[R-sig-Geo] categorical values in im-objects (spatstat/ppm)

[Rolf Turner]

I cannot make head nor tail of your question.

I don't understand your use of eval.im() --- that function is
intended to do calculations on objects that are (already) of class "im".

E.g. if X is an image with real positive pixel values then

    Y <- eval.im(sqrt(Y))

is an image whose pixel values are the square roots of the corresponding 
pixel values from X.

The help for im() in spatstat explains pretty clearly how to create a 
factor valued image.

I tried to have a look at the data to which you gave a link at the end
of your email.  The "owin.csv" file has 44 lines, all but 6 of which are
missing.  If the missing lines are eliminated the resulting window looks 
like a triangle, despite having 6 vertices.

Of the 224 points in "events.csv", 208 fall outside the window referred 
to above.

We can't really advise you about "zones" unless you provide "zones" 
which you seem not to have done.

I suggest that you back off, get a coherent story together, provide a 
complete and consistent set of data, and then ask again.

cheers,

Rolf Turner

On 10/03/15 11:02, Silvia Cordero-Sancho wrote:
> Hello,
>
> I will like to fit a point process model (ppm) with several covariates. One
> of them is a grid with 15 categorical variables (zones).
>
> To recognized the values in my grid as categorical, I followed the code in
> the following link:
>
> http://stackoverflow.com/questions/26162955/r-package-spatstat-how-to-use-point-process-model-covariate-as-factor-when-pixe?answertab=active#tab-top
>
>
>
>
> *zone1<-eval.im <http://eval.im>(as.factor(zone))*
>
> *levels(zone1)<-c("A1","A2","A3","A4","B1","B2","B3","B4",*
> *              "C1","C2","C3","C4","C5","C6","D")*
>
> *unitname(zone1)<-c("meter","meters")*
>
> But I am not sure if it really worked. If I run the function
> *is.factor(zone1)*, the result is FALSE, but if I run the function
> selecting any column or row (e.g. *is.factor(zone1[1,])* or
> *is.factor(zone1[,200])*) the results show as TRUE.
>
> However, the function *summary(zone1)* indicates that it is a factor value
> pixel image:
>
> factor-valued pixel image
> 2641 x 680 pixel array (ny, nx)
> enclosing rectangle: [992380, 1012780] x [732491, 811721] meters
> dimensions of each pixel: 30 x 30 meters
> Image is defined on a subset of the rectangular grid
> Subset area = 1577529000 square meters
> * Pixel values (inside window):
>
>   A1     A2     A3     A4     B1     B2     B3     B4     C1     C2     C3
>    C4     C5     C6    D
> 116928   5670  16614   6823  27917   7547    197   9354 132658 405515
> 1016 136784 576913 113978 194896
>
> * *The distribution of the number of cells per zone is the same than the
> original file *
>
> However, when I used the layer within the ppm function, not all the
> categories are included in the analysis:
>
> *m1<-ppm(ag4u,~Z, covariates=list(Z=zone1), AreaInter(200))*
>
> *coef(summary(m1))*
>
>                 Estimate
> (Intercept) -16.4787854
> ZA3           2.6334407
> ZA4           1.4900159
> ZB1           0.6177496
> ZB2           0.3502884
> ZB4           1.4179890
> ZC1          -2.0643563
> ZC2          -0.6806136
> ZC4          -0.1897898
> ZC5          -2.8285278
> ZC6           1.5300109
> ZD            2.1210203
> Interaction   2.4118998
>
> The zones identified as A1, A2, B3, C3 are excluded from the analysis
>
> Similarly, I get the same results when I used the following expression:
>
> *m2<-ppm(ag4u,~factor(Z),covariates=list(Z=zone),  AreaInter(200)) *
>
> And the following error when I tried to plot the residuals...
>
> *qqplot.ppm(m1,nsim=100,verbose=F)*
>
> Error in model.frame.default(Terms, newdata, na.action = na.action, xlev =
> object$xlevels) :
>    factor Z has new levels A2, C3
>
> So, I think that the problem could be associated with functions I am
> employing to assign the factor-values. Is this is the problem, i*s there an
> alternative to define categorical values for im-objects? Or, it could be
> other reason for the exclusion of categories?*
>
> I will appreciate any advise.


-- 
Rolf Turner
Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276
Home phone: +64-9-480-4619