r - Does the varIdent function, used in LME work fine? -

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i glad if me solve problem. have data repeated measurements design, tested reaction of birds (time.dep) before , after infection (exper). have fl (fuel loads, % of lean body mass), fat score , group (experimental vs control) explanatory variables. decided use lme, because distribution of residuals doesn’t deviate normality. there problem homogeneity of residuals. variances of groups “before” , “after” , between fat levels differ (fligner-killeen test, p=0.038 , p=0.01 respectively). 

   ring    group fat time.dep  fl   exper 1 xz13125     e   4     0.36 16.295 before 2 xz13125     e   3     0.32 12.547  after 3 xz13126     e   3     0.28  7.721 before 4 xz13127     c   3     0.32  9.157 before 5 xz13127     c   3     0.40 -1.902  after 6 xz13129     c   4     0.40 10.382 before

after have selected random part of model, random-intercept (~1|ring), have applied weight parameter both “fat” , “exper” (varcomb(varident(form=~1|fat), varident(form=~1|exper)). plot of standardized residuals vs. fitted looks better, still violation of homogeneity these variables (same values in fligner test). do wrong?

a common trap in lme default give raw residuals, i.e. not adjusted of heteroscedasticity (weights) or correlation (correlation) sub-models may have been used. ?residuals.lme:

type: optional character string specifying type of residuals used. if ‘"response"’, default, “raw” residuals (observed - fitted) used; else, if ‘"pearson"’, standardized residuals (raw residuals divided corresponding standard errors) used; else, if ‘"normalized"’, normalized residuals (standardized residuals pre-multiplied inverse square-root factor of estimated error correlation matrix) used. partial matching of arguments used, first character needs provided.

thus if want residuals corrected heteroscedasticity (as included in model) need type="pearson"; if want them corrected correlation, need type="normalized".


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