On 2009-May-25 , at 15:34 , Jim Burke wrote:
Everyone's suggestions work. There are lots
of roads to solutions in R. This is wonderful!
There's even more. In the package Hmisc, there is a %nin% command that
does what you want. It is less standard (requires an additional
package) but more
Hi All,
Everyone's suggestions work. There are lots
of roads to solutions in R. This is wonderful!
It seems that Dan's and Pedro's solutions
work faster for my largish 1,897 spatial
polygon data frame.
Thanks,
Jim Burke
All replies are below.
=
Sorry, I sent my initial reply only to Jim, not the list.
Dan
Forwarded Message
From: Jim Burke
To: Dan Putler
Subject: Re: [R-sig-Geo] How to negate %in%
Date: Mon, 25 May 2009 14:01:26 -0500
Thanks Dan,
Both yours and Torleif's suggestions work with a
SpatialPolygonsDataFr
Hi
This (!) might work:
aa <- 1:10
bb <- 5:6
aa[!aa %in% bb]
[1] 1 2 3 4 7 8 9 10
aa[aa %in% bb]
[1] 5 6
Best wishes
Torleif
On Monday 25 May 2009 08:13:40 pm Jim Burke wrote:
> I can subset a "SpatialPolygonsDataFrame" from a
> data frame containing a smaller subset of IDs. For
> ex
I can subset a "SpatialPolygonsDataFrame" from a
data frame containing a smaller subset of IDs. For
example below.
smaller_sp <- large_sp [large_sp$ID %in% smaller_df$ID,]
Given the above how can I do the logically opposite from
the %in% operation and get all those IDs not %in%?
I am processing
Ebrahim Jahanshiri wrote:
> for kriging a preliminary analysis is important. We should know if the data
> has trend or how are the ouliers? should we remove them or not. Some of the
> kriging techniques like universal kriging create very odd values at some
> points if we dont deal with the data fri
for kriging a preliminary analysis is important. We should know if the data
has trend or how are the ouliers? should we remove them or not. Some of the
kriging techniques like universal kriging create very odd values at some
points if we dont deal with the data frist hand before kriging. I hope tha
Edzer Pebesma escribió:
> I believe that many interpolation problems "out there" are simple, and
> can be solved using geostatistics with a "finish" or "interpolate"
> button. A question I find interesting is whether this button should
> always do its best, or should it be so clever to warn the use
I believe that many interpolation problems "out there" are simple, and
can be solved using geostatistics with a "finish" or "interpolate"
button. A question I find interesting is whether this button should
always do its best, or should it be so clever to warn the user in case a
problem is not "that
Raphael,
you could read your shapefile with readOGR in package rgdal,
then use function overlay to find the points in the polygon,
along the lines of:
# grd is your full, rectangular grid
# polygon is the shapefile you read
require(sp)
fullgrid(grd) = FALSE
sel = !is.na(overlay(grd, polygon))
grd
Hello,
I have been using gstat to krige maps over my study area.
The area is irregularly shaped and has quite a complex boundary.
I have made a regular (square) grid that covers my study area and made
my estimates on this grid.
Now that I have my maps, I would like to 'mask out' or delete the
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