[R-sig-ME] lme varFix under ML fit does not match coefficients standard error

[Ben Bolker]

   From ?summary.lme:

adjustSigma: an optional logical value.  If ‘TRUE’ and the estimation
           method used to obtain ‘object’ was maximum likelihood, the
           residual standard error is multiplied by sqrt(nobs/(nobs -
           npar)), converting it to a REML-like estimate.  This argument
           is only used when a single fitted object is passed to the
           function.  Default is ‘TRUE’.




On 2024-02-18 2:34 p.m., Vaida, Florin via R-sig-mixed-models wrote:
> Hello all,
> 
> This is probably known, but it’s news to me: the standard errors printed for the lme model fit run under method=”ML” are in fact those computed under method=”REML”.
> Is this the expected behavior?  And if so, are there any reasons for this choice?
> Reproducible example below.
> 
> Thanks,
> Florin
> 
> library(nlme)
> fit.reml =  lme(log(conc) ~ Time, random=~1|Subject, data=Glucose, na.action=na.omit, method="REML")
> (se.reml = summary(fit.reml)$tTable[,2])
> ## (Intercept)        Time
> ## 0.019457141 0.005829144
> (se.reml = sqrt(diag(summary(fit.reml)$varFix)))
> ## (Intercept)        Time
> ## 0.019457141 0.005829144
> fit.ml =  lme(log(conc) ~ Time, random=~1|Subject, data=Glucose, na.action=na.omit, method="ML")
> (se.ml = summary(fit.ml)$tTable[,2]) # they match the REML SE’s
> ## (Intercept)        Time
> ## 0.019457141 0.005829144
> (se.ml = sqrt(diag(summary(fit.ml)$varFix))) # they do not match the tTable SE’s
> ## (Intercept)        Time
> ## 0.019405324 0.005813621
> 
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> 
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