similar to: Comparison of correlation coefficients

Hi, I have a dataset like this, > testdata Grouped Data: expr ~ visit | subject expr visit subject 1 6.502782 V1 A 2 6.354506 V1 B 3 6.349184 V1 C 4 6.386301 V2 A 5 6.376405 V2 B 6 6.758640 V2 C 7 6.414142 V3 A 8 6.354521 V3 B 9 6.396636 V3 C I tried the command >

Hi I have used a polycor package for categorical correlation coefficients. I run the following script. But there were no results. Could you tell me how to correct the script? Thanks in advance, vars <- names(sdi) for (i in 1:length(vars)) { for (j in 1:length(vars)) { paste(vars[i]," and ", vars[j]) polychor(vars[i], vars[j]) # corr } } -- Kum-Hoe Hwang, Ph.D.Phone :

Hi, 1. How do I construct 95% prediction interval for new x values, for example - x = 30000? 2. How do I construct 95% confidence interval? my dataframe is as follows : >dt structure(list(y = c(26100000, 60500000, 16200000, 30700000, 70100000, 57700000, 46700000, 8600000, 10000000, 61800000, 30200000, 52200000, 71900000, 55000000, 12700000 ), x = c(108000, 136000,

Dear all, I would like to compare two distance matrices to find the correlation between those and create a XY plot.could you please kindly help me and let me know how I can do that using R.(Mantel test?). Your help is very much appreciated!! Many thanks! Regards, Sima Fakheran ............................................................... Sima Fakheran Esfahani PhD Student Institute of

I was hoping to fit along the lines of g<-gl(20,5) y<-runif(100) fit<-lme(fixed=y~g,correlation=corAR1(0,~1|g)) But I get the error "Incompatible formulas for groups in "random" and "correlation"" Any help would be greatly appreciated. Ben

Hi, I would like to estimate something like y = a + b*d2*y + c*d3*y where the dummies are created from some vector d with three (actually many more) levels using factor(). But either there is included the variable y or d1*y. How could I get rid of these? Example: x = c(1,2,3,4,5,6,7,8) y = c(3,6,2,8,7,6,2,4) d = c(1,1,1,2,3,2,3,3) fd = factor(d) lm(x ~ fd*y) gives: Coefficients: (Intercept)

Dear all, I am new to R. I wish to use R's multiple imputation to deal with missing data. I have a data set with the size around 300 observations and 150 variables. I checked the help function in R and could not locate how to write the codes for this. can anyone give a hand? Do appreciate your time and kindness! Q. [[alternative HTML version deleted]]

Hi, I try to calculate Kendall's W coefficient and I have a bizarre error. little.app.mat<-matrix(c(1,3,4,2,6,5,2,4,3,1,5,6,3,2,5,1,5,4),nrow=3,byrow=TRUE) print(kendall.w(little.app.mat[-1,])) >>> Kendall's W for ordinal data >>> W = 0.7753623Error in if (is.na(x$p.table)) { : argument is of length zero

Howyd all? Seems that I have been routed. Possibly by a physical B&E, but who knows? Probably some of you do.... anyways, some politically sensitive email was deleted from a user account and the line low -tr & inserted into my .xinitrc . Duncan (Dhu) Campbell

Hi Tena, I recommend rapache for building websites with R. See http://biostat.mc.vanderbilt.edu/rapache/ -Ista > ---------- Forwarded message ---------- > From: "Tena Sakai" <tsakai at gallo.ucsf.edu> > To: <ted.harding at manchester.ac.uk>, <r-help at r-project.org> > Date: Tue, 28 Apr 2009 15:04:53 -0700 > Subject: Re: [R] Newbie R question PART2 >

Hi. Please, how can I put together 2 or more lines at the same scatterplot ? Example: measures of protein intake (quantitative) of 4 children over 30 days, by day. How to plot all children at same graphic: Protein X Time ? Is there any command like "overlay" ? Thank you, Mauricio

Hi Ian, Per your suggestion, I reinstalled R 2.9.0, then I reinstalled ggplot2 on top. The problem persists. Here's the what happens after the installation: > qplot (carat, price, data = diamonds, alpha = I(1/10)) Warning message: In grid.Call.graphics("L_points", x$x, x$y, x$pch, x$size) : semi-transparency is not supported on this device: reported only once per

I am using library(nnet) to train up an ANN with what I believe is a moderately sized dataset, but R is complaining about too many weights: --- > nn.1 <- nnet(t(data), targets, size = 4, rang = 0.1, decay = 5e-4, maxit = 200) Error in nnet.default(t(data), targets, size = 4, rang = 0.1, decay = 5e-04, : Too many (1614) weights > dim(targets) [1] 146 2 > dim(data) ## Note

Hello, I am trying to model an intensity function with time-varying covariates. Before, I have successfully defined a log likelihood function for a Power-Law Process (lambda(t)=alpha*beta*t^(beta-1)) with two paramters and no covariates for a repairable systems with failure times (t). This function was maximized with R optim. No problem! But now I want to include a covariate indicating a

The expected number of bladder cancer over next 20 years a tire industry is 1.8. Poission distribution is assumed to hold and 6 reported deaths are caused by bladder cancer among the employees. Trying to find how unusual this event is. > ppois(q=6, lambda=1.8, lower.tail = TRUE, log.p = FALSE) [1] 0.9974306 not sure if ppois is the right one to use and the parameters... thx much

I'm sorry to all those who are tired of seeing my email appear in need of help. But, I've never coded in any program before, so this has been a difficult process for me. Is there a simple function to test for colinearity in R? I'm running a logistic regression and a linear regression. Thanks for the help! [[alternative HTML version deleted]]

Hi, I have a problem with aggregate. x <- aggregate(t1,list(t2,t3,t4), mean) z<-x[,3] I want z to be a vector but it is a factor. I've tried to use as.vector(z,mode="numeric") but then the numbers get scrambeled. Any help is appriciated /anders

Hi, I'm having some problems with a bubble plot (ps package). I don't want tick marks on all four sides (just two), I want to have a smaller font size, and I would like to be able to define bubble sizes shown in the legend (now it shows 0, 0, 0, 9.747 and 4265.757 which is not really convenient. Passing some of the standard plot arguments didn't help (in fact, nothing changed).

Hi, I have Forecast Class and Observed Class in a data matrix as below. > Sample1 FCT OBS 1 1 5 2 2 4 3 3- 3+ 4 3 3 5 3+ 3- 6 4 2 7 5 1 I want to find the difference between Observed and Forecast Classes. How can I get this done? I tried to following to convert the 1 through 5 classes, to 1 through 7 for both OBS and FCT column. > Sample1$OBS2 <- Sample1$OBS

Dear Ioannis Thank you very much for pointing me to meta-analysis. Although it may not solve my problem with the normalization, it gives me some other options to display the different correlation coefficients. One possibility is the use of Funnel plots, which are even available in library(rmeta). Another possibility is the use of forest-plots, as implemented in rmeta as metaplot. Sorrowly,