similar to: Pairwise n for large correlation tables?

Hello, The code below is supposed to be a wrapper for matplot to do columnwise visible comparison of several matrices, but I'm doing something wrong because I can't access an argument called 'colnum'. I'd be most grateful for some insight. Thanks, John Thaden Little Rock, AR ################################ # mmatplot is a matplot wrapper to compare the same column of #

>I have a series of write statements because >i am writing to a file >where the characters strings are the column names of a dataframe >and the numbers are the elements in a particular row. >So, a file might look like > >AAA 2.1 >BB 3.1 >AHLZ 0.2 > >and it would be named "rowname".mls. > >so, each time i get to a new row, i create a new file and

Dear Colleagues, I've been searching for a post or article or something which explains why having na.rm=FALSE or na.action=na.fail as the default is a better choice than TRUE or na.omit. I understand the basic argument: it does not make sense to average a nonexistance into an aggregate, and removing them implicitly leads to accidental pairwise deletion in some cases, and sum(x) /

I await Luke''s node settings implementation with interest. At the moment however, I have this sort of ugliness: $site = $hostname ? { fred => "opsera", barney => "bedrock", default => "unknown site", ... } So I''ve knocked up this little function to use CSV files instead. Now I can just do: $site =

Hi all, This is a very basic question, but I just can't figure out why R is handling a loop I'm writing the way it is. Here is the script I have written: grid_2_series<-function(gage_handle,data_type,filename) series_name<-paste(gage_handle,data_type,sep="_") data_grid<-read.table(file=paste(filename,".txt",sep=""))

Dear R users, suppose I have a matrix of observations for which I calculate all pair-wise correlations: m=matrix(sample(1:100,replace=T),10,10) w=cor(m,use="pairwise.complete.obs") How do I extract only those correlations that are >0.6? w[w>0.6] #obviously doesn?t work, and I can?t find a way around it. I would very much appreciate any help! Best wishes Christoph (using R

Hi list, Could you explain the error I see here? Thanks! ## I'm using R 2.8.0 on WinXP, Hmisc_3.4-3 > table1 <- matrix(10, 180,7) > cell.format <- matrix("", ncol=7, nrow=180) > cell.format[c(seq(3,180,6),seq(4,180,6)),] <- "color{red}" > cell.format[c(seq(5,180,6),seq(6,180,6)),] <- "color{green}" > > latex(table1,

Hello, I have searched and failed for a program or script or method to conduct a power analysis for a MANOVA. My interest is a fairly simple case of 5 dependent variables and a single two-level categorical predictor (though the categories aren't balanced). If anybody happens to know of a script that will do this in R, I'd love to know of it! Otherwise, I'll see about writing one

Hello Lets say as an example I have a dataframe with the following attributes: rownum(1:405), colnum(1:287), year(2000:2009), daily(rownum x colnum x year) and foragePotential (0:1, by 0.01). The data is actually stored in a netcdf file and I'm trying to provide a conceptual version of the data. Ok. I need to calculate a moving mean and a moving variance for each cell on the following

Dear colleagues, I've been running some principal components analyses, which generate tables of loadings that I'm interested in looking at. print(f1$rot$load,cutoff=.4) is what I use, and it gives me what I want. However, I'm now interested in comparing these loadings across a few data sets. In other words, I would like R to match the loadings on rownames() and display them next

Dear colleagues, I run R on a few different machines, and view graphs and the like by tunnelling X through SSH to my local machine. This is useful for me because my local machine can't easily handle some of the data sets I work with. However, when an ssh connection dies, the tunnelled X session also dies, which breaks R's device connection, generating this error: > Error: X11

Dear Colleagues, Occasionally I deal with computer-generated (i.e., websurvey) data files that haven't quite worked correctly. When I try to read the data into R, I get something like this: Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : line 26 did not have 648 elements ...is there any way to get R to tell me how many elements line 26 *did* have? That

Dear all I am trying to do a multiple pairwise comparison test with Tukey-Kramer test as I have unequal sample sizes and variance. I have downloaded the DTK package from CRAN website in my computer for Windows, however I have difficulties in installing it in R. I have unzipped the compressed file in my computer and then I open R, I try to install the package through File - Source R code - DTK

Dear R gurus, I have the following code, but I still not know how to estimate and extract confidence intervals (95%CI) from resampling. Thanks! ~Adriana #data penta<-c(770,729,640,486,450,410,400,340,306,283,278,260,253,242,240,229,201,198,190,186,180,170,168,151,150,148,147,125,117,110,107,104,85,83,80,74,70,66,54,46,45,43,40,38,10) x<-log(penta+1) plot(ecdf(x),

Dear R-help, I have a question about pairwise.t.test and adjustment for multiple comparisons for paired data points. I have the following data: n=c("x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "y", "y", "y", "y", "y", "y",

Dear R-users, Is there a function in R that classifies data in more than 2 groups using logistic regression/classification? I want to compare the c-indices of earlier research (lrm, binary response variables) with new c-indices obtained from 'multiple' (more response variables) logistic regression. Best regards, Stephanie Delalieux Department Biosystems M?-BIORES Group of Geomatics

Dear list, When I use VennDiagram package, I got a error as follow: venn.plot <- draw.pairwise.venn( area1 = 3186, area2 = 325, cross.area = 5880); Error in draw.pairwise.venn(area1 = 3186, area2 = 325, cross.area = 588) : Impossible: cross section area too large. Does anyone have suggestion? Thank you.

Un texte encapsul? et encod? dans un jeu de caract?res inconnu a ?t? nettoy?... Nom : non disponible URL : <https://stat.ethz.ch/pipermail/r-help/attachments/20081112/618073fe/attachment.pl>

Hi all, I'm not quite sure if this is a feature or a bug or if I just fail to understand the documentation: If I use cor() with pairwise.complete.obs and method=pearson, the result is a scalar: ->cor(c(1,2,3),c(3,4,6),use="pairwise.complete.obs",method="pearson") [1] 0.9819805 The documentation says that " '"pairwise.complete.obs"' only

Dear all, I am perplexed when trying to get the same results using pairwise.t.test and t.test. I'm using examples in the ISwR library, >attach(red.cell.folate) I can get the same result for pairwise.t.test and t.test when I set the variances to be non-equal, but not when they are assumed to be equal. Can anyone explain the differences, or what I'm doing wrong? Here's an example