问题
I am using the 'MuMIn' package in R to select models and calculate effect sizes of the input variables (rain, brk, onset, wid). To make my effect size comparable between variables, I standardised them using standardize function in arm package. Here is the code that I am following:
For reference, please refer to the appendix of this paper: http://onlinelibrary.wiley.com/doi/10.1111/j.1420-9101.2010.02210.x/full Grueber et al. 2011: Multimodel inference in ecology and evolution: challenges and solutions
data1<-read.csv("data.csv",header=TRUE) #reads the data
global.model<-lmer(yld.res ~ rain + brk + onset + wid + (1|state),data=data1,REML="FALSE") # prepares a global model
stdz.model <- standardize(global.model,standardize.y = FALSE) # standardise the input varaibles
model.set <- dredge(stdz.model) ### generates the full submodel set
top.models <- get.models(model.set, subset= delta<2) # selects models with delta AIC <2
model.avg(top.models) # calculates the average effect size of input variables
Here is the result of model.avg(top.models) which gives the average effect size of each input variable
Coefficients:
(Intercept) brk rain wid onset
subset -4.281975e-14 -106.0919 51.54688 39.82837 35.68766
I read around how the standardize function works- subtracts mean and divides by 2SD.
My question is this: Since I have standardised the input variables, should not the effect sizes be between -1 to 1? or the effect size which the output shows is correct?
Please advise
Thanks a lot
回答1:
This is more of a statistical question than a programming question, but: you've only standardized the predictor variables, not the response variable (you specified standardize.y=FALSE); therefore, each of your coefficients represents the expected change of the response (in the response's units!) per 2 SD change in the predictor. If the range of the response is large (as it must be in your example), then there could be a very large change. For example, if I were analyzing the change in elephant weight measured in milligrams, I could expect very large changes in the response for reasonably small changes in the predictors (e.g. sex, age, food availability). You should probably use standardize.y=TRUE if you want truly nondimensional/unitless effect sizes. Even nondimensional effects aren't necessarily constrained to be between -1 and +1, but it would be surprising for them to be so large.
By the way, I think your standardize function comes from the arm package, not from MuMIn (library("sos"); findFn("standardize",sortby="Function)).
来源：https://stackoverflow.com/questions/25059697/standardisation-in-mumin-package-in-r