similar to: Little graph questions!

Dear Friends in R: On RedHat linux, R-1.1, I've gotten far enough to create the graph that shows on the screen with 3 lines, but I have some trouble. Here is the way I created the three "overlaid" graphs: data<-read.table("DataCulture0",header=T,as.is = TRUE) attach(data) tmp1<-plot(acquaint~T,type='l', ylim=c(0,1),ylab="average

Using RedHat Linux 7.0, R-1.2.2, R-hdf5-1.2 library, I want to load a dataset, do some stuff with it, then erase its objects, get an other, repeat. My friend wrote a function which tried to clear away all the objects. At the end, it uses rm() to remove objects. This is the same way we do it interactively, from the R prompt: testLoadSeveralHDF <- function(numFiles) { for (i in

I have made a plot with a legend and on the screen it looks fine, but when I save as jpg or pdf, or print, the legend box is too small, it cuts through the words on the right side. I put an example here: http://lark.cc.ukans.edu/~pauljohn/R/apdftest.pdf Is there a work around? -- Paul E. Johnson email: pauljohn at ukans.edu Dept. of Political Science

I'm looking for an analogy to SPSS and "variable lable" and "value lable" in R. I have a copy of S+4.0 and can't find any info in their docs, and don't find it in R-intro either. In SPSS, for each variable, there is a name like VAR001 and a variable lable (a longer descriptive string) like Respondent ID and for many of the values of other variables there are

I just heard a talk about a semi-parametric model. I was quite excited by the idea. This model is fitted y= xB + g(z) + e where x is a data matrix, B a column vector, z is another data matrix, and g is a smooth model fitted by a Kernel Smoothing regression (I got the idea any smoother would do as well). The speaker said that when z is considered as a "control" variable, and there is

Running R 1.1 on RedHat Linux 6.2. I need to write a shell script that goes through a bunch of directories of simulation output, creating summary files that have the mean and standard deviation of the variables found in the data files in each directory. I've got the R code doing almost the right thing. It reads in data, then gets the mean and standard deviation for the numeric variables,

It seems like every time I try to do something a little different, I cannot get output saved just right. This is on RedHat 7.2 with R 1.4.1. The png output looks fine, but the eps output has the problem that the bounding box on the legend cuts the legend in half. I put a copy of a bad one here: http://lark.cc.ukans.edu/~pauljohn/ResearchPapers/meanProtest-box.eps When I asked about these

Dear R help reader, I'm not an expert in R and are lerning a lot by reading the help digest, which is sometimes difficult because the huge amount of data posted. I have posted some questions before, and are impressed how quick I got a solution for my problem. Sometimes with quite different suggestions. I was always wondering if my questions didn't come up before. On the other site,

Simple usage questions that I ought to be able to figure on my own, but can't. 1.I'm able to produce a cross tabulation table showing counts with either table or xtabs. But I want column percentages for interpretation, and it seems stupid to sit there with a calculator figuring marginals and column percentages. How to make R do it after this: > x <- c(1,3,1,3,1,3,1,3,4,4)

Hello, Can anyone offer any insight on graphing classification dendrograms with the nodes marked? plot(tree()) produces a nice tree structure but there's no acccompanying text. Thanks, D S. David White sdavidwhite at bigfoot.com Columbus, Ohio -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

On Linux (RH7.1) with R installed from CRAN rpms: I have many files with miscellaneous names *.dat. I would like to successively open each one, run some functions on them, and then write some output numbers to *.summary. I have succeeded in doing this when I type in each file name individually for the data input, but have not found a way to search files in a directory and grab the *.dat. If

I've seen XGobi come up in here from time to time, and I wonder what is it, what is it for, do I need it? How about throwing me a bone here! -- Paul E. Johnson email: pauljohn at ukans.edu Dept. of Political Science http://lark.cc.ukans.edu/~pauljohn University of Kansas Office: (785) 864-9086 Lawrence, Kansas 66045 FAX: (785)

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I have a student that I'm encouraging to use R rather than SAS or Stata and within just 2 weeks he has come up with a question that stumps me. What does a person do about endogeneity in generalized linear models? Suppose Y1 and Y2 are 5 category ordinal dependent variables. I see that MASS has polr for estimation of models like that, as long as they are independent. But what if the

I got great, quick advice here earlier today. One little hitch was that the pattern in list.files wants a regular expression, and *.dat was grabbing all files with .dat in them, not ones ending in .dat. This code creates a *.dat.summary file with the mean and std. deviation. of the numeric variables for every *.dat file in the current directory. myDat<-list.files(pattern="*.dat$")

Hey, I want to draw several plots sequently, but have to make them dispaly in one figure. So how to achieve this? Thanks. Fred

how can I import/export data from ms excel? also is there a way to import/export data of the hdf format? is there a way to import/export hdf format for ms excel as well? thank you very much in advance. /js ###################################################################### This e-mail message has been scanned and cleared by MailMarshal http://www.marshalsoftware.com

Dear r-help, I want to conduct nonlinear fitting to a data frame having x and y variables. Because both x and y have measurement error, I want to include error term of x variable in the model. I'm not sure but I think ordinary nls model only consider error term of y variable. How can I do this kind of nonlinear fitting in R. Is there any examples in nls package? Thanks in advance, --

To all I have some simple questions pertaining to weights used in regression. If the variability of the dependent variable (y) is a function of the magnitude of predictor variable (x), can the use of weights give an appropriate answer to the regression parameters and the std errors? Assume that y at x=1 and 6 has a standard deviation of 0.1 and at x=11 it is 0.4 Then according to a web page on

When I saw "SciGraphica": http://magnet.fsu.edu/~feiguin/scigraphica/screenshots.html I wished it could be a GUI frontend for R on Unix. -- Paul E. Johnson email: pauljohn at ukans.edu Dept. of Political Science http://lark.cc.ukans.edu/~pauljohn University of Kansas Office: (785) 864-9086 Lawrence, Kansas 66045 FAX: