similar to: hiding/protecting utility functions in .Rprofile

I have a list of vectors, all forced to be the same length: testlist <- list( shape=c(0, 0, 2), cell.fill=c("red","blue","green"), back.fill=rep("white",3), scale.max=rep(100,3) ) > str(testlist) List of 4 $ shape : num [1:3] 0 0 2 $ cell.fill: chr [1:3] "red" "blue" "green" $ back.fill: chr [1:3]

Is there an R implementation of a scheme for automatic smoothing parameter selection with loess, e.g., by minimizing one of the AIC/GCV statistics discussed by Hurvich, Simonoff & Tsai (1998)? Below is a function that calculates the relevant values of AICC, AICC1 and GCV--- I think, because I to guess from the names of the components returned in a loess object. I guess I could use

I'm trying to form contingincy tables among a set of character variables which were read from a .csv file and have missing represented as "". I want to exclude the missing levels from the table. > levels(CPIC) [1] "" "N" "Y" > levels(Manix) [1] "" "N" "Y" > xtabs(~CPIC + Manix, exclude=c("",NA))

In an R graphic, I'm using cond.col <- c("green", "yellow", "red") to represent a quantitative variable, where green means 'OK', yellow represents 'warning' and red represents 'danger'. Using these particular color names, in B/W, red is darkest and yellow is lightest. I'd like to find color designations to replace yellow and

I'm sure this is probably simple, but I can't find an answer... I have a data frame (Galton's data on heights of parents and children), in grouped form, parent child frequency 73.5 72.2 1 73.5 73.2 3 72.5 68.2 1 72.5 69.2 2 72.5 70.2 1 72.5 71.2 2 72.5 72.2 7 72.5 73.2 2 72.5 74.2 4 71.5 65.2 1 71.5 66.2 3 71.5 67.2 4 71.5 68.2 3 71.5 69.2 5 71.5 70.2 10 ... and need the ungrouped

For an historical paper I'm working on, I have some Spanish plaintext, presently in the form of a Word .doc file, http://euclid.psych.yorku.ca/SCS/Gallery/images/Private/Langren/Verdadera-spanish-stripped.doc and also some ciphered text from the same original source. The ultimate goal is to use some frequency analysis of letters and word lengths in the plaintext to help decode the

[Using R 2.7.2 on Windows XP] After re-building our heplots package, I've begun to get the following error from sessionInfo(), even though it passes R CMD check and builds without errors: > sessionInfo() Error in x$Priority : $ operator is invalid for atomic vectors In addition: Warning message: In FUN(c("MASS", "heplots", "car", "rgl",

[Env: Win XP, R 2.14.2, R 2.15.0] I have a replacement function for setwd() in my .Rprofile which displays the current R path in the R window title. It no longer works in R 2.15.x, giving the error below. Worse, the error prevents the rest of my .Rprofile script from completing. Is there some way to rescue this, i.e., preserve this behavior in R 2.15? If not, how can I modify my script so it

Hi, I have a two-way anova with unequal cell numbers that I want to analyze. The problem is I don't have individual observations of the data. I only have the sufficient statistics (mean, variance, # of observations) for each cell. Is there any existing function in S-plus that would allow me to do aov() with the sufficient statistics? The table is like G1 G2 G3 G5 G6 T1 T2 T3

In the following, I'm fitting a logistic regression model, and using car:::influencePlot. When I run the latter with output to the screen, it calls identify() that lets me label observations with large CookD. However, if I use postscript() to get .eps output, identify() seems not to be called at all. If instead, I use dev.copy2eps() after getting output to the screen, the point labels

For a function that takes an argument as a list of lists of parameters, I'd like to be able to convert that to a data.frame and vice versa, but can't quite figure out how. pats <- list(structure(list(shape = 0, shape.col = "black", shape.lty = 1, cell.fill = "white", back.fill = "white", label = 1, label.size = 1, ref.col = "gray80",

I've just installed R 1.6.1 and would like to install all the contributed packages I had used in 1.5.1. It's not too hard to do this manually from Rgui, but I wondered if there is an easier way, something like writing the results of installed.packages() from 1.5.1 to a file and using that with install.packages() in 1.6.1, or just having 1.6.1 read what's in rw151/library. --

How can I read a space-delimited file, where the data values for each case are folded before column 80, and so appear on two lines for each case? The first few cases look like this loc type bio H2S sal Eh7 pH buf P K Ca Mg Na Mn Zn Cu NH4 OI DVEG 676 -610 33 -290 5.00 2.34 20.238 1441.67 2150.00 5169.05 35184.5 14.2857 16.4524 5.02381 59.524 OI DVEG 516 -570 35 -268 4.75 2.66 15.591 1299.19

I have two series of events over time and I want to construct a graph of the relative frequency/density of these events that allows their distributions to be sensibly compared. The events are the milestones items in my project on milestones in the history of data visualization [1], and I want to compare trends in Europe vs. North America. I decided to use a graph of two overlaid density

== Summary == * Problem: BibTeX entries extracted from R packages via citation() require too much manual editing to be of general use. * Proposal: Date: fields should be made mandatory in package DESCRIPTION files, perhaps beginning with warnings from R CMD check * Proposal: Package authors should be encouraged to use a (new) Contributors: field in the DESCRIPTION file rather than packing all

Given a data frame consisting of a pointID and 12 pairs of (lat, long) variables, with names latA, longA, latB, longB, ... latL, longL, I want to reshape it into a data frame with the structure point source lat long 1 A ... ... 1 B ... ... I've looked at reshape and plyr, but can't figure out how to do this. Details of my data frame are below

Hello, for my meta-analysis I try to test if two varainces are equal without using the raw scores. I have is the SD's, N's and the Means. I want to test the variances from dependent and independend samples. I assume I can use the var.test procedure for the independent samples, but what about the dependent samples ? Has anyone an idea how to realise this with R ? Thanks in advance

The question is how to use a URL in place of a file= argument for read.*.functions that do not support this internally. e.g., utils::read.table() and her family all support a file= argument that can take a URL equally well as a local file. So, if I have a file on the web, I can equally well do > langren <- read.csv("langrens.csv", header=TRUE) > langren <-

I'm trying to test an extension of mosaic() from the vcd package that requires a change to the basic strucplot() function from that package. I want to test my change by sourcing the replacement function into my R session. But when I do that, source("c:/R/mosaics/strucplot-MF.R") and run my extension, it is apparent that it is vcd:::strucplot that is eventually called, not my

How can I get the result of, e.g., poly(1:3. degree=2) to give me the unnormalized integer coefficients usually used to explain orthogonal polynomial contrasts, e.g, -1 1 0 -2 1 1 As I understand things, the columns of x^{1:degree} are first centered and then are normalized by 1/sqrt(col sum of squares), but I can't see how to relate this to what is returned by poly(). >