Hi Thierry, Thanks for the input. Ive attached some links to qqnorm(resid(x)) plots run for varying data. Plot 1 (link below) (based on lmer code) is characteristic of all my plots when using my raw data, i.e., showing positive skew to a greater or lesser extent than this example. I identified the response variable as key to driving this and therefore tried log transforming my response variable. Plot 2 is the same raw data and lmer coding, but with a log transformed response variable. It now shows slight negative skew and if anything is worse. Plot 3 is from my glmer coding I proposed in my first message (the two former used the lmer coding) and also my raw untransformed response or predictors. This looks lots better. A SW test is also not significant (p=0.67). This is actually based on using "family=poisson(link=log)" as ive now read that the "family=" aspect only relates to the response, not predictors ...Id be interested in your thoughts. Plot 1 http://s166.photobucket.com/user/michaeljackson1972/media/Plot1_zps1e8eb440.png.html Plot 2 http://s166.photobucket.com/user/michaeljackson1972/media/Plot2_zpsa5e555b8.png.html Plot 3 http://s166.photobucket.com/user/michaeljackson1972/media/Plot3_zpsf4a5b638.png.html Thanks again, Mike .............. PhD Candidate Centre for Biodiversity and Restoration Ecology Room KK 411 Kirk Building Kelburn Parade Wellington 6012
lmer or glmer?
3 messages · Michael Jackson, Ken Beath, ONKELINX, Thierry
Rather than using poisson you should use quasi poisson, as the data that you have is not counts so you can't assume the fixed relationship between mean and variance that Poisson requires. You should also look at the residuals versus fitted values as these will indicate whether the increasing variance with mean from a Poisson or quasi Poisson is correct. The spread of the residuals should look fairly constant. On 13 January 2015 at 06:41, Michael Jackson <Michael.Jackson at vuw.ac.nz> wrote:
Hi Thierry, Thanks for the input. Ive attached some links to qqnorm(resid(x)) plots run for varying data. Plot 1 (link below) (based on lmer code) is characteristic of all my plots when using my raw data, i.e., showing positive skew to a greater or lesser extent than this example. I identified the response variable as key to driving this and therefore tried log transforming my response variable. Plot 2 is the same raw data and lmer coding, but with a log transformed response variable. It now shows slight negative skew and if anything is worse. Plot 3 is from my glmer coding I proposed in my first message (the two former used the lmer coding) and also my raw untransformed response or predictors. This looks lots better. A SW test is also not significant (p=0.67). This is actually based on using "family=poisson(link=log)" as ive now read that the "family=" aspect only relates to the response, not predictors ...Id be interested in your thoughts. Plot 1 http://s166.photobucket.com/user/michaeljackson1972/media/Plot1_zps1e8eb440.png.html Plot 2 http://s166.photobucket.com/user/michaeljackson1972/media/Plot2_zpsa5e555b8.png.html Plot 3 http://s166.photobucket.com/user/michaeljackson1972/media/Plot3_zpsf4a5b638.png.html Thanks again, Mike .............. PhD Candidate Centre for Biodiversity and Restoration Ecology Room KK 411 Kirk Building Kelburn Parade Wellington 6012 [[alternative HTML version deleted]]
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Dear Michael, Neither of the qq plots look terribly problematic. But don't just look at qq plots! Plot the residuals against the available covariates and see if there is any pattern. Choose a distribution family based on the properties of the response. Poisson assumes non-negative integer values. So don't use Poisson if the response is continuous. You will need to tell us more about the response if you need help on that. Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium + 32 2 525 02 51 + 32 54 43 61 85 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 ________________________________________ Van: R-sig-mixed-models [r-sig-mixed-models-bounces at r-project.org] namens Michael Jackson [Michael.Jackson at vuw.ac.nz] Verzonden: maandag 12 januari 2015 20:41 Aan: r-sig-mixed-models at r-project.org Onderwerp: Re: [R-sig-ME] lmer or glmer? Hi Thierry, Thanks for the input. Ive attached some links to qqnorm(resid(x)) plots run for varying data. Plot 1 (link below) (based on lmer code) is characteristic of all my plots when using my raw data, i.e., showing positive skew to a greater or lesser extent than this example. I identified the response variable as key to driving this and therefore tried log transforming my response variable. Plot 2 is the same raw data and lmer coding, but with a log transformed response variable. It now shows slight negative skew and if anything is worse. Plot 3 is from my glmer coding I proposed in my first message (the two former used the lmer coding) and also my raw untransformed response or predictors. This looks lots better. A SW test is also not significant (p=0.67). This is actually based on using "family=poisson(link=log)" as ive now read that the "family=" aspect only relates to the response, not predictors ...Id be interested in your thoughts. Plot 1 http://s166.photobucket.com/user/michaeljackson1972/media/Plot1_zps1e8eb440.png.html Plot 2 http://s166.photobucket.com/user/michaeljackson1972/media/Plot2_zpsa5e555b8.png.html Plot 3 http://s166.photobucket.com/user/michaeljackson1972/media/Plot3_zpsf4a5b638.png.html Thanks again, Mike .............. PhD Candidate Centre for Biodiversity and Restoration Ecology Room KK 411 Kirk Building Kelburn Parade Wellington 6012 _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models Disclaimer<https://www.inbo.be/nl/disclaimer-mailberichten-van-het-inbo>