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. Silvia Cordero Only in case, here a link with the data https://www.dropbox.com/sh/7t9ga3lmsx9ub0y/AACegUGCwXq6F7Gxn3elcBU9a?dl=0 [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
