Error in applying calc to rasterbrick with NA vallues

Dear all,

I am sorry for previous email. I am learning how to handle correctly this
list.

On R-sig-Geo Digest, Vol 201, Issue 5  I got a smart solution for my tests
with parallelization.

However, my original data has NA values, and applying the function created
to my data I get some errors.
I have played around with other commands like na.rm, na.action=na.exclude
or reclassifications and none worked.

Could someone give me some herp again?

best wishes,

Jackson
################
library(raster)
library(trend)

r <- raster(ncol=96, nrow=63, vals=runif(63*96))
n <- 672  # number of copies
s <- stack(lapply(1:n, function(i) rz<-stack(r[i]<-(r^2/3))))
s[s < 0.1] <- NA

# Define the function to apply to each pixel; it should return a numeric
or a vector of numerics with always the same lenght
fun <- function(x){
   out <- pettitt.test(x)[3]$estimate
   return(unname(out))
}

# Check that it works
fun(seq(2000))

# Check that it works with calc
s_out <- calc(s, fun = fun)

Error in .calcTest(x[1:5], fun, na.rm, forcefun, forceapply) :
   cannot use this function

# Parallelize with clusterR
beginCluster()
s_out <- clusterR(s, fun=calc, args=list(fun=fun))

[1] "cannot use this function"
attr(,"class")
[1] "snow-try-error" "try-error"
Error in clusterR(s, fun = calc, args = list(fun = fun)) : cluster error

endCluster()
##############

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Hi Jackson,

All the layers of the toy dataset you create in your example are
identical. Is that intentional?
Below I create a more heterogeneous dataset and introduce a few NAs.

library(raster)
library(trend)

set.seed(12)
r <- raster(nrow=10, ncol=10)
s <- lapply(1:200, function(i) setValues(r, rnorm(ncell(r),
sample.int(5,1), 0.5)))
s <- stack(s)

s[s < 0] <- NA

# Visualize that some pixels have NAs and other don't
hasna <- stackApply(s, indices = 1, fun = function(x, na.rm){anyNA(x)})
plot(hasna)

# pettitt.test function does not handle NAs, so you need to add a condition
# to "exclude" vectors that contain NAs
fun <- function(x){
     out <- ifelse(anyNA(x),
                   yes = NA,
                   no = unname(pettitt.test(x)[3]$estimate))
     return(out)
}

# Check that it works
fun(as.vector(s[1])) # first pixel, does not contain NAs
fun(as.vector(s[2])) # second pixel, contains NAs

# Check that it works with calc
s_out <- calc(s, fun = fun)
plot(s_out)


Cheers,
Lo?c

On 05/08/2020 03:59 PM, Jackson Rodrigues wrote:

Dear all,

I am sorry for previous email. I am learning how to handle correctly this
list.

On R-sig-Geo Digest, Vol 201, Issue 5  I got a smart solution for my

tests

with parallelization.

However, my original data has NA values, and applying the function

created

to my data I get some errors.
I have played around with other commands like na.rm, na.action=na.exclude
or reclassifications and none worked.

Could someone give me some herp again?

best wishes,

Jackson
################
library(raster)
library(trend)

r <- raster(ncol=96, nrow=63, vals=runif(63*96))
n <- 672  # number of copies
s <- stack(lapply(1:n, function(i) rz<-stack(r[i]<-(r^2/3))))
s[s < 0.1] <- NA

# Define the function to apply to each pixel; it should return a numeric
or a vector of numerics with always the same lenght
fun <- function(x){
   out <- pettitt.test(x)[3]$estimate
   return(unname(out))
}

# Check that it works
fun(seq(2000))

# Check that it works with calc
s_out <- calc(s, fun = fun)

Error in .calcTest(x[1:5], fun, na.rm, forcefun, forceapply) :
   cannot use this function

# Parallelize with clusterR
beginCluster()
s_out <- clusterR(s, fun=calc, args=list(fun=fun))

[1] "cannot use this function"
attr(,"class")
[1] "snow-try-error" "try-error"
Error in clusterR(s, fun = calc, args = list(fun = fun)) : cluster error

endCluster()
##############

      [[alternative HTML version deleted]]