similar to: suppress printing within a function

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

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

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

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

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.

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

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

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 using lm to run an ANOVA, and would like to use Ryan's Q as my post-hoc (as recommended by Day and Quinn, 1989, Ecological Monographs). I can't seem to find any methods in the base stats package that implement this post-hoc. Is there a good package of post-hoc methods out there, or has someone written a method for Ryan's Q previously? Thanks! -Jarrett

I'm attempting to analyze some survey data comparing multiple docks. I surveyed all of the slips within each dock, but as slips are nested within docks, getting multiple samples per slip, and don't really represent any meaningful gradient, slip is a random effect. There are also an unequal number of slips at each dock. I'm having syntactical issues, however. When I try

I have formulae with coefficents that I would like to update. However, I get some strange results. For example, see the following: For the formula y ~ d+ 3*r+t I want to add a variable p, so > update(y~d+0*r+t, .~.+p) produces y ~ d + t + p - 1 If the coefficient is not 0, but rather, something else - say, 3, I get the following: > update(y~d+3*r+t, .~.+p) Error in

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

Hello. A quick question about incorporating variation due to study in the metafor package. I'm working with a particular data set for meta-analysis where some studies have multiple measurements. Others do not. So, let's say the effect I'm looking at is response to two different kinds of drug treatment - let's call their effect sizes T1 and T2. Some studies have multiple

I'm running r 2. on a mac running 10.6.4 and a dual-core macbook pro. I'm having a funny time with multicore. When I run it with 2 cores, mclapply, R borks with the following error. The process has forked and you cannot use this CoreFoundation functionality safely. You MUST exec(). Break on __THE_PROCESS_HAS_FORKED_AND_YOU_CANNOT_USE_THIS_COREFOUNDATION_FUNCTIONALITY___YOU_MUST_EXEC__()

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.

I'm working with large data frames and running out of memory. I hope some of you may be able to suggest a more efficient approach. I have grid/lattice data representing a time series of 1 m2 quadrats in a grassland: Each 1 cm2 cell or pixel contains one ecological state (ie grass or bare ground). The goal is to calculate, for each cell, the transition probabilities to all available

I'm currently working on writing up some documentation for some of my code, but am having the darndest time coding in equations. For example, the equation in the following: \details{ Calculated the R Squared for observed endogenous variables in a structural equation model, as well as several other useful summary statistics about the error in thoe variables. R Squared values are

Hello, R-i-zens. I'm working on an data set with a factorial ANOVA that has a significant interaction. I'm interested in seeing whether the simple effects are different from 0, and I'm pondering how to do this. So, I have my.anova<-lm(response ~ trtA*trtB) The output for which gives me a number of coefficients and whether they are different from 0. However, I want the

I had a question about scan in R. For better code readability, I would like to have lines in the block of data to be scanned that are commented - not just lines that have a comment at the end. For example #age, weight, height 33,128,65 34,56,155 instead of having to do something like 33,128,65 #age, weight, height 34,56,155 Is this at all possible?