##### ##### #####read in appropriate libraries library(AICcmodavg) library(lme4) library(lattice) library(mgcv) library(gamm4) library(car) library(cvTools) library(influence.ME) library(sp) library(rgdal) library(raster) library(maptools) library(ape) library(geosphere) library(geoR) library(gstat) #read in data Ponded <- read.csv("BinomialNonRowcropsExtremeChangePresent3.csv", header = TRUE) head(Ponded) nrow(Ponded) ##### ##### #####set nominal variables as factors Ponded$Wetland = factor(Ponded$Wetland) Ponded$Wettype = factor(Ponded$Wettype) Ponded$Year = factor(Ponded$Year) ##### ##### #####make predictions for ponded area manually #with original data Ponded$Pred.Log.Meters.ManualFixedOnly <- NA for(i in 1:nrow(Ponded)){ Ponded$Pred.Log.Meters.ManualFixedOnly[[i]] = 8.25802+ + 1.22951 * Ponded$SemiPerm[[i]]+ - 0.57479 * Ponded$Temporary[[i]]+ - ((0.72171 * Ponded$log.NewfootPA[[i]] - 0.72171 * -4.38943) / 0.59353)+ + ((0.10567 * Ponded$espro2PostChange[[i]] - 0.10567 * 0.27358) / 0.46648)+ + ((0.26324 * Ponded$log.lsprsPostChange[[i]] - 0.26324 * 5.42392) / 0.29450)+ - ((0.53600 * Ponded$fatmaxmPostChange[[i]] - 0.53600 * 15.97654) / 1.10901)+ + ((0.17197 * Ponded$log.wprsPostChange[[i]] - 0.17197 * 4.24774) / 0.79608)+ + ((0.63935 * Ponded$wtmaxsfPostChange[[i]] - 0.63935 * 25.65612) / 4.46749) } Ponded$Pred.Meters.ManualFixedOnly <- exp(Ponded$Pred.Log.Meters.ManualFixedOnly) #obtain mean flooded area mean(Ponded$Pred.Meters.ManualFixedOnly) #obtain total flooded area sum(Ponded$Pred.Meters.ManualFixedOnly)