similar to: ANOVA procedure on the sufficient statistics

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

Dear all, I recently had to review the current litterature about some medical treatment with two possible variants (let's call them A and B). I collected all available prospective randomized trials about this treatment : I got four trials for the A variant and three for the B variant, all studies comparing one variant to a "suitably choosen" placebo. Two classes of variables are of

posting the question in r-help@ to get more feedback :-) thanks, Yun-Fang ----- Original Message ----- From: "Yun-Fang Juan" <yunfang at yahoo-inc.com> To: <s-news at wubios.wustl.edu> Sent: Tuesday, December 16, 2003 2:04 PM Subject: [S] plot stacked bar chart in R > Hi, > I am trying to plot a stacked bar chart in R but am not able to find the > documentation.

All Is there an R command to produce error bars around a plotted point. Crawley's book uses the command error.bar() but my version of R rejects it. Must be an S+ command(?). Thanks REX [[alternative HTML version deleted]]

Hi, I try to use lm to fit a linear model with 600k rows and 70 attributes. But I can't even load the data into the R environment. The error message says the vector memory is used up. Is there anyone having experience with large datasets in R? (I bet) Please advise. thanks, Yun-Fang [[alternative HTML version deleted]]

Hi there, I'd like to plot some maps. Is it possible for me to use SAS map data in R? Thank you. Best, Junjie _________________________________________________________________ your life. [[alternative HTML version deleted]]

Hello, I have a file with two dependent variables (three and five) and one independent variable. I do i.mod <- lm(cbind(three, five) ~ species, data=i.txt) and get the following output: Coefficients: three five (Intercept) 9.949 9.586 species -1.166 -1.156 I do a" i.can<-candisc(i.mod,data=i): and get the following output: Canonical Discriminant Analysis

Hi I am using the heatmap function in package mva to look at large correlation matrices visually. Is there any way to "flip" the output of heatmap plot left-right so that, if presented with a correlation matrix, it plots the unity elements in the correlation matrix along a diagonal from top left to bottom right? For example: library(mva) x = matrix(rnorm(1000), ncol=10) z = cor(x)

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 <-

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

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

I'd like to do some plots of historical event data on a reverse log scale, started, say at the year 2000 and going backwards in time, with tick marks spaced according to log(2000-year). For example, see: http://euclid.psych.yorku.ca/SCS/Gallery/images/log-timeline.gif As an example, I'd like to create a density plot of such data with the horizontal axis reverse-logged, a

== 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

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]

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

[Env: WinXp, R 2.9.2] In my .Rprofile, I define a number of utility functions I'd like to have available in my R session, but don't want them to be *normally* listed by ls(), or more importantly, saved if I save my session variables/functions. How can I do this? -- Michael Friendly Email: friendly AT yorku DOT ca Professor, Psychology Dept. York University Voice: 416

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

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))