similar to: logistic regression model specification

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 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

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

Hi, I am curious about how to interpret the results of a polynomial regression-- using poly(raw=TRUE) vs. poly(raw=FALSE). set.seed(123456) x <- rnorm(100) y <- jitter(1*x + 2*x^2 + 3*x^3 , 250) plot(y ~ x) l.poly <- lm(y ~ poly(x, 3)) l.poly.raw <- lm(y ~ poly(x, 3, raw=TRUE)) s <- seq(-3, 3, by=0.1) lines(s, predict(l.poly, data.frame(x=s)), col=1) lines(s,

Hi, How would one go about determining if the slope terms from an analysis of covariance model are different from eachother? Based on the example from MASS: library(MASS) # parallel slope model l.para <- lm(Temp ~ Gas + Insul, data=whiteside) # multiple slope model l.mult <- lm(Temp ~ Insul/Gas -1, data=whiteside) # compare nested models: anova(l.para, l.mult) Analysis of Variance

Hi, Running the following example from ?Predict() throws an error I have never seen before: set.seed(1) x1 <- runif(300) x2 <- runif(300) ddist <- datadist(x1,x2); options(datadist='ddist') y <- exp(x1+ x2 - 1 + rnorm(300)) f <- ols(log(y) ~ pol(x1,2) + x2) p1 <- Predict(f, x1=., conf.type='mean') Error in paste(nmc[i], "=", if (is.numeric(x))

Hi, Are there any functions in R that could be used to estimate the phase-shift between two semi-sinusoidal vectors? Here is what I have tried so far, using the spectrum() function -- possibly incorrectly: # generate some fake data, normalized to unit circle x <- jitter(seq(-2*pi, 2*pi, by=0.1), amount=pi/8) # functions defining two out-of-phase phenomena f1 <- function(x)

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, 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, 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 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, 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

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

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, 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, I have a quick statistics-related question. I would like to perform a comparison of means across treatments using a linear model framework. However, my 'baseline' treatment has many more observations than either of the other two treatments and the variance within each treatment is different. Furthermore, each observation is assigned a weight-- where weights within a treatment sum

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

Greetings, I am a bit confused by the results returned by the functions: cohen.kappa {concord} classAgreement {e1071} when using a two-way table. for example, if I have an matrix A, and a similar matrix B (same dimensions), then: matrix A and B can be found: http://casoilresource.lawr.ucdavis.edu/drupal/files/a_40.txt http://casoilresource.lawr.ucdavis.edu/drupal/files/b_40.txt A <-

Hi, I recently upgraded to R 2.9.0 on linux x86. After doing so, I switched to the RPostgreSQL package for interfacing with a postgresql database. I am using postgresql 8.3.7. A query that works from the postgresql terminal is causing a segfault when executed from R. My sessionInfo, the error message, and the R code used to generate the error are listed below. I have noticed that a