G'day all, I have been using the ppm() function to try (mainly successfully) to fit an outbreak pattern from the spatstat library (1-22)
This was giving me results that were consistent with my observations, showing a trend related to a village density and increasing towards the east of the region. However, the scalebar was somewhat out of range, and I've realised I was working in longitude/latitude for my coordinates. I've transformed all of the data to utm, and gone through the same process, but when I try to fit the same model, I'm getting the following messages: fit2.utm <- ppm(outbreaks.utm.ppp, ~ x + y + dens, covariates=list(dens=vill.dens.utm), use.gam=TRUE) Warning messages: 1: In countingweights(id, areas) : Some tiles with zero area contain points 2: In countingweights(id, areas) : Some weights are zero The two (one) data sets are given below : > summary(outbreaks.ll.ppp) Marked planar point pattern: 2231 points Average intensity 12.9 points per square unit Multitype: frequency proportion intensity A 222 0.09950 1.2800 Asia1 7 0.00314 0.0403 O 1090 0.48900 6.2800 Unknown 912 0.40900 5.2600 Window: polygonal boundary single connected closed polygon with 2222 vertices enclosing rectangle: [92.20499, 109.46484]x[1.269528, 28.546524]units Window area = 173.539 square units *** 36 illegal points stored in attr(,„rejects‰) *** > summary(outbreaks.utm.ppp) Marked planar point pattern: 2102 points Average intensity 1.01e-09 points per square unit Multitype: frequency proportion intensity A 215 0.10200 1.03e-10 Asia1 6 0.00285 2.88e-12 O 1000 0.47600 4.80e-10 Unknown 880 0.41900 4.22e-10 Window: polygonal boundary single connected closed polygon with 2222 vertices enclosing rectangle: [417539.8, 2308845.2]x[142732, 3166241]units Window area = 2.08442e+12 square units *** 165 illegal points stored in attr(,„rejects‰) *** The result of the two models are given below : > fit2.ll Nonstationary multitype Poisson process Possible marks: A Asia1 O Unknown Trend formula: ~x + y + dens Fitted coefficients for trend formula: (Intercept) x y dens 0.3513215637 0.0141986623 -0.0176586862 0.0004128483 Estimate S.E. Ztest CI95.lo CI95.hi (Intercept) 0.3513215637 4.494623e-01 -0.5296082814 1.2322514088 x 0.0141986623 4.211134e-03 *** 0.0059449912 0.0224523335 y -0.0176586862 2.966964e-03 *** -0.0234738292 -0.0118435432 dens 0.0004128483 4.701333e-05 *** 0.0003207039 0.0005049927 Warning message: model was fitted by gam(); asymptotic variance calculation ignores this > fit2.utm Nonstationary multitype Poisson process Possible marks: A Asia1 O Unknown Trend formula: ~x + y + dens Fitted coefficients for trend formula: (Intercept) x y dens -2.193521e+01 1.991195e-07 -1.734678e-07 5.609414e+00 Warning message: model was fitted by gam(); asymptotic variance calculation ignores this and any attempt to plot fit2.utm fails - I suspect this is to do with 'empty' cells or the cell intensity? Is there some way around this? cheers Ben sessionInfo() R version 2.13.0 Patched (2011-05-26 r55996) Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit) locale: [1] en_AU.UTF-8/en_AU.UTF-8/C/C/en_AU.UTF-8/en_AU.UTF-8 attached base packages: [1] grid stats graphics grDevices utils datasets methods [8] base other attached packages: [1] spatstat_1.22-3 RandomFields_2.0.45 deldir_0.0-14 [4] mgcv_1.7-6 rgdal_0.6-33 Cairo_1.4-9 [7] surveillance_1.2-1 Matrix_0.999375-50 msm_1.0.1 [10] vcd_1.2-11 colorspace_1.1-0 MASS_7.3-13 [13] maptools_0.8-9 lattice_0.19-30 foreign_0.8-44 [16] spc_0.4.0 xtable_1.5-6 rgeos_0.1-4 [19] stringr_0.5 sp_0.9-83 zoo_1.6-5 [22] RColorBrewer_1.0-5 RPostgreSQL_0.1-7 DBI_0.2-5 loaded via a namespace (and not attached): [1] mvtnorm_0.9-9991 nlme_3.1-101 plyr_1.5.2 splines_2.13.0 [5] survival_2.36-9 tools_2.13.0 _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo