[R-sig-ME] General Questions Regarding lmer Output

[Ben Bolker]

Henrik Singmann <henrik.singmann at ...> writes:

> 
> Hi,
> 
> there is a relatively simple way of getting a p-value for the intercept 
> which involves pbkrtest's
> KRmodcomp for obtaining the Kenward-Rogers ddf:
> 
> 1. Fit full model
> 2. Fit model without intercept (e.g., using 0 + model, or model - 1)
> 3. compre both using KRmodcomp.
> 
> For example:
> 
> require(lme4)
> require(pbkrtest)
> 
> fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
> fm2 <- lmer(Reaction ~ 0 +Days + (Days | Subject), sleepstudy)

 Or 

fm2 <- update(fm1, . ~ . - 1)

(equivalent but a more compact statement)

> 
> KRmodcomp(fm1, fm2)
> 
> ## F-test with Kenward-Roger approximation; computing time: 0.25 sec.
> ## large : Reaction ~ Days + (Days | Subject)
> ## small : Reaction ~ 0 + Days + (Days | Subject)
> ##       stat  ndf  ddf F.scaling   p.value
> ## Ftest 1357    1   17         1 < 2.2e-16 ***
> ## ---
> ## Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> 
> Alternatively (and now comes shameless self-promotion) you can 
> use mixed from my afex package which gives
> you p-values for all effects including the intercept using KRmodcomp:
> 

  While I don't think presenting it does any great harm, the test of
the difference of the intercept from zero only rarely says anything
interesting about the data. (The most obvious exceptions are when the
response variable is already normalized or scaled so that the value of
zero is special: e.g., if the response variable is a treatment-control
difference.)  Consider striking a blow for sensibility by leaving out
the p-value for the intercept ...