Explain what mean function and covariance function are being used here.

fit $<-$ spatialProcess$($loc$[$good$,],$ y$[$good$])$

out.full $<-$ predictSurface$($fit$,$ extrap $=$ T$)$

out.poly $<-$ predictSurface$($fit$,$ just.fixed $=$ T$,$ extrap $=$ T$)$

out.spatial $<-$ out.full

out.spatial$z $<-$ out.full$z $-$ out.poly$z

set.panel$(1,3)$

surface$($out$.$full, las $=1,$ xlab $=$ "Lon", ylab $=$ "Lat", col $=$ tim.colors$())$

title$("$Full model"$)$

map$("$state$",$ add $=$ T$)$

surface$($out$.$poly, las $=1,$ xlab $=$ "Lon", ylab $=$ "Lat", col $=$ tim.colors$())$

map$("$state$",$ add $=$ T$)$

title$("$Spatial trend"$)$

surface$($out$.$spatial, las $=1,$ xlab $=$ "Lon", ylab $=$ "Lat", col $=$ tim.colors$())$

map$("$state$",$ add $=$ T$)$

title$("$Spatial error"$)$