[R-sig-ME] F and P values for random factors
tom_philippi at nps.gov
Sat Feb 6 01:16:09 CET 2016
A perhaps more comprehensive answer is available in the draft
On Fri, Feb 5, 2016 at 4:05 PM, Alex Fine <abfine at gmail.com> wrote:
> Hi Mahendra,
> You can assess the significance of both fixed and random factors using
> likelihood ratio tests. Say you want to test the significance of predictor
> A in a model with predictors A and B and random slopes for A. You can do:
> full_model = lmer(y ~ A + B)
> model_without_A = lmer(y ~ B)
> anova(full_model, model_without_A)
> The anova() function in this case will return a chi-squared score (df =
> number of predic) and a p-value. The same procedure can be used for random
> effects, e.g.:
> full_model_2 = lmer(y ~ A + B + (1+A | random_thing)
> model_without_random_slope_for_A = lmer(y ~ A + B + (1 | random_thing)
> anova(full_model, model_without_random_slope_for_A)
> This works because the log-likelihoods of nested models, in the limit,
> approximate a chi-squared distribution.
> See: https://en.wikipedia.org/wiki/Likelihood-ratio_test
> Or maybe that wasn't what you were asking at all.
> Also I think you forgot to attach the file.
> Hope that helps!
> On Thu, Feb 4, 2016 at 8:32 PM, Mahendra Dia <diamahendra at gmail.com>
> > Hi.
> > I am reaching you out to learn how to compute F ratio and P values for my
> > experiment where all the factors are treated as random factors.
> > Please see the attached file where I explained my treatments and sample
> > data.
> > I thank you in advance.
> > Sincerely,
> > Mahendra-
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> Alex Fine
> Ph. (336) 302-3251
> web: abfine.github.io/
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