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I handed out some results from glm() and the students ask "how many observations were dropped due to missing values"? How would I know? In other stat programs, the results will typically include N and the number dropped because of missings. Without going back to R and fiddling about to find the total number of rows in the dataframe, there is no way to tell. Somewhat

Last Friday, I noticed that it is difficult to work with regression models in which there are factors. It is easier to do the old fashioned thing of coding up "dummy" variables with 0-1 values. The predict function's newdata argument is not suited to insertion of hypothetical values for the factor, whereas it has no trouble with numeric variables. For example, if one uses a

I have a longitudinal data analysis project. There are 10 observations on each of 15 units, and I'm estimating this with randomly varying intercepts along with an AR1 correction for the error terms within units. There is no correlation across units. Blundering around in R for a long time, I found that for linear/gaussian models, I can use either the MASS method glmmPQL (thanks to

Dear R users: I assigned students to make some graphs and I'm having trouble answering some questions that they have. We are all working on R 2.1 on Fedora Core Linux 4 systems. 1. In the plot, the axis is not labeled by "numbers", but rather scientific notation like "-2e+08" or such. We realize that means -200,000,000. We want to beautify the plot. We would rather

Greetings, R-help! On 2 Fedora Core 2 Linux systems, i've completely erased the previous R and all packages and then installed R-2.0 and installed fresh packages. In using Rcmdr, I see some trouble and I wonder if other people see this and if it is due to the tcl/tk, or R, or Rcmdr. (If readers have not yet tried Rcmdr, I recommend it not just because of the GUI it provides, but also

Greetings: I'm running R-1.9.1 on Fedora Core 2 Linux. I tested a proportional odds logistic regression with MASS's polr and Design's lrm. Parameter estimates between the 2 are consistent, but the standard errors are quite different, and the conclusions from the t and Wald tests are dramatically different. I cranked the "abstol" argument up quite a bit in the polr

Greetings Everybody: I generated a 1.2MB dta file based on the general social survey with Stata8 for linux. The file can be re-opened with Stata, but when I bring it into R, it says all the values are missing for most of the variables. This dataset is called "morgen.dta" and I dropped a copy online in case you are interested http://www.ku.edu/~pauljohn/R/morgen.dta looks like this

OK, my journey to make lab machines automagically install & update all desirable R packages is nearing an end! The only question I have now is this: How can I control where the system prints the *.Rout file that is created automatically when the R batch program runs. In "man R" I don't find any information about it. When the cron job runs "R_installAll.sh" (see

I have a linux system with Fedora Core 2 and R-2.0. I was comparing plots made with plot() and dotplot() and discovered a problem. Although the dots are positioned correctly, the numerical labels in the dotplot y axis are not correct. I put copies here: http://lark.cc.ku.edu/~pauljohn/R/plotTrouble1.jpg That is the "correct" one from plot, with the higest value on y showing at 18.

I'm writing R code to calculate Hierarchical Social Entropy, a diversity index that Tucker Balch proposed. One article on this was published in Autonomous Robots in 2000. You can find that and others through his web page at Georgia Tech. http://www.cc.gatech.edu/~tucker/index2.html While I work on this, I realize (again) that I'm a C programmer masquerading in R, and its really

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

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

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,

I'm working up a set of small working examples in R to show what various distributions are and the beauty of the central limit theorem. Those example programs are in this directory: http://lark.cc.ukans.edu/~pauljohn/R/ExampleCode/ You can feel free to use those if you want, or you can send me other small working example code for R. The key here is small, self contained things that

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

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 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

Dear colleagues: I've been storing up this question for a long time and apologize for the length and verbosity of it. I am having trouble in consulting with graduate students on their research projects. They are using surveys to investigate the sources of voter behavior or attitudes. They have predictors that are factors, some ordered, but I am never confident in telling them what

I've been reading the R mail archives and I've found a lot of messages with this same kind of problem, but I can't understand the answers. Can one of you try to explain this to me? Here's my example. Given a regression model and a variable, I want to use unstack() on the vector of residuals and make some magic with the result. But unstack hates me. PCSE <- function

Concerning this article, Christopher Zorn, "Generalized Estimating Equation Models for Correlated Data: A Review with Applications." 2001. American Journal of Political Science 45(April):470-90. The author very kindly provides data for replication on his web page: http://www.emory.edu/POLS/zorn/Data/GEE.zip. I've been comparing the Professor Zorn's results obtained with