I have two sets of rasters, both with same x,y,z extent. I've made two stacks: stacka and stackb. I want to calculate the Pearson correlation coefficient (PCC) in each grid cell between two stacks along the time line. I've made a simpler example (forgive me with the dumb way of creating rasters)
a1<-c(1,1,1,1,1,1,1,1,NA)
a2<-c(2,2,2,2,1,2,2,NA,2)
a3<-c(3,3,3,3,3,2,NA,3,3)
b1<-c(2,2,2,2,2,2,2,2,2)
b2<-c(3,3,3,3,3,3,3,3,3)
b3<-c(4,4,4,4,4,4,4,4,4)
matrixa1<-matrix(a1,3,3)
matrixa2<-matrix(a2,3,3)
matrixa3<-matrix(a3,3,3)
matrixb1<-matrix(b1,3,3)
matrixb2<-matrix(b2,3,3)
matrixb3<-matrix(b3,3,3)
rastera1<-raster(matrixa1)
rastera2<-raster(matrixa2)
rastera3<-raster(matrixa3)
rasterb1<-raster(matrixb1)
rasterb2<-raster(matrixb2)
rasterb3<-raster(matrixb3)
stacka<-stack(rastera1,rastera2,rastera3)
stackb<-stack(rasterb1,rasterb2,rasterb3)
a_bar<-calc(stacka,mean,na.rm=TRUE)
b_bar<-calc(stackb,mean,na.rm=TRUE)
numerator<-setValues(rastera1,0)
denominator1<-numerator
denominator2<-numerator
for(i in 1:noflayers){
numerator<-numerator+(stacka[[i]]-a_bar)*(stackb[[i]]-b_bar)
denominator1<-denominator1+(stacka[[i]]-a_bar)^2
denominator2<-denominator2+(stackb[[i]]-b_bar)^2
}
pearsoncoeff<-numerator/sqrt(denominator1*denominator2)
In the end I have a raster with each grid cell filled with PCC. The problem is, data a is intermittent, some grids are NA in some layers. So the end product has some blanks. My algorithm spits out "NA" when it encounters NA. I'd need some option like na.rm=TRUE in the calculation, so the output would calculate whatever months have values.
The method I can think of is to use is.na(stacka[[nlayers]][nrows,ncols]==FALSE and find corresponding pair in stackb, but that's on cell basis,which'd take enormous amount of computer time.
I edited Paulo's recommended approach to deal with NAs in the computation and it seems to work fast on a bunch of tests, including the dataset above:
stack.correlation <- function(stack1, stack2, cor.method){
# output template
cor.map <- raster(stack1)
# combine stacks
T12 <- stack(stack1,stack2)
rnlayers=nlayers(T12)
# the function takes a vector, partitions it in half, then correlates
# the two sections, returning the correlation coefficient.
stack.sequence.cor <- function(myvec,na.rm=T){
myvecT1<-myvec[1:(length(myvec)/2)]
myvecT2<-myvec[(length(myvec)/2+1):length(myvec)]
return(cor(myvecT1,myvecT2, method = cor.method, use="complete.obs"))
}
# apply the function above to each cell and write the correlation
# coefficient to the output template.
cor.map <- stackApply(T12, indices = rep(1, rnlayers),
fun = stack.sequence.cor, na.rm = FALSE)
return(cor.map)
}
cor_r=stack.correlation(stacka, stackb, "pearson")