09694   -0.11
verbzaobikalia:precno_prec      0.20556    0.09694    2.12
verbzaviva:precno_prec         -0.24722    0.09694   -2.55
verbobikalia:precsmooth_prec   -0.19722    0.09694   -2.03
verbzaobikalia:precsmooth_prec  0.12778    0.09694    1.32
verbzaviva:precsmooth_prec     -0.15833    0.09694   -1.63
verbobikalia:folno_fol         -0.33333    0.09694   -3.44
verbzaobikalia:folno_fol        0.21389    0.09694    2.21
verbzaviva:folno_fol           -0.14722    0.09694   -1.52
verbobikalia:folsmooth_fol     -0.26667    0.09694   -2.75
verbzaobikalia:folsmooth_fol    0.31944    0.09694    3.30
verbzaviva:folsmooth_fol       -0.04167    0.09694   -0.43


> anova (all_v_a_va_vf_vp_vt.lmer )
Analysis of Variance Table
           Df  Sum Sq Mean Sq  F value
verb        2  131.07   65.53  38.7458
angle       2  136.09   68.05  40.2310
verb:angle  4 2489.53  622.38 367.9741
verb:type   3   30.62   10.21   6.0350
verb:prec   6   27.89    4.65   2.7478
verb:fol    6   45.62    7.60   4.4952


> all_v_a_va_vf_vp_vt.pvals 
$fixed
                               Estimate MCMCmean HPD95lower HPD95upper  pMCMC Pr(>|t|)
(Intercept)                      4.5194   4.5198     4.2530     4.7685 0.0001   0.0000
verbzaobikalia                  -1.4269  -1.4253    -1.7338    -1.1122 0.0001   0.0000
verbzaviva                      -2.8231  -2.8247    -3.1252    -2.5124 0.0001   0.0000
angle180                        -0.8861  -0.8862    -1.0768    -0.6987 0.0001   0.0000
angle360                        -2.8472  -2.8491    -3.0388    -2.6610 0.0001   0.0000
verbzaobikalia:angle180         -0.3778  -0.3766    -0.6426    -0.1051 0.0056   0.0059
verbzaviva:angle180              1.7694   1.7696     1.5117     2.0402 0.0001   0.0000
verbzaobikalia:angle360          2.2444   2.2465     1.9892     2.5295 0.0001   0.0000
verbzaviva:angle360              4.9583   4.9611     4.7059     5.2427 0.0001   0.0000
verbobikalia:typeround           0.2333   0.2339    -0.0069     0.4895 0.0666   0.0613
verbzaobikalia:typeround        -0.0741  -0.0757    -0.3212     0.1628 0.5384   0.5524
verbzaviva:typeround             0.3537   0.3527     0.0980     0.5983 0.0060   0.0046
verbobikalia:precno_prec        -0.0111  -0.0118    -0.2111     0.1717 0.9012   0.9088
verbzaobikalia:precno_prec       0.2056   0.2050     0.0145     0.3916 0.0344   0.0340
verbzaviva:precno_prec          -0.2472  -0.2456    -0.4378    -0.0615 0.0136   0.0108
verbobikalia:precsmooth_prec    -0.1972  -0.1969    -0.3790     0.0009 0.0412   0.0420
verbzaobikalia:precsmooth_prec   0.1278   0.1278    -0.0743     0.3078 0.1890   0.1875
verbzaviva:precsmooth_prec      -0.1583  -0.1569    -0.3441     0.0336 0.0952   0.1025
verbobikalia:folno_fol          -0.3333  -0.3344    -0.5240    -0.1438 0.0002   0.0006
verbzaobikalia:folno_fol         0.2139   0.2133     0.0254     0.4008 0.0282   0.0274
verbzaviva:folno_fol            -0.1472  -0.1467    -0.3379     0.0420 0.1314   0.1289
verbobikalia:folsmooth_fol      -0.2667  -0.2674    -0.4645    -0.0839 0.0058   0.0060
verbzaobikalia:folsmooth_fol     0.3194   0.3186     0.1252     0.5074 0.0006   0.0010
verbzaviva:folsmooth_fol        -0.0417  -0.0412    -0.2339     0.1437 0.6828   0.6673
$random
    Groups        Name Std.Dev. MCMCmedian MCMCmean HPD95lower HPD95upper
1  subject (Intercept)   0.3046     0.2993   0.3027     0.2235     0.3868
2 Residual               1.3005     1.3011   1.3012     1.2694     1.3329



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