I am running a gam model based on a large dataset with many variables. My response variable is the level of "recruitment" by a herd every fall/autumn. This is calculated by the fawn:female ratio every fall/autumn over a 60 year period.
My problem is that there are many years and study sites where only between 1 - 10 females are recorded. This means that the robustness of the ratio is not trustworthy. For example if one female and one fawn is seen, it has a recruitment of 100%, but if they see one more female, that drops by 50%!
I need to tell the model that years/study sites with smaller sample sizes should be weighted less than those with larger sample sizes as these smaller sample sizes are no doubt affecting the results.
Above is a table of the females observed every year and a histogram of the same.
My model is as follows:
gamFIN <- gam(Fw.FratioFall
~ s(year)
+ s(percentage_woody_coverage)
+ s(kmRoads.km2)
+ s(WELLS_ACTIVEinsideD)
+ s(d3)
+ s(WT_DEER_springsurveys)
+ s(BadlandsCoyote.1000_mi)
+ s(Average_mintemp_winter, BadlandsCoyote.1000_mi)
+ s(BadlandsCoyote.1000_mi, WELLS_ACTIVEinsideD)
+ s(BadlandsCoyote.1000_mi, d3)
+ s(YEAR, bs = "re") + s(StudyArea, bs = "re"), method = "REML", select = T, data = mydata)
How might I tell the model to weight my response variable by the sample sizes they are based on.
