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Coordinador funcional de Biología
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Física y Química Inorgánica
Universidad Rey Juan Carlos
Móstoles España
Instituto de Investigación en Cambio Global (IICG-URJC)
Universidad Rey Juan Carlos,
Tulipán s/n,
28933 Móstoles, España
column between the spatial
scales, but it didn't work.
>From here
<https://drive.google.com/drive/folders/1KhnQEajpSKh59XuWkxTZcc_2YxPcxYW7?usp=sharing>
you can download the two data.frames.
[[alternative HTML version deleted]]
st_as_sf()
g_25000 # Geometry type: POLYGON
g_25000_c <- st_intersection(g_25000, g_5_d)
g_25000_c # Geometry type: GEOMETRY
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El 10/02/2023 a las 15:13, Roger Bivand escribió:
On Fri, 10 Feb 2023, Marcelino de la Cruz Rot wrote:
It depends on what you mean by "overimpose".
Maybe this way?
# some spatial polygon
Please, in the spirit of the evolving r-spatial package ecosystem
(maptools will retire d
uot;, add=T)
plot(B, border="red", add=T)
plot(A, add=T)
Cheers,
Marcelino
El 10/02/2023 a las 13:29, Manuel Spínola escribió:
Thank you very much Marcelino.
And how can overimpose those grids to a spatial polygon?
Manuel
On Fri, 10 Feb 2023 at 03:09 Marcelino de la Cruz Ro
km x 4km, and in each of those I want 4 tiles of 2km x
2km, and in each of those I want 4 tiles of 1km x 1km.
Manuel
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Sorry, I meant to say
subset(insects.ppp, marks=="termiNests" & ddd[,"antNests"] >20)
El 16/09/2020 a las 9:18, Marcelino de la Cruz Rot escribió:
Hi Alexandre,
may be this?
ddd <- nndist(insects.ppp, by=factor(insects.ppp$marks))
subset(insects.ppp, marks==&
termiNests" to
"antNests" marks and not the "termiNests" with "termiNests" too.
Please any ideas?
Thanks in advanced,
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> LinkedIn:
> br.linkedin.com/in/alexandre-dos-santos-87961635Mendeley:www.mendeley.com/profiles/alexandre-dos-santos6/
> ======
>
>
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grid of 500mx500m cells) with an equal number of objects per cluster?
I tried to attach an example of what I mean but I think the message was
never delivered.
Regards
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e that ties
in with packages like BIEN would be great. Perhaps I'll work on it someday.
-A
On 9/25/17, 11:32 AM, "Marcelino de la Cruz Rot"
wrote:
Hello Andy,
I've been using raster to play with WordlClim 2.0 and it works fine. For
example:
# Download 2.5' arc P data
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wish to achieve?
cheers,
Rolf Turner
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Phone: +64-9-373-7599 ext. 88276
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plot it in a single grid.
Thanks,
Guy
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62.x
I wonder how I can do the same analysis using R.
Thanks,
Dome
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the same analysis using R.
Thanks,
Dome
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ut I'm not sure however.
Thanks in advance for any advice
Paolo
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My apologies for such a pair (or more) of embarrassing mistakes!
I should read a bit more these days...
Marcelino
El 04/03/2017 a las 2:31, Rolf Turner escribió:
On 04/03/17 08:38, Marcelino de la Cruz Rot wrote:
Hi Manuel,
I do answer to the question "How can I make a spatial grid
:28992")
xx <- spsample(meuse.grid, type = "hexagonal", cellsize = 62.04*2)
xx <- HexPoints2SpatialPolygons(xx)
rgeos::gArea(xx, byid=TRUE)
It gave me an area per hexagon = 1.19
Both results are wrong, because I was expecting close to 1.
How can I make a spatial grid of 1
Hi Diego,
it seems to me that what you want to compute are weighted centroids.
Here some advice is given (it is for polygons but you can get the idea):
https://stat.ethz.ch/pipermail/r-sig-geo/2016-February/024107.html
Cheers,
Marcelino
El 20/04/2016 a las 8:45, Diego Pavon escribió:
Dear
Hi eugeneby,
This is quite easy, using rgdal, maptools and spatstat.
See this example:
library(sp)
library(rgdal)
library(maptools)
library(spatstat)
# read external grid with rgdal; for example this data on package sp
x <- readGDAL(system.file("external/test.ag", package="sp")[1])
#transfor
Maybe the information related to r you are looking for is in the Warning
section:
"Warnings
The estimator of K(r) is approximately unbiased for each fixed r. Bias
increases with r and depends on the window geometry. For a rectangular
window it is prudent to restrict the r values to a maximum
It depends on what do you exactly mean by "rescale". May be with
dilation() or affine():
plot(letterR, xlim=c(0,10), ylim=c(0,10), main="")
plot(dilation(letterR, .2), add=T, border=2)
plot(affine(letterR, mat=diag(c(2,2)),vec=c(-2,-.65)), add=T, border=3)
Cheers,
Marcelino
El 22/09/2014
Hi Edoardo,
This happens because, as the help for predict.glm() says:
'The default [prediction] is on the scale of the linear predictors; the
alternative "response" is on the scale of the response variable.'
I.e., you should use
pred = predict(predictors, logit, ext=ext, type ="response")
Hi Jesús,
If you are talking about gridded maps, you could consider Syrjala's test
(Stephen E. Syrjala 1996. A Statistical Test for a Difference between
the Spatial Distributions of Two Populations. Ecology 77:75–80.
http://dx.doi.org/10.2307/2265656). It is implemented in the ecespa package.
Hi Lutfor,
This seems to me as an instance of what has been called "independent
labelling" (De la Cruz et al. 2008) or "trivariate random labelling"
(Biganzoli et al. 2009, Xu et al. 2009).
You can find an old implementation of this approach in function K012 in
the package ecespa (which depe
Yes, it is possible and very easy. How do you extract your p-value
depends on wether you are making a pointwise or a global test. See the
help page of envelope(). You can also try a maximum absolute deviation
test with dclf.test().
Cheers,
Marcelino
El 13/01/2014 16:52, Francesco Carrer
You can complaint directly to to David Strauss for their unscientific
behaviour. :-p
As ?redwoodfull says:
"The dataset redwoodfull contains the full point pattern of 195 trees.
The window has been rescaled to the unit square. Its physical size is
approximately 130 feet across.
...
The plot
It seems that you get an estimate of lambda= 0.0 for your ficus
point #2. Kinhom can only be estimated when lambda > 0 in all points.
I would use either another sigma for bandwith estimation or set the
argument leaveoneout=FALSE.
Cheers,
Marcelino
El 22/10/2013 14:37, David Villalobos
In general it is acknowledged that it is more important to select a
bandwith (i.e., a sigma for the Gaussian kernel) than the type of kernel
(see for example the 1st ed. of the ASDAR book, p. 167).
You can select a sensible bandwith with the functions bw.diggle in
spatstat or mse2d in splancs.
Hi LS,
you can us function tess to create a tesselation and then split your ppp
with it. See this example:
data(lansing)
lansing
gridA<- tess(xgrid=seq(0,1, by=0.1), ygrid=seq(0,1,by=0.1))
gridA
plot(lansing)
plot(gridA, add=T)
lansing.g=split(lansing, gridA)
lansing.g
length(lansing.g)
lansin
: 'Marcelino de la Cruz'
Cc: r-sig-geo@r-project.org
Subject: RE: [R-sig-Geo] random spatial distribution code / shapefiles with
more than one polygon
Hi Marcelino,
I think what I would like to do is convert the shapefile comprised of multiple
polygons into a single object of class owin.
Howev
> that comes later on in my code.
>
> Thanks,
> Abby
>
> -Original Message-
> From: Marcelino de la Cruz [mailto:marcelino.delac...@upm.es]
> Sent: Thursday, March 07, 2013 2:53 AM
> To: Abby Rudolph
> Cc: r-sig-geo@r-project.org
> Subject: Re: [R-sig-Geo] random spatia
e
other error in the code?
Any help would be very much appreciated.
Thanks!
Abby
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igma" or
whether this function even what I need to meet my ultimate goal. Any
alternative solutions for thinning point data are also appreciated.
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ral de Educação, Ciência e Tecnologia de
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Hi Mitra,
In the archives of Ecography you can find data and R code to lern
autologistic regression according to the paper of Dorman et al.
(Ecography 30: 609628, 2007)
http://www.oikos.ekol.lu.se/appendixdown/snouterdata.txt
http://www.oikos.ekol.lu.se/appendixdown/E5171.pdf
HTH,
Marcelino
fitting-point-pattern-using-spatstat-tp7577627.html
>Sent from the r-sig-ecology mailing list archive at Nabble.com.
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_
Maybe this could be a solution (to the "compute
the distance from each of my points to the nearest "ocean coastline" demand)
First, I understand that except for islands
"coastline" is a line, so instead of using
coastline as a window, use the coastline as a line segment pattern.
Then compute t
convexhull(my.owin)
HTH,
Marcelino
At 15:17 04/10/2012, sara martino wrote:
>Content-Type: text/plain
>Content-Disposition: inline
>Content-length: 1408
>
>Hi, thanks for the answer but this is not what i want.
>I want to get a simplified windows which
>includes all the points of the old o
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ds is easy) what i wish to do is create a voxel index and say
>how much points are inside in the voxel.
>
>Always thanks for your suggestions and really appreciate solution
>Gianni
>
>
>
>
>On Thu, Jul 12, 2012 at 4:13 PM, Marcelino de la Cruz
><<mailto:marcelin
Hi Gianni,
I don't know if I this is what you want but maybe would be based in
something like this:
myPointsc <- apply(myPoints,2,cut, breaks=0:10, labels=paste("v",
1:10, sep=""))
myPointsc <- apply(myPointsc,1,paste, collapse="")
> myPoints[myPointsc=="v1v1v1",]
[,1] [,2]
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Hi,
with respect to the implementation of the "Bivariate i.e.2-type point
pattern (see package 'spatstat')" you may do something like this:
library(spatstat)
plants.ppp <- ppp( x = example$x, y =example$y, marks=factor(sex),
window = yourwindow),
where "example" is a data.frame with your d
d I proceed?
>
>Best regards,
>
>Cleverson
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Marcelino de la Cruz Rot
- Original Message -
>From: <mailto:marcelino.delac...@upm.es>Marcelino de la Cruz
>To: <mailto:amonle...@wanadoo.es>TM ;
><mailto:r-sig-geo@r-project.org>r-sig-geo@r-project.org
>Sent: Monday, March 05, 2012 2:14 PM
>Subject: Re: [R-sig-Geo] Probability map of presence(1)-abse
f Barcelona
>
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Depto. Biologia Vegetal
EUIT Agricola
Universidad Politecn
WF-Canada
>Email: god...@dal.ca | Web: wormlab.biology.dal.ca
>
>--
>View this message in context:
>http://r-sig-geo.2731867.n2.nabble.com/Loosmore-and-Ford-2006-Goodness-of-fit-GoF-test-for-CSR-tp7332928p7336880.html
>Sent from the R-sig-geo mailing list archive at Nabble.com.
>
>
gt;something basic about how to functions?
>Thanks if anyone can help out as to where I'm going wrong?
>
>Robin
>
>
>--
>Robin Hunnewell
>University of New Brunswick
>Fredericton, NB
>Canada
>
>_
ttp://r-sig-geo.2731867.n2.nabble.com/Extracting-GIS-file-best-approach-tp6904616p6904976.html
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list archive at
>Nabble.com.
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Marcelino de la Cruz Rot
Depto. Bio
and a regular
>user of several of the maps/spatial/raster packages.
>
>Any help would be much appreciated.
>
>- Malcolm
>
>
> [[alternative HTML version deleted]]
>
>
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and calculate herefrom 10 envelope objects:
> >
> > ENV_OBJ_1 = envelope(X,fun=pcf,simulate=sim_pat_list1)
> > ENV_OBJ_2 = envelope(X,fun=pcf,simulate=sim_pat_list2)
> >
> > ENV_OBJ_10 = envelope(X,fun=pcf,simulate=sim_pat_list10)
> >
> >
> > is it then poss
produce something like this (pseudo-code):
>
>ENV_OBJ = join(ENV_OBJ_1, ENV_OBJ_2, ..., ENV_OBJ_10)
>
>to get the same result?
>
>Thank you,
>Jan
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build cluster:
>a- points within 3km of distance from each other
>b- earthquakes happened within 14 days (2 weeks) from each other
>
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"y"]
image(meuse.grid["xcoord"]) # note the single [
image(meuse.grid["ycoord"])
Cheers,
Marcelino
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28040 Madri
On 17/05/2011 14:00, Mathieu Rajerison wrote:
> Many thanks for the code!
>
> What are the units of R in marktable? I didn't find the information in the
> documentation.
>
The same units of x and y coordinates in your ppp object.
Cheers,
Marcelino
> 2011/5/
e
definition of diversity (i.e. "richness"). From that table you could
also compute Shannon or Simpson diversity indices, if you would prefer
that.
Marcelino
i cant figure out how diversity is calculated here?
Thanks again for the help,
matevz
-Original Message-
From:
es of density and diversity
plot(eval.im(lansing.div.s >4.5 & (lansing.den/max(lansing.den))>0.9))
HTH. Cheers,
Marcelino
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;are all zeros.
>s.df96$n.longi-s.df97$n.longi
>s.df96$n.lati-s.df97$n.lati
># Combine two data
>f.df <- data.frame(cbind(s.df96, s.df97))
>head(f.df)
># Compare distribution of two species
>library(ecespa)
>library(spatstat)
>coords <- data.frame(f.df$n.longi, f.df$n.l
y by
> the distance of the roads >from "u". >Get it
> clear in your mind first how you want to model
> the intensity. Then >build your image, or
> images, in terms of your roads data in a
> manner >consistent >wit
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tel: 34 + 91336
a?
> > If so, is there any (potential) problem(s) in doing that?
> >
> > Thanks for your any comment and suggestion in advance!
> >
> > Steve
> >
> >
> >
> >
>
> [[alternative HTML version deleted]]
>
>_________
nto a vector
>v_class7 <- as.numeric(grids50m$landform5)
># Load the landform data into the raster
>r_class7 <- setValues(r_class5, v_class5)
>
>Many thanks for your advice...
>Andy Wilson
>
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tel:
"pixels"
> lambdaW.px = density.ppp(wh, sigma=0.15)#default at="pixels"
> K3 = Kcross.inhom(lansing, "whiteoak", "maple", lambdaW.px, lambdaM.px)
> plot(K3)
>
>Erika Mudrak
>Postdoctoral Researcher
>Ecology, Evolution, and Organism
for this exists?
>
>Cheers,
>
>Simon
>
> [[alternative HTML version deleted]]
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__
Hi Alexander,
Try this:
spatstat.options(gpclib=TRUE)
data(letterR)
m<- as.mask(letterR, dimyx=32)
p<- as.polygonal(m)
Cheers,
Marcelino
Con fecha 3/12/2010, "Alexandre Villers"
escribió:
>Good afternoon
>
>I'm trying to convert a mask to a polygonal windows with spatstat.
>However,
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