On Wed, 22 Apr 2009, Michael Sumner wrote:
Ouch - sorry about that, I should have checked.
I don't know how to set axis labels with spplot I'm afraid.
Something like:
library(maptools)
xx <- readShapeSpatial(system.file("shapes/sids.shp", package="maptools")[1],
IDvar="FIPSNO", proj4string=
Many thanks for all the responses to my query regarding identifying
simulated fire scars and computing their sizes. I received many helpful
responses, and thankfully no unhelpful ones. It turns out that what I'm
looking for is called a connected clusters algorithm, and is, as I
suspected, well-
Ouch - sorry about that, I should have checked.
I don't know how to set axis labels with spplot I'm afraid.
Regards, Mike.
==Original message text===
On Wed, 22 Apr 2009 11:49:09 +1000 jin...@ga.gov.au wrote:
Thank you very much, Mike. That is very helpful. I got the
Thank you very much, Mike. That is very helpful. I got the colour I was after.
My data are in lat/long, when I tried degAxis I got:
> degAxis(1)
Error in axis(side, at = at, labels = labels, ...) :
plot.new has not been called yet
> degAxis(2)
Error in axis(side, at = at, labels = labels, ...) :
This examples shows the use of colorRampPalette(grDevices) to create a
color ramp with sp.theme, borrowing from the spplot documentation. See
colors()[grep("brown", colors())]
to find the browns R already knows about, or generate your own.
library(lattice)
trellis.par.set(sp.theme(regions
Thanks, Matt. But it seems that we still can not specify col=
brown((0:100)/100) in image.plot. The brown colour is what I really need for
continuous data. Cheers, Jin
-Original Message-
From: matt.j.oli...@gmail.com [mailto:matt.j.oli...@gmail.com] On Behalf Of
Matt Oliver
Sent: Wednes
you may want to try
require(fields)
?image.plot
On Tue, Apr 21, 2009 at 8:39 PM, wrote:
> Dear all,
>
>
>
> I am using spplot to generate some maps. The maps produced are beautiful as
> shown by the attached file, but I was wondering if it is possible to do the
> following modifications:
>
>
Hi Alina,
It sounds like you want to dissolve the zips based on their membership in a
district. If I have that right, then try the function unionSpatialPolygons. The
example from the help is below.
Zev
library(sp)
library(gpclib)
nc1 <- readShapePoly(system.file("shapes/sids.shp", package="mapt
On Tue, 21 Apr 2009, Alina Sheyman wrote:
To rephrase my question and explain my problem further
I have already created a map for revenue by zip code, in the state of
Massachusetts. To accomplish that I used a shapefile for Massachusetts and a
dataframe with my revenue/zip codes.
What I am tryi
To rephrase my question and explain my problem further
I have already created a map for revenue by zip code, in the state of
Massachusetts. To accomplish that I used a shapefile for Massachusetts and a
dataframe with my revenue/zip codes.
What I am trying to do now is to have the same map done not
Beware that results yield by hierarchical clustering are very dependent
upon the actual initial subsample,
Agus
Corey Sparks wrote:
Yes, I've already run into this problem trying to run hclust on a
landsat image. I'm on an imac with leopard and 4gb of ram and the
distance matrix needed wouldn't
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