LRT tests in lmer
Hi Jarrord, I have tried using MCMCglmm, however the posterior distributions of the majority of the fixed factors straddle 0, which i have read is a problem, likely with the priors. HPDintervals - https://files.me.com/chrismcowen/wqq1lu prior=list(R=list(V=1, fix=1), G=list(G1=list(V=1, nu=0), G2=list(V=1, nu=0))) So i am unsure how to interpret the results, as to ascertain the importance of each factor. Unfortunately i don't know enough about baysian statistics or R to alter my model so the interpretations become clearer. An example lower upper (Intercept) -3.510792767 2.40740650 STOStorage organ -0.299408836 0.23073133 BSUnisexual flower -0.131660436 0.54887912 BSUnisexual plant 0.003566637 0.81742862 PDBiotic 0.054625970 0.72436838 PDMammalia -2.139720264 1.39753939
On 11 Aug 2010, at 16:37, Jarrod Hadfield wrote:
Hi Chris, It is hard to say as it will depend on the fixed effects. In addition its not clear whether such a situation is diagnostic of a problem. Imagine you just have an intercept which is estimated to be exactly zero. The residuals on the data scale will be either 0.5 or -0.5, but this does not imply the model is wrong. Cheers, Jarrod
On 11 Aug 2010, at 15:41, Chris Mcowen wrote:
Thats great thanks, But will this work where you have a binary response variable or will the residuals clump around 1 and 0? Chris On 11 Aug 2010, at 15:31, Ben Bolker wrote: On 10-08-11 10:21 AM, Chris Mcowen wrote:
Dear Ben/Rob.
As far as I can tell, the standard advice is simply to look at the predictions of the model, compare them with the data, and try to spot any systematic patterns in the residuals.
I have plotted the residuals of my model - https://files.me.com/chrismcowen/v586vx I have been made aware that that lmer uses the random effects in its prediction ( Jarrord Hadfield). And this is reflected in the residual plot with the the long lines of equal residuals all belonging to the same family - i.e 200 - 600 is the orchid family and 650-100 is the grass family. So is there a work around with a glmm? Thanks Chris
If you want to do population-level predictions from a GLMM (i.e. setting all random effects to zero), the basic recipe is to (1) construct a model (design) matrix for the desired sets of predictor variables (if you want to the predict the observed data rather than some other set, you can just extract the model matrix from the fitted object); (2) multiply it by the vector of fixed effect coefficients; (3) transform it back to the scale of the observations with the inverse link function. There's an example on p. 6 of http://glmm.wdfiles.com/local--files/examples/Owls.pdf ...
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