similar to: any good way to convert sp class objects to splancs object

Dear all, I had to place points at random, one in each of larger number of polygons (actually in objects of class 'SpatialPolygonsDataFrame' , see sp library), and tried first to do it using spsample (from sp). Surprisingly, every 5-15 trials, the output was a NULL value. The doc says that ' this may occur when trying to hit a small and awkwardly shaped polygon in a large

Greetings: As I am not a windows user, I cannot try this: is it possible to install rgdal on windows without having to compile it from source ? Compilation on MacOS is within my abilities, however each time i try and install the rgdal package it dies complaining that it cannot find gdal-config --- which was recently installed with GRASS. I have updated my PATH environment variable, logged

Hi, I have developed a logistic regression model in the form of (factor_1~ numeric + factor_2) and would like to perform a cross-validation or some similar form of sensitivity analysis on this model. using cv.glm() from the boot package: # dataframe from which model was built in 'z' # model is called 'm_geo.lrm' # as suggested in the man page for a binomial model: cost <-

Greetings, Experimenting with the cluster package, and am starting to scratch my head in regards to the *best* way to standardize my data. Both functions can pre-standardize columns in a dataframe. according to the manual: Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the variable's mean absolute deviation. This

Greetings, I am trying to add an extra labled axis in position 3 (top x-axis), with numbers that do not match up with the existing axes. Surely this must be possible, and I am just doing it incorectly. So far I have tried the following: #make a plot plot(TIK, type="l", cex=.25, xlim=c(2,32), ylim=c(0,1600)) #try and add a new axis with different numbers in position 3

Greetings, I have had good success using the clara() function to perform a simple cluster analysis on a large dataset (1 million+ records with 9 variables). Since the clara function is a wrapper to pam(), which will accept known medoid data - I am wondering if this too is possible with clara() ... The documentation does not suggest that this is possible. Essentially I am trying to

Getting a very strange error with a new install of R from source on x86; make[3]: Leaving directory `/tmp/R.INSTALL.r20887/cluster/src' ** R ** data ** moving datasets to lazyload DB Error in factor(c(1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1), : invalid labels; length 2 should be 1 or 1 Execution halted ERROR: lazydata failed for package 'cluster' ** Removing

Hi everyone, Suppose I have a count the occurrences of positive results, and the total number of occurrences: pos <- 14 total <- 15 testing that the proportion of positive occurrences is greater than 0.5 gives a p-value and confidence interval: prop.test( pos, total, p=0.5, alternative='greater') 1-sample proportions test with continuity correction data: 14 out of

Greetings, I am using both the lrm() {Design} and glm( , family=binomial()) to perform a a logisitic regression in R. Apart from the typical summary() methods, what other methods of diagnosing logistic regression models does R provide? i.e. plotting an 'lm' object, etc. Secondly, is there any facility to calculate the R^{2)_{L} as suggested by Menard in "Applied Logistic

This may sound like a very naive question, but... give two lists of coordinate pairs (x,y - Cartesian space) is there any simple way to compute the affine transformation matrix in R. I have a set of data which is offset from where i know it should be. I have coordinates of the current data, and matching coordinates of where the data should be. I need to compute the composition of the affine

Greetings, After pouring over the documentation for the 'colorspace' package, I have not been able to figure out how the plot() method converts colorspace coordinates to RGB values for display on the screen. I am convert between colorspaces with the various as() methods... but cannot seem to find a way to extract RGB (i.e. for displaying on a computer screen) triplets from color space

Hi, I have a problem that concerns combination of the package Maptools and Splancs I have 2 shapefiles that i want to manipulate (one of type point and one polygon).I import them in R using Maptools but then i can't estimate a quartic Kernel using Splancs. The package doesn't recognize the shapes (invalid points and poly argument).I don't know if this is an easy task but i have

I am trying to port the splancs library for S++ version 4.0 to R. I have modified the original FORTRAN to use dllwrap/dlltool (e.g. removed common blocks). I have been able to generate dll's (splancs.dll) using dllwrap/dlltool and Ming32 g77 with a -mdll switch. As of yet I have not been able to generate a dll that can be loaded by R. Does anyone out there have any suggestions. An example

Greetings, A couple general questions regarding the use of splines to interpolate depth profile data. Here is an example of a set of depths, with associated attributes for a given soil profile, along with a function for calculating midpoints from a set of soil horizon boundaries: #calculate midpoints: mid <- function(x) { for( i in 1:length(x)) { if( i > 1) { a[i] = (x[i] -

Hi, Is there a simple way to suppress the plotting of polygon boundaries with spplot() ? # simple list of 12 colors cols <- brewer.pal(12, "Paired") # plot pile of polygons, with 12 classes: spplot(x, zcol='class2', col.regions=cols, scales=list(draw=T), xlab="Easting (m)", ylab="Northing (m)") ... seems to work well. However the polygon boundaries

I have a problem with function 'inout()' in package 'splancs' on CRAN-R Version 1.2.3 under FreeBSD4.3-STABLE. The following script produces and draws points and a polygon-surrounded area. Repeating the same script many times shows, that points on the polygon-line often, but not always, are outside of the polygon-area. library(splancs) # dataset with polygon (convex

Greetings, I have two matrices, read in from raster data stored in GRASS. Each matrix represents the output of a aggregation process on categorical data (Nomial / Ordinal) - derived from imagery. I have compared these two data by the following methods: ***** pretending the data is continuous approaches (flawed i am sure...) 1. computing a difference map by computing the absolute difference

Hi, I would need some help with the splans package in R. I am using a Shapefile (downloadable at) http://rapidshare.com/files/215206891/Redlands_Crime.zip and the following execution code setwd("C:\\Documents and Settings\\Dejan\\Desktop\\GIS\\assignment6\\DataSet_Redlands_Crime\\Redlands_Crime") library(foreign) library(splancs) auto_xy<-read.dbf("Auto_theft_98.dbf")

Hello, I am trying to conduct a bi-variate ripley's k using the k12hat function in the splancs package (found here http://rss.acs.unt.edu/Rdoc/library/splancs/html/k12hat.html). Although, I receive an error when getting to the bboxx part of the code: poly <- list(x=c(fire4$X, nar4$X), y=c(fire4$Y, nar4$Y)) plot(seq(5,80,5), sqrt(k12hat(fire4), as.points(nar4),

Dear forum, I would like to use the kernel2d or spkernel2d in the Splancs-package, but it does not recognize my polygon data. "Error in kernel2d(as.points(ptsbin), polygonprov, h0 = 2, nx = 100, : " is the error message. Invalid poly argument The data are defined as follows: polgonprov<-list(x=polyprov$X, y=polyprov$Y) with X and Y coordinates in the Lambert1972