[R-sig-ME] Packages of variables?
chrismcowen at gmail.com
Tue Apr 5 14:01:24 CEST 2011
I am relatively new to mixed models and models in general so i apologise in advance
I have a binary response, 0,1 and two random effects A & B. I then have a range of predictor values which can be put into groups.
Human - L,M,N,O,P
Environment - Q,R,S,T,U
Spatial - V,W,X
I am interested in asking two questions - Which predictors in each group are the most important
Biological_Model <- lmer(yesno~1+(1|A/B)+C,D,E,F,G,H,I,J,K, family=binomial)
I have constructed a series of pre-dfinied models and compared AIC rather than using stepwise regression. I have done this for all the groups.
I am now interested, and this is where i am struggling, to investigate which of the four groups and combinations therein are most important in determining the response and how much do they each contribute?
I have tried this, based on the results from above
Environment <- cbind(R,S,T)
I have then run through the combinations of the groups of predictors i.e
Human_Environment_model <- lrm(yesno~Human+Environment)
Spatial_Biological_model <- lrm(yesno~Spatial+Biological)
I have compared the r-squared values and not surprisingly the greater the number of packages the better the fit. To counter this i also calculated the AIC value of the various models, and this pretty much agrees.
The idea was to look at how much influence these packages have - can you look at the difference in R-squared values i.e if i have a Biological model with a r-squared value of 0.2 then i add human impact and it goes up to 0.35, can i say the addition of human impact added 0.15 or is this not statistically sensible? Or would it be more sensible to use the AIC values which indicate Human & Environment describe the relationship best?
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