Summarize
Compute summary statistics for cells, either across layers or between layers (parallel summary).
Because generic functions are used, the method applied is chosen based on the first argument: "x". This means that if r is a SpatRaster, mean(r, 5) will work, but mean(5, r) will not work.
The mean method has an argument "trim" that is ignored.
If pop=TRUE stdev computes the population standard deviation, computed as:
f <- function(x) sqrt(sum((x-mean(x))^2) / length(x))
This is different than the sample standard deviation returned by sd (which uses n-1 as denominator).
## S4 method for signature 'SpatRaster' min(x, ..., na.rm=FALSE) ## S4 method for signature 'SpatRaster' max(x, ..., na.rm=FALSE) ## S4 method for signature 'SpatRaster' range(x, ..., na.rm=FALSE) ## S4 method for signature 'SpatRaster' mean(x, ..., trim=NA, na.rm=FALSE) ## S4 method for signature 'SpatRaster' median(x, na.rm=FALSE) ## S4 method for signature 'SpatRaster' stdev(x, ..., pop=TRUE, na.rm=FALSE) ## S4 method for signature 'SpatRaster' which.min(x) ## S4 method for signature 'SpatRaster' which.max(x)
x |
SpatRaster |
... |
additional SpatRaster objects or numeric values |
trim |
ignored |
pop |
logical. If |
na.rm |
logical. If |
SpatRaster
set.seed(0) r <- rast(nrow=10, ncol=10, nlyr=3) values(r) <- runif(ncell(r) * nlyr(r)) x <- mean(r) # note how this returns one layer x <- sum(c(r, r[[2]]), 5) # and this returns three layers y <- sum(r, r[[2]], 5) max(r) max(r, 0.5) y <- stdev(r) # not the same as yy <- app(r, sd) z <- stdev(r, r*2)
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