similar to: Getting univariate information from a multivariate data set

Quick question about the usage of glht. I'm working with a data set from an experiment where the response is bounded at 0 whose variance increases with the mean, and is continuous. A Gamma error distribution with a log link seemed like the logical choice, and so I've modeled it as such. However, when I use glht to look for differences between groups, I get significant

Hey, all. I had a quick question about fitting new glm values and then looking at the error around them. I'm working with a glm using a Gamma distribution and a log link with two types of treatments. However, when I then look at the predicted values for each category, I find for the one that is close to 0, the error (using se.fit=T with predicted) actually makes it overlap 0.

Dear R folk, First off I want to thank those of you who responded with comments for my R quick and dirty stats tutorial. They've been quite helpful, and I'm in the process of revising them. When it comes to repeated measures MANOVA, I'm in a bit of a bind, however. I'm beginning to see that all of the documentation is written for psychologists, who have a slightly

I have a slight conundrum. I'm attempting to write a scrip that will take a number of objects (lm, glm, and lmer) and return AIC scores and weights. I've run into 3 problems, and was wondering if anyone had any pointers. 1) is there any convenient way to extract the name of the objects? Simply, if I have a vector of objects c(my.lm, my.lmer) and I want to get a character

Hey, all, I'm trying to construct a centroid plot using canonical values from a MANOVA. I know that from the summary.manova object you can get Eigenvalues, and the H and E matrices (from SS$Treatment and SS$Residuals), but I am at a loss to get the loadings for the canonical values, nor values for the centroid centers and radii. Is there a package that does this that I am just missing,

Greetings to all, First off, I want to thank you all for answering any nagging questions I've had over the past few days. I've been in the process of putting together A Quick and (Very) Dirty Intro to Doing Your Statistics in R (which I have posted to http://didemnid.ucdavis.edu/rtutorial.html ) in order to teach an R workshop for the graduate students in my department. This is a

I'm working on a program that loads several large data files. I'm using ddply (plyr is really awesome) but I want to minimize the amount of times a large data file is read in. One solution is to keep track of which data file is open with a global variable and then change the currently open data file globally as needed. However, I'm unclear on how to alter a global variable

I'm working with a few functions (e.g. do.base.descriptions in the netstat package) that, in addition to returning an object with variables I want to extract, also print output. There is no way to turn this default printing behavior off in many of the functions. Is there a blanket way to suppress such printing, say, within a loop or a ddply statement? Thanks! -Jarrett

I've tried several methods in OS X, and here's what works best for me. Save the R graphic as a PDF file. Open it with Apple's "Preview" application, and save it as a PNG file. The resulting .png file can be inserted into MS Word or PowerPoint, can be resized, and looks good on either OS X or Windows. There are other programs available for translating the pdf file to png

anova.lm() gives the sequential tests: > set.seed(1) > dat <- data.frame(y=rnorm(10), x1=runif(10), x2=runif(10)) > anova(lm(y ~ x1 + x2, dat)) Analysis of Variance Table Response: y Df Sum Sq Mean Sq F value Pr(>F) x1 1 1.1483 1.1483 2.0943 0.1911 x2 1 0.4972 0.4972 0.9068 0.3727 Residuals 7 3.8383 0.5483 > anova(lm(y ~ x2 + x1,

Hey, all. I'm looking for packages that are good at two things 1) Drawing directed graphs (i.e nodes and edges), both with single and double headed arrows, as well as allowing for differences in line width and solid versus dashed. Note: I've tried Rgraphviz here, but have run into some problems (which seem fixable and I may go with it in the end), and it doesn't satisfy need

I'm attempting to write a general function to implement Faraway's bootstrapping algorithm for mixed models with lmer, but have run into a curious problem. I'm comparing two models model.1<-lmer(Response ~ Treatment + (1|Trial), data=exp.data, method="ML") model.2<-lmer(Response ~ 1 + (1|Trial), data=exp.data, method="ML") When I attempt to update

I've been using sna to work with some networks, and am trying to visualize them easily. My networks are hierarchical (food webs). All of the layout engines I've tried with gplot don't seem to plot hierarchical networks, as one would using dot from graphviz. While I could do all of this by outputting to dotfiles and running it through graphviz, the graphics I get from R are much

This is a 'rather than re-invent the wheel' post. Has anyone out there re-written combn so that it can be parallelized - with multicore, snow, or otherwise? I have a job that requires large numbers of combinations, and rather than get all of the index values, then crank it through mclapply, I was wondering if there was a way to just do this natively within a function. Just curious.

Dear R gurus and users, I'm having problems with the use of write.table. I have a 28-variables data frame create at each cycle of a loop; it can contain between 2000 and 3000 rows for each cycle. After each cycle the data frame is written out to a file with the "append=TRUE" option and then removed from memory. These are the couple of lines involved: > data2 <-

Hi Everyone, I am trying to understand the unstack() function but after struggling for two days, I have given up. More specifically, I am trying the exercises at the end of Chapter 1 of Data Analysis and Graphics Using R by Maindonald and Braun, 2nd ed. Exercise 18 (p. 41) asks to unstack the Rabbit data frame from the MASS package to get a certain data frame that is shown in the exercise.

I've been reading the R mail archives and I've found a lot of messages with this same kind of problem, but I can't understand the answers. Can one of you try to explain this to me? Here's my example. Given a regression model and a variable, I want to use unstack() on the vector of residuals and make some magic with the result. But unstack hates me. PCSE <- function

Dear All, I fear I am badly misunderstanding something fundamental about the mclust package. Please considered the dataset pasted at the end of the email (you can also have a look at http://dl.dropbox.com/u/5685598/dataedges.csv ). Now, I would like to use the info on columns 1,2 and 4 to predict the value in column 3. However, when I run the script below (obg_mining_script.R), I get

Folks, I have the following dataframe: > x <- structure(list(name = c("EU B", "EU B", "EU B", "EU B", "EU B", "EU B", "AU A", "AU A", "AU A", "AU A", "AU A", "AU A"), date = c("2010-10-11", "2010-10-12", "2010-10-13",

thank you for your quick answer, I'm far of the digits capacity and my values are not the result of a computation. I'm developping a R package to acces a specific data source. And I need precision a few better. How can I do ? When I try this In R console, this is correct and what I need : > my_value <- 29.958334 > my_value == 29.958334 [1] TRUE But I need to do the first