Another possible solution is to use the spsurvey package, and think of each
edge as a level of stratification and use the grts function to design a
survey of sample points on the edges. An advantage of the grts function is
that it uses a local neighborhood variance estimator, which can produce
Hi All,
I am puzzled by some predictions I am getting from the predict() function
on a raster stack containing a factor. I trained a gbm() model on 25
variables, one of which is a factor. Then, I am using the predict()
function on a raster stack created from a group of gridded ESRI .txt files,
one
Dear Adrian,
even this solution is pretty cool.
Again, thanks to all who suggested me how to do that.
Best
Paolo
Da: Adrian Baddeley
Inviato: venerd� 14 ottobre 2016 02.14
A: Paolo Piras; Rolf Turner
Cc: r-sig-geo; Ege Rubak
Dear all,I'm trying to use MannKendall test to get trend of 10 year aridity
data. Can some one help me to correct this code.
>library(raster)
>library(trend)
>setwd("D:/ClimateData/MK_aridity")
>list <- list.files(pattern = "*.asc")
>RasterStack <- Stack(list)
>test <- calc(r, fun=function(x)