[R-sig-ME] (no subject)

[Ben Bolker]

  (please keep r-sig-mixed-models in the Cc:)

  I'm pretty sure that lmer and lm models are commensurate, in case that
helps.  Here's an example rigged to make the random-effects variance
equal to zero, so we can check that the log-likelihoods etc. are identical.

set.seed(101)
dd <- data.frame(y=rnorm(20),x=rnorm(20),f=factor(rep(1:2,10)))
library(lme4)
m1 <- lmer(y~x+(1|f),data=dd,REML=FALSE) ## estimated sigma^2_f=0
m2 <- lm(y~x,data=dd)
all.equal(c(logLik(m1)),c(logLik(m2))) ## TRUE
all.equal(fixef(m1),coef(m2))
anova(m1,m2)


On 2018-08-01 11:41 PM, Peter Paprzycki wrote:
> Thank you. Oh, was just trying to compare my random-effects model to the
> one where my grouping variable (schools) is treated as fixed.
> 
> Peter
> 
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> On Wed, Aug 1, 2018 at 10:32 PM, Ben Bolker <bbolker using gmail.com
> <mailto:bbolker using gmail.com>> wrote:
> 
> 
>       I'm not 100% sure I understand the question, but I think the answer is
>     "no": lmer cannot fit a model that doesn't contain any random effects.
>     Perhaps you can give more context as to why it won't work for you to
>     revert to lm() or plm() in these cases?
> 
>     On 2018-08-01 11:30 PM, Peter Paprzycki wrote:
>     > This is very basic, is there a way to specify in lmer function
>     that I would
>     > like to run my grouping variable as a fixed factor only, without
>     reverting
>     > to lm or plm functions. If one does not specify a random variable,
>     one gets
>     > the error message with lmer function; something that is equivalent
>     to the
>     > statement, "index = "grouping variable", model = "within"" with
>     the plm
>     > function.
>     >
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> 
> Peter Paprzycki, Ph.D.
> Visiting Assistant Professor
> Research Support Center Manager
> 
> Educational Research and Administration
> College of Education and Psychology
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