similar to: validity of means comparison: unbalanced + weights

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, is it possible to reverse the order in which panel.lmline() or panel.smooth() operation in xyplot() ? This type of situation might occur when plotting some variable with depth, but the relation we want to describe is variable ~ depth, and not depth ~ variable, as the plotting formula would suggest. # an example: d <- 1:100 v <- d * rnorm(100) xyplot(d ~ v, ylim=c(100,0),

Hi, I was recently splitting some massive phylo class objects with extract.clade() and noticed what appears to be a bug in how tip labels are copied from the full tree to the pruned tree. This possible bug was also mentioned here: http://www.mail-archive.com/r-sig-phylo at r-project.org/msg00537.html An example: library(ape) set.seed(5) x <- matrix(rnorm(100), ncol=10) p <-

Hi, I am trying to transition a system based on dynamic image generation (via R) from our development system to a production environment. Our R script functions as expected when run by a regular user. However the script dies when calling the png() function, when started by the webserver user. Here are some details >sessionInfo() R version 2.9.2 (2009-08-24) i686-pc-linux-gnu locale: C

Hi, I have fit a series of ols() models, by group, in this manner: l <- ols(y ~ rcs(x, 4)) ... where the series of 'x' values in each group is the same, however knots are not always identical between groups. The result is a table of 'coefs' derived from the ols objects, by group: group Intercept top top' top'' 1 6.864 0.01 2.241 -2.65

Hi, I have been trying to use the new .parallel argument with the most recent version of plyr [1] to speed up some tasks. I can run the example in the NEWS file [1], and it seems to be working correctly. However, R will only use a single core when I try to apply this same approach with ddply(). 1. http://cran.r-project.org/web/packages/plyr/NEWS Watching my CPUs I see that in both cases

I expected, that I will get the same prediction, if I multiply the weights for all classes with a constant factor, but I got different results. Please look for the following code. > library(e1071) > data(Glass, package = "mlbench") > index <- 1:nrow(Glass) > testindex <- sample(index, trunc(length(index)/5)) > testset <- Glass[testindex, ] > trainset <-

I think there are two bugs in aov() that shows up when the right hand side of `formula' contains both `-1' and an Error() term, e.g., aov(y ~ a + b - 1 + Error(c), ...). Without `-1' or `Error()' there is no problem. I've included and example, and the source of aov() with suggested fixes below. The first bug (labeled BUG 1 below) creates an extra, empty stratum inside

I believe you are right, but can you please explain why anyone would want to fit this model? It differs only in the coding from aov(y ~ a + b + Error(c), data=test.df) and merely lumps together the top two strata. There is a much simpler fix: in the line if(intercept) nmstrata <- c("(Intercept)", nmstrata) remove the condition (and drop the empty stratum later if you

Hi, after upgrading my R install from 2.5 -> 2.6.1 and performing multiple iterations of update.packages(), I am getting an odd error when trying to plot a calibration curve from the val.prob() function in package Design. when running this function (which used to work) I get the following error message: Error in .C("lowess", x = as.double(xy$x[o]), as.double(xy$y[o]), n,

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

Hi, Is there any way to suppress plotting of panels that don't actually contain any information? I have tried using 'drop.unused.levels=TRUE', but there doesn't seem to be any effect. Here is an example: library(lattice) # some fake data: d <- data.frame(x=runif(20), x.class=rep(letters[1:5], each=4), f1=rep(letters[1:2], each=10), f2=rep(letters[10:19], each=2) ) # plot

Hi, I am looking for some tips on how to incorporate known measurement error into the comparison of slopes in an analysis of covariance. Specifically, if I know that each measurement comes with a 5% error, is it possible to 'expand' the confidence intervals around the estimates for the slope of the line passing through the data defined by the grouping variable? With standard linear

Hi, I have fit a model using the rms package with the Gls() function. Is there a way to get the model estimates, std errors, and p-values (i.e. what you get with print(fit)) into latex format? I have tried: f <- Gls(...) latex(f, file='') ... but I get the following error Error in replace.substring.wild(s, old, new, test = test, front = front, : does not handle > 1 * in

Hi, Is anyone on the list familiar with an R implementation of Piper Diagrams? Example: http://faculty.uml.edu/nelson_eby/89.315/IMAGES/Figure%209-78.jpg I am thinking that two calls to triax.plot (plotrix) along with some kind of affine-transformed standard plot would do the trick. Not so sure about the final layout, or a nice generalized version for something like lattice. Cheers, Dylan

The following warning message occurs when running the nls on some data: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Warning message: In is.na(wts) : is.na() applied to non-(list or vector) of type 'NULL' ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ and I would like to know what is causing it and how I can fix it. As an example, the following, from Venables and Ripley, which

Hi, Using the latest rms package I am able to make nice plots of model predictions +/- desired confidence intervals like this: # need this library(rms) # setup data d <- data.frame(x=rnorm(100), y=rnorm(100)) dd <- datadist(d) options(datadist='dd') # fit model l <- ols(y ~ rcs(x), data=d) # predict along original limits of data l.pred <- Predict(l) # plot of fit and

I'm wondering whether anyone has any insight into why the 'simulate' methods for the built-in glm() families (binomial, Poisson, Gamma ...) extract the prior weights using object$prior.weights rather than weights(object,"prior") ? At first I thought this was so that things work correctly when e.g. subset= and na.action=na.exclude are used. However, the current versions of

Hi, I have setup a simple logistic regression model with the glm() function, with the follow formula: y ~ a + b where: 'a' is a continuous variable stratified by the levels of 'b' Looking over the manual for model specification, it seems that coefficients for unordered factors are given 'against' the first level of that factor. This makes for difficult

Hello list, I am faced with a two-class classification problem with highly asymetric class sizes (class one: 99%, class two: 1%). I'd like to obtain a class probability model, also introducing available information on the class prior. Calling kernlab/ksvm with the line > ksvm_model1<-ksvm(as.matrix(slides), as.factor(Class), class.weights= c("0" =99, "1" =1),