From: Gregory Ryslik <rsaber_at_comcast.net>

Date: Sat, 16 Apr 2011 10:17:20 -0400

*>
*

> Your procedure seems to evince quite a bit of confusion in your mind about

*> what you are actually doing. To start with, one gets the impression that you have
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*> *two* point patterns ("people" and "no people"). But then, from your quadrature
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*> scheme it appears that you are treating the "no people" pattern as the dummy
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*> points for the quadrature scheme.
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*>
*

*> *Are* they just dummy points, chosen by you in some more or less arbitrary
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*> manner? Or are they points of an actual *observed* point pattern?
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*>
*

*> Assuming that they are indeed dummy points (chosen in a reasonably sensible
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*> manner) what you need to do is create a data frame of covariate values at each
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*> of the points of your quadrature scheme. You indicate that you have the covariate
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*> values available at each of these points, so that should be alright.
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*>
*

*> (In respect of "sensible manner" --- 2828 dummy points may be somewhat too
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*> few. The default quadrature scheme that ppm() would create would have 4904
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*> dummy points.)
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*>
*

*> Anyhow, suppose that you have such a data frame, say "covDf" with two columns
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*> named "Z1" and "Z2" and with 3772 = 944 + 2828 rows, the i-th row corresponding
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*> to the i-th point in the quadrature scheme.
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*>
*

*> You would then execute a command of the form
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*>
*

*> fit <- ppm(people_quadrature, ~ Z1 + Z2, covariates=covDf)
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*>
*

*> See the help on ppm().
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*>
*

*> Life would be much easier for you if you could build *images* providing the values of
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*> Z1 and Z2 for a fairly fine pixellation of the observation window. See the help on im().
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*> If you have such images, say IZ1 and IZ2, then you can let ppm() take care of creating
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*> the dummy points and proceed as follows:
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*>
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*> fit <- ppm(people_exist, ~ IZ1 + IZ2, covariates=list(IZ1=IZ1, IZ2=IZ2))
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*>
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*> I.e. you just need the ``real'' point pattern (presumably a point pattern of human
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*> habitations) and the covariates expressed as (pixellated) images.
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*>
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*> cheers,
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*>
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*> Rolf Turner
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*>
*

*> P. S. The foregoing all assumes that the pattern of interest is a realization of
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*> an inhomogeneous ***Poisson*** process, with intensity depending (log linearly)
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*> upon the two covariates Z1 and Z2. There could of course be *interaction* between
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*> the points and the dependence upon covariates could be more complicated than
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*> that proposed.
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*>
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*> R. T.
*

*>
*

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Sat 16 Apr 2011 - 14:21:16 GMT

Date: Sat, 16 Apr 2011 10:17:20 -0400

You are correct that I am confused a bit by the RCode. Basically, I have 3772 observations of data and only about 500 of them correspond to where "people" exist. For the other observations, I just have the covariate values so I thought that this was appropriate. Thus, where "people exist" is my spatial point pattern and everywhere else I just have covariate values.

Thank you for your help and suggestions on how to fit the data. I was able to get it to work using the data frame method but I seem to be having difficulty getting the image thing to work.

Basically, at the moment, I have two matrices for Z1 and Z2 which is in the form of (z, x, y) where z is the value, x is the x-coordinate and y-is the y coordinate. Thus the matrix dimension is 3772x3. I've tried converting this to an image but they do a index swap so I'm not quite sure what the correct way to do it would be? Hopefully, I would get the fit using the image way and see that the fits are consistent.

Thank you again for your help!

Kind regards,

Greg

On Apr 16, 2011, at 4:46 AM, Rolf Turner wrote:

> On 16/04/11 15:50, Gregory Ryslik wrote:

>> Hi Everyone, >> >> I am trying to figure out the spatstat package for the first time and am having some trouble. Unfortunately, I can't post my data set but I'll hopefully post enough details for some help. >> >> I want to model the intensity of a spatial point process using 2 covariates from my data. After reading through the documentation, I have successfully created 2 "ppp" objects. The first ppp object is a list of coordinates where people exist and the second is where people do not exist as follows: >>> people_exist >> planar point pattern: 944 points >> window: rectangle = [73, 135] x [18, 54] units >>> people_empty >> planar point pattern: 2828 points >> window: rectangle = [73, 135] x [18, 54] units >> >> Now, I also have observed values for two covariates, Z1 and Z2 for both the 944 and 2828 points in dataframe form. Finally, following the documentation, I was able to create one quadrature Q, with 944 points and 2828 dummy points that correctly takes the points where we had an event (people_exist) and the points where we don't have an event (people_empty). >>> people_quadrature >> Quadrature scheme >> 944 data points, 2828 dummy points >> Total weight 1098.64 >> >> How do I use the Quadrature to model my intensity based off of those two covariates and an intercept term alpha? In mathematical terms, if \lambda is my intensity function, I want to estimate \lambda(s;b) = exp(alpha + b_1 * Z_1 + b_2 * Z_2). >> >> Thank you for your help! I really appreciate it.

> Your procedure seems to evince quite a bit of confusion in your mind about

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