Forgive me if this is an idiot question, but I believed that including a finite population correction parameter in the R survey package should only impact my variance estimates in a stratified sample (simple random sampling within stratum). Yet the addition of fpc seems to slightly modify my point estimate as well...perhaps just an unimportant artifact of calculation method?
I realize this example is very short on details, but I first wanted to confirm that my intuition that the change that I've observed is suspicious...perhaps the warning message is applicable to the difference in mean Height observed (187.751 changes to 186.83)?
dstrat=svydesign(ids=~1,strata = ~stratum, fpc=~pop, data=demo) svymean(~Height+Weight,dstrat,na.rm=T) mean SE Height 65.614 0.3091 Weight 187.751 2.4551
dstrat=svydesign(ids=~1,strata = ~stratum, data=demo) Warning message: In svydesign.default(ids = ~1, strata = ~stratum, data = demo) : No weights or probabilities supplied, assuming equal probability svymean(~Height+Weight,dstrat,na.rm=T) mean SE Height 65.62 0.3019 Weight 186.83 2.6220
Final note: I also used bootstrap estimates for SE (perhaps incorrectly), and arrived at the same point estimates as those above withOUT fpc:
demodrep=svrepdesign(data=demo, type="bootstrap", repweights=W,scale=bootresults$scale,rscale=bootresults$rscales,combined.weights=TRUE)
svymean(~Height+Weight,demodrep,na.rm=T) mean SE Height 65.62 0.4617 Weight 186.83 3.2325
R 3.2.0 Survey package version 3.29-5
Thank you in advance for any insight/pointers