interpreting Std. error from glmer output
Dear Christine, Be carefull about that. The SE of a sum is NOT the sums of the SE! But the variance of a sum is the sum of the variances minus the covariance. The easiest option would be to refit the model without intercept if you want the 'total' SE for each treatment. HTH, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and quality assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx at inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey -----Oorspronkelijk bericht----- Van: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Christine Griffiths Verzonden: dinsdag 15 september 2009 18:29 Aan: William Morris CC: r-sig-mixed-models at r-project.org Onderwerp: Re: [R-sig-ME] interpreting Std. error from glmer output Thank you for your help. Because I am using glmer with Binomial family I cannot calculate 95% CIs using the mcmcsamp function and have not been able to find a way to do this. Hence my reason to look at the SE. I probably didn't explain myself clearly enough. What I meant is, is the SE for a fixed effect the difference from the intercept, just as the mean for a fixed effect needs to be calculated as a difference from the intercept? Your definition earlier seemed to support that it is a difference and so I need to calculate the SE by summing the value given with the intercept. --On 15 September 2009 23:32 +1000 William Morris <wkmor1 at gmail.com> wrote:
Well, it depends. It depends on what you mean by deviance, you should clarify this (here
is a start http://en.wikipedia.org/wiki/Deviance_%28statistics%29). In
general, deviance is used as a measure of model fit and usually encountered as a component of Information criteria. Do you need to take the uncertainty (SE) in model estimates into account? It is probably a good idea if you are going to make predictions based on model estimates to also calculate predictions at
the 95CI limits. On 15/09/2009, at 10:26 PM, Christine Griffiths wrote:
Thank you. So to clarify, I do not need to calculate the deviance of the standard error from the intercept standard error, in the way that
I would do for the estimate? Cheers Christine --On 15 September 2009 21:38 +1000 Will Morris <wkmor1 at gmail.com> wrote:
The SE is a measure of the models uncertainty about the parameter
estimates, it takes into account your sample size as well as sample
variance. +_2*SE is usually a good estimate of the 95% confidence
interval. In other words your treatment effect for treatment2 is
probably somewhere between -.6 and -.86.
On Tue, Sep 15, 2009 at 8:05 PM, Christine Griffiths
<Christine.Griffiths at bristol.ac.uk> wrote:
I want to plot my predictions from a model and use the standard
error output as a measure of dispersion as I am unable to calculate
confidence intervals with mcmcsamp as I have a binomial
distribution.
I know that the estimates are deviations from the intercept.
Fixed effects below:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.90836 0.34041 8.544 < 2e-16 ***
treatment2 -0.73507 0.12986 -5.660
1.51e-08
***
treatment3 -1.20052 0.12371 -9.705 < 2e-16 ***
So the estimate for treatment 2 is 2.9 + -0.73. Are standard errors
also deviations from the intercept? i.e. 0.34 + 0.13 for treatment
2?
Many thanks
Christine
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models -- Will Morris Masters of Philosophy candidate Vesk Plant Ecology Lab The School of Botany The University of Melbourne Australia Phone: +61 3 8344 0120 http://www.botany.unimelb.edu.au/vesk/
---------------------- Christine Griffiths PhD student School of Biological Sciences University of Bristol Woodland Road Bristol BS8 1UG Tel: 0117 9287593 Fax 0117 3317985 Christine.Griffiths at bristol.ac.uk http://www.bio.bris.ac.uk/research/mammal/tortoises.html
Will Morris Masters of Philosophy candidate Vesk Plant Ecology Lab The School of Botany The University of Melbourne Australia Phone: +61 3 8344 0120 http://www.botany.unimelb.edu.au/vesk/
---------------------- Christine Griffiths PhD student School of Biological Sciences University of Bristol Woodland Road Bristol BS8 1UG Tel: 0117 9287593 Fax 0117 3317985 Christine.Griffiths at bristol.ac.uk http://www.bio.bris.ac.uk/research/mammal/tortoises.html _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models Druk dit bericht a.u.b. niet onnodig af. Please do not print this message unnecessarily. Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document. The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document.