Re: [R] spatstat regression troubles
On 17/04/11 02:17, Gregory Ryslik wrote:
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
500? You said 944 previously. Doesn't really matter, but.
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!
To make use of a covariate you ***really*** need to have the values of
the covariate
available at ***all*** points of the observation window. In your
situation I think that
the best that you can do is to interpolate between the actual observations.
You could use, I think, the interp() function from the "akima" package.
Here's a toy
demo:
require(akima)
W <- owin(c(73,135),c(18,54))
M <- as.mask(W,dimyx=c(250,500)) # Window is roughly twice as wide as
it is high.
set.seed(42)
X <- runifpoint(3772,win=W)
Z <- exp(2*(sin(2*X$x/pi) + sin(2*X$y/pi))) # A made-up covariate.
XYZ <- interp(X$x,X$y,Z,xo=M$xcol,yo=M$yrow,linear=FALSE,extrap=TRUE)
IZ <- im(t(XYZ$z),xcol=XYZ$x,yrow=XYZ$y) # Note the transpose of the z
matrix!
E <- as.im(function(x,y){exp(2*(sin(2*x/pi) + sin(2*y/pi)))},W=W)
plot(listof(exact=E,interp=IZ),nrows=2,main="")
The interpolated image is a bit rough compared with the truth (which we
know in this
artificial case) but no worse than what one might reasonably expect.
HTH
cheers,
Rolf Turner
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Re: [R] spatstat regression troubles
On 17/04/11 02:17, Gregory Ryslik wrote:
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
500? You said 944 previously. Doesn't really matter, but.
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!
To make use of a covariate you ***really*** need to have the values of
the covariate
available at ***all*** points of the observation window. In your
situation I think that
the best that you can do is to interpolate between the actual observations.
You could use, I think, the interp() function from the "akima" package.
Here's a toy
demo:
require(akima)
W <- owin(c(73,135),c(18,54))
M <- as.mask(W,dimyx=c(250,500)) # Window is roughly twice as wide as
it is high.
set.seed(42)
X <- runifpoint(3772,win=W)
Z <- exp(2*(sin(2*X$x/pi) + sin(2*X$y/pi))) # A made-up covariate.
XYZ <- interp(X$x,X$y,Z,xo=M$xcol,yo=M$yrow,linear=FALSE,extrap=TRUE)
IZ <- im(t(XYZ$z),xcol=XYZ$x,yrow=XYZ$y) # Note the transpose of the z
matrix!
E <- as.im(function(x,y){exp(2*(sin(2*x/pi) + sin(2*y/pi)))},W=W)
plot(listof(exact=E,interp=IZ),nrows=2,main="")
The interpolated image is a bit rough compared with the truth (which we
know in this
artificial case) but no worse than what one might reasonably expect.
HTH
cheers,
Rolf Turner
__
R-help@r-project.org mailing list
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.
Re: [R] 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
Re: [R] spatstat regression troubles
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.
__
R-help@r-project.org mailing list
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.
[R] spatstat regression troubles
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. Kind regards, Greg R. __ R-help@r-project.org mailing list 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.
