[R] FW: Reading output of a GLMM run in R

[Ben Bolker]

John Kane <jrkrideau <at> inbox.com> writes:

> 
> Can you resend the information in plain text?  
> It looks like you sent it in html format and it is very close to
> completely unreadable.
> 
> John Kane
> Kingston ON Canada

  You've also posted this question at CrossValidated:

http://stats.stackexchange.com/questions/93601/
  reading-the-output-from-a-glmm-run-in-r

(broken URL to make Gmane happy).  Cross-posting between
StackOverflow forums and r mailing lists is not explicitly
prohibited by the mailing list guidelines (since those guidelines
predate StackOverflow), but it has a good chance of wasting
answerers' time (because you get parallel answers in both venues).
At least you should state in each place that you've cross-posted,
with a link.

  As I commented on CrossValidated, you haven't given enough
context (what R package or functions are you using??) to answer
the question.

  You should consider reading http://glmm.wikidot.com/faq

  If you prefer the R mailing lists to CV you should
probably post to r-sig-mixed-models at r-project.org rather than
here.


> 
> > -----Original Message-----
> > From: ruthy_ <at> hotmail.co.uk
> > Sent: Sun, 13 Apr 2014 00:28:43 +0000
> > To: r-help <at> r-project.org
> > Subject: [R] FW: Reading output of a GLMM run in R
> > 
> > 
> > 
> > 
> > 
> > 
> > Hi, I am a complete novice and dummy when it comes to statistics so I
> > apologise in advance... I have been asked to report the results of my
> > GLMMs (I ran two) in a table. This table must state: effect, standard
> > error, test statistic, and P value, for all fixed effects. Unfortunately
> > I am struggling to read my output. The out put is as follows, if anyone
> > would be kind enough to help I would be very grateful and will know for
> > future reference which bit equates to what (also I have been told my
> > degrees of freedom are different for both the tests, could someone
> > explain why this is?). GLMM 1-run for predictors of step length. Response
> > variable = step length. fixed effects = depth and direction threshold.
> > random factor = individual Models: m2: step ~ (1 | ind) m1: step ~ Depth
> > * threshold + (1 | ind) Df AIC BIC logLik deviance Chisq Chi Df
> > Pr(>Chisq) m2 3 373235 373259 -186615 373229 m1 8 373225 373290 -186605
> > 373209 19.767 5 0.001382 ** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01
> > ?*? 0.05 ?.? 0.1 ? ? 1 GLMM 2 -run to investigate potential predictors of
> > PDBA. response variables = depth and step length. fixed effect =
> > direction threshold. random factor = Individual Models: m3: PDBA ~ Depth
> > + (1 | ind) + thresholdepth m2: PDBA ~ step * threshold + Depth *
> > threshold + (1 | ind) Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
> > m3 6 -48205 -48157 24109 -48217 m2 11 -48430 -48341 24226 -48452 235.1 5
> > < 2.2e-16 *** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1
> > ? ? 1 Models: m4: PDBA ~ step + (1 | ind) + step:threshold m2: PDBA ~
> > step * threshold + Depth * threshold + (1 | ind) Df AIC BIC logLik
> > deviance Chisq Chi Df Pr(>Chisq) m4 6 -48206 -48158 24109 -48218 m2 11
> > -48430 -48341 24226 -48452 233.81 5 < 2.2e-16 *** --- Signif. codes: 0
> > ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
> > 	[[alternative HTML version deleted]]
> > 
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