spatstat regression troubles
Hi Mr. Turner, 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
what you are actually doing. To start with, one gets the impression that you have
*two* point patterns ("people" and "no people"). But then, from your quadrature
scheme it appears that you are treating the "no people" pattern as the dummy
points for the quadrature scheme.
*Are* they just dummy points, chosen by you in some more or less arbitrary
manner? Or are they points of an actual *observed* point pattern?
Assuming that they are indeed dummy points (chosen in a reasonably sensible
manner) what you need to do is create a data frame of covariate values at each
of the points of your quadrature scheme. You indicate that you have the covariate
values available at each of these points, so that should be alright.
(In respect of "sensible manner" --- 2828 dummy points may be somewhat too
few. The default quadrature scheme that ppm() would create would have 4904
dummy points.)
Anyhow, suppose that you have such a data frame, say "covDf" with two columns
named "Z1" and "Z2" and with 3772 = 944 + 2828 rows, the i-th row corresponding
to the i-th point in the quadrature scheme.
You would then execute a command of the form
fit <- ppm(people_quadrature, ~ Z1 + Z2, covariates=covDf)
See the help on ppm().
Life would be much easier for you if you could build *images* providing the values of
Z1 and Z2 for a fairly fine pixellation of the observation window. See the help on im().
If you have such images, say IZ1 and IZ2, then you can let ppm() take care of creating
the dummy points and proceed as follows:
fit <- ppm(people_exist, ~ IZ1 + IZ2, covariates=list(IZ1=IZ1, IZ2=IZ2))
I.e. you just need the ``real'' point pattern (presumably a point pattern of human
habitations) and the covariates expressed as (pixellated) images.
cheers,
Rolf Turner
P. S. The foregoing all assumes that the pattern of interest is a realization of
an inhomogeneous ***Poisson*** process, with intensity depending (log linearly)
upon the two covariates Z1 and Z2. There could of course be *interaction* between
the points and the dependence upon covariates could be more complicated than
that proposed.
R. T.