Hi, I have a very basic query (I think).  If I fit a model of the specification given below how many model parameters are there?  Am I correct in thinking that the model parameters are the fixed effects intercept and coefficients (in this case two of them) and also a parameter for within and for between group variance?  So in total 5 fitted parameters? or are there also covariance parameters?.  I am trying to calculate the standard error of the residuals as:

RSE = ((sum(residuals^2))/(N - no.of parameters))

and therefore need to know how many parameters my model actually has.  I've gotten a little confused by the literature definitions of what constitutes a parameter. 

Many thank to anyone who can clarify this for me,


Rachel


model<-lmer((log.mass)~centre.log.dbh+centre.log.height+(1+centre.log.dbh+centre.log.height|species_site),data=allometry_2,REML=T) 

 model summary output table:

Linear mixed model fit by REML 
Formula: (log.mass) ~ centre.log.dbh + centre.log.height + (1 + centre.log.dbh +      centre.log.height | species_site) 
   Data: allometry_2 
   AIC   BIC logLik deviance REMLdev
 234.2 272.4 -107.1    206.2   214.2

Random effects:
 Groups       Name              Variance    Std.Dev.     Corr          
 species_site (Intercept)       0.061636  0.24827                
              centre.log.dbh        0.279362   0.52855   0.575        
              centre.log.height    0.285753    0.53456  -0.441 -0.755 
 Residual                              0.086963  0.29489                
Number of obs: 337, groups: species_site, 18

Fixed effects:
                  Estimate Std. Error t value
(Intercept)        2.59802    0.07532   34.49
centre.log.dbh     1.83208    0.16249   11.28
centre.log.height  0.60963    0.17819    3.42

Correlation of Fixed Effects:
            (Intr) cntr.lg.d
cntr.lg.dbh  0.524          
cntr.lg.hgh -0.515 -0.772   
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