Odd behavior of dismo's extract function
Updating to R 3.3.1 fixed it. Thanks! Still baffled as to why the sudden dropoff between 250 and 251, but as long as it's working all is well. Cheers!
On Mon, Jul 25, 2016 at 12:24 PM, Dan Warren <dan.l.warren at gmail.com> wrote:
How very odd. I'm using R 3.3.0, but as far as I can tell I'm using the same package versions as you. I've tried this on two machines (12 core Mac Pro and an older Macbook Pro) and I'm getting the same phenomenon on both. Could it be a weird OSX thing? I'll try updating R and then if it still persists I'll bootcamp over into Windows and see if it's happening for me there. My session info (sorry for not including that the first time): Session info ---------------------------------------------------------------------------------------------------------------------------------------- setting value version R version 3.3.0 (2016-05-03) system x86_64, darwin13.4.0 ui RStudio (0.99.491) language (EN) collate en_AU.UTF-8 tz Australia/Sydney date 2016-07-25 Packages -------------------------------------------------------------------------------------------------------------------------------------------- package * version date source colorspace 1.2-6 2015-03-11 CRAN (R 3.3.0) devtools 1.12.0 2016-06-24 CRAN (R 3.3.0) digest 0.6.9 2016-01-08 CRAN (R 3.3.0) dismo * 1.1-1 2016-06-16 CRAN (R 3.3.0) ENMTools * 0.1 2016-07-25 local ggplot2 * 2.1.0 2016-03-01 CRAN (R 3.3.0) gridExtra * 2.2.1 2016-02-29 CRAN (R 3.3.0) gtable 0.2.0 2016-02-26 CRAN (R 3.3.0) highr 0.6 2016-05-09 CRAN (R 3.3.0) knitr * 1.13 2016-05-09 CRAN (R 3.3.0) lattice 0.20-33 2015-07-14 CRAN (R 3.3.0) memoise 1.0.0 2016-01-29 CRAN (R 3.3.0) munsell 0.4.3 2016-02-13 CRAN (R 3.3.0) plyr * 1.8.4 2016-06-08 CRAN (R 3.3.0) raster * 2.5-8 2016-06-02 CRAN (R 3.3.0) Rcpp 0.12.5 2016-05-14 CRAN (R 3.3.0) rgeos * 0.3-19 2016-04-04 CRAN (R 3.3.0) rJava 0.9-8 2016-01-07 CRAN (R 3.3.0) scales 0.4.0 2016-02-26 CRAN (R 3.3.0) sp * 1.2-3 2016-04-14 CRAN (R 3.3.0) viridis * 0.3.4 2016-03-12 CRAN (R 3.3.0) withr 1.0.2 2016-06-20 CRAN (R 3.3.0) On Mon, Jul 25, 2016 at 12:15 PM, Michael Sumner <mdsumner at gmail.com> wrote:
On Mon, 25 Jul 2016 at 11:35 Dan Warren <dan.l.warren at gmail.com> wrote:
Just realized I pasted in the results backwards. It should have been system.time(extract.test(env, 250)) user system elapsed 124.562 0.516 125.061 system.time(extract.test(env, 251)) user system elapsed 2.807 0.084 2.891
I don't see the effect.
Perhaps it was fixed in recent version of raster?
Please post reproducible details, I downloaded your data files to
"test/testdata/" to try this.
Cheers, Mike.
library(raster)
library(dismo)
extract.test <- function(env, N){
extract(env, dismo::randomPoints(env, N))
}
env.files <- list.files(path = "test/testdata/", pattern = "pc",
full.names =
TRUE)
env <- raster::stack(env.files)
library(rbenchmark)
benchmark(n250 = extract.test(env, 250),
n251 = extract.test(env, 251), replications = 4)
# test replications elapsed relative user.self sys.self user.child
sys.child
# 1 n250 4 6.31 1.008 5.13 1.14 NA
NA
# 2 n251 4 6.26 1.000 5.02 1.22 NA
NA
devtools::session_info()
# Session info
-------------------------------------------------------------------------------------------------------------------------------
# setting value
# version R version 3.3.1 Patched (2016-07-09 r70874)
# system x86_64, mingw32
# ui RStudio (0.99.1261)
# language (EN)
# collate English_Australia.1252
# tz Australia/Hobart
# date 2016-07-25
#
# Packages
-----------------------------------------------------------------------------------------------------------------------------------
# package * version date source
# devtools * 1.12.0 2016-06-24 CRAN (R 3.3.1)
# digest 0.6.9 2016-01-08 CRAN (R 3.3.1)
# dismo * 1.1-1 2016-06-16 CRAN (R 3.3.1)
# evaluate 0.9 2016-04-29 CRAN (R 3.3.1)
# htmltools 0.3.5 2016-03-21 CRAN (R 3.3.1)
# knitr 1.13 2016-05-09 CRAN (R 3.3.1)
# lattice 0.20-33 2015-07-14 CRAN (R 3.3.1)
# magrittr 1.5 2014-11-22 CRAN (R 3.3.1)
# memoise 1.0.0 2016-01-29 CRAN (R 3.3.1)
# raster * 2.5-8 2016-06-02 CRAN (R 3.3.1)
# rbenchmark * 1.0.0 2012-08-30 CRAN (R 3.3.0)
# Rcpp 0.12.5 2016-05-14 CRAN (R 3.3.1)
# rgdal 1.1-10 2016-05-12 CRAN (R 3.3.1)
# rmarkdown 1.0.2 2016-07-19 Github (rstudio/rmarkdown at b65e177)
# sp * 1.2-3 2016-04-14 CRAN (R 3.3.1)
# stringi 1.1.1 2016-05-27 CRAN (R 3.3.0)
# stringr 1.0.0 2015-04-30 CRAN (R 3.3.1)
# withr 1.0.2 2016-06-20 CRAN (R 3.3.1)
Dan Warren, Ph.D. Department of Biology Macquarie University Email: dan.warren at mq.edu.au <dan.warren at anu.edu.au> Phone (US): 530-848-3809 Phone (Australia): 0468 696 897 Phone (Work): 02 9850 8587 Skype: dan.l.warren Google Scholar <https://scholar.google.com/citations?user=NTzu9c8AAAAJ&hl=en> Orcid <http://orcid.org/0000-0002-8747-2451> ResearcherID <http://www.researcherid.com/rid/B-3821-2010> Scopus <http://www.scopus.com/authid/detail.url?authorId=7202133982> On Mon, Jul 25, 2016 at 10:34 AM, Dan Warren <dan.l.warren at gmail.com> wrote:
This is not an error per se so much as just something very weird that I have noticed with a project I've been working on recently. I'm
wondering
if anyone here has any insight as to what may be causing this
behavior. I
haven't yet been able to duplicate it with simulated rasters (more
info on
that below), but it appears very reliably with real environmental data including the PC rasters for Cuba I have hosted here: https://github.com/danlwarren/ENMTools/tree/master/test/testdata What's happening is this: if I go to extract data from those rasters
using
occurrence points, the amount of time it takes increases very rapidly
up to
exactly 250 points, and falls dramatically after that. So dramatically that it takes over two minutes to extract data for 250 points but just under three seconds for 251. I've established that it's not a
question of
the points themselves being wonky, because it happens with random
points as
well.
extract.test <- function(env, N){
extract(env, randomPoints(env, N))
}
env.files <- list.files(path = "testdata/", pattern = "pc", full.names
=
TRUE) env <- stack(env.files) system.time(extract.test(env, 250)) user system elapsed 2.807 0.084 2.891 system.time(extract.test(env, 251)) user system elapsed 124.562 0.516 125.061 numpoints,time 1,1.54 5,3.93 10,6.764 50,29.939 100,61.431 150,79.295 200,110.283 250,120.118 251,2.748 252,2.756 254,2.767 500,2.876 1000,3.153 The data being extracted looks perfectly reasonable in all cases. It's not just these layers, either. Although (as I mentioned above) I have
yet
to come up with simulated rasters that show this behavior, I see this behavior for both of the sets of rasters for real environmental data
that
I've tried. The results above are from a PCA on Worldclim data for
Cuba,
but I just tried them on some Climond data I've got for Australia and
I get
the same behavior. Those rasters are much larger, though, and a
result the
times are longer; 251 points took about 43 seconds, whereas I just had
to
give up and stop the 250 point extraction after about 30 minutes. As for those simulated rasters, I've tried the following: Plain grids of sequential numbers As above, but with a bunch of NAs added Filling the Cuban rasters with sequential numbers Filling the Cuban rasters with random numbers from a uniform (0,1) distribution None of those show this issue. Anyone have any thoughts about what
might
be going on here?
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