similar to: match.call to obtain the name of a function

The example below creates parallel time-series plots of three different y variables conditioned by a dichotomous factor. In the graphical layout, ? Each y variable inhabits its own row and is plotted on its own distinct scale. ? Each level of the factor has its own column, but within each row the scale is held constant across columns. ? The panels fit tightly (as they do

The as.matrix function behaves in a puzzling manner. The help file says: "`as.matrix' is a generic function. The method for data frames will convert any non-numeric column into a character vector using `format' and so return a character matrix." But this does not appear to be the case in the following example. Instead, as.matrix turns a data.frame into a list, not a

Suppose we have some glm object such as: myglm <- glm( y ~ x, data=DAT) Is there an elegant way--or the "right way" within the R way of thinking--to obtain the names of the response variable, the predictor variables, and the dataset, as character strings? For instance, suppose the "right way" was to use the (currently fictitious) functions theresponse(), thepredictors(),

I would like to use lattice graphics to plot multiple functions (or groups or subpopulations) on the same plot region, using different line types "lty" or colors "col" to distinguish the functions (or groups). In traditional graphics, this seems straightforward: First plot all the data using 'type="n"', and subsequently execute a series of "points"

Is there any documentation on what kind of SPSS file can and cannot be read by read.spss? Alternatively, how can one modify or "clean" an SPSS file to make it readable by read.spss? What properties must a *.sav file before read.spss can read it? The file in this example is 270KB, with 5 rows and 173 columns. I have no trouble reading larger files with read.spss, so it's not

When we call a lattice function such as xyplot, to what extent does the "data" designation cause the function to look inside the "data" for variables? In the examples below, the "subset" argument understands that "Variety" is a variable in the data. But the "scales" argument does not understand that "nitro" is a variable in the data.

Is there a way to specify the color of the main title, the subtitle, or the axis labels? I mean, for instance, something like title(main="cougar", col=2) For me, the above command produces the color black; that is, the "col" argument has no effect. I'm on a Windows 2000 machine with > version _ platform i386-pc-mingw32 arch i386 os mingw32 system

How would I create the following plot using lattice? symbols( combPsummary$pastRate, combPsummary$finRate, circles=sqrt(combPsummary$N) ) The idea is to plot finRate vs pastRate using circles whose areas are proportional to the number of people in each group. The following attempt does not really work: xyplot( finRate ~ pastRate , data=combPsummary , panel=function( x, y,

I want to put the "plus or minus" symbol into a character variable, so that this can be turned into a factor and be displayed in the "strip" of a faceted ggplot2 plot. A very nice solution, thanks to Professor Ripley's post of Nov 16, 2008; 3:13pm, visible at http://r.789695.n4.nabble.com/Symbols-to-use-in-text-td874239.html and subsequently

help.search("groupedData") says that it's part of the lme4 package, but it appears not to be there (details below). Is this because lme4 is new and (perhaps) still under development? > update.packages() trying URL `http://cran.r-project.org/bin/windows/contrib/1.9/PACKAGES' Content type `text/plain; charset=iso-8859-1' length 19113 bytes opened URL downloaded 18Kb >

Alternate title: How can I persuade my students that R is for them? Alternate title: Can R replace SAS, SPSS or Stata for clinicians? I am teaching introductory statistics to twelve physicians and two veterinarians who have enrolled in a Mentored Clinical Research Training Program. My course is the first in a sequence of three. We (the instructors of this sequence) chose to teach R rather than

Hi, I am expieriencing strange behaviour of llvm's optimization passes and I don't understand what I am doing wrong. Basically all I've done is extracting code for optimization of a llvm-function in a llvm-module and put it into a separate function for better readability. The original code looks like follows (and works as expected): ----------------------------- std::string

Dear list, I've been using R for a while, but am new to web services. I'm a relatively novice programmer; advance apologies for incorrect terminology. I'm trying to send queries and get results back from a SOAP server, using the SSOAP package. My code contains sensitive API keys and URLs, and unfortunately I'm unable to share it uncensored. I have not been able to reproduce the

Hi, I am expieriencing strange behaviour of llvm's optimization passes and I don't understand what I am doing wrong. Basically all I've done is extracting code for optimization of a llvm-function in a llvm-module and put it into a separate function for better readability. The original code looks like follows (and works as expected): ----------------------------- std::string

The purpose of this email is to (1) report an example where fisher.test returns p > 1 (2) ask if there is a reliable way to avoid p>1 with fisher.test. If one has designed one's code to return an error when it finds a "nonsensical" probability, of course a value of p>1 can cause havoc. Example: > junk<-data.frame(score=c(rep(0,14), rep(1,29), rep(2, 16))) >

Consider all the text that one sees on the console during an R session. Is there a way, within R, to make all this text--both the "output" and the "messages"--automatically get copied to a single text file, in addition to seeing it on the console? If I remember to save the console to a file at the end of my R session, that does it. But (1) That requires pointing and

How does one implement a likelihood-ratio test, to test whether the variances of the random effects differ between two groups of subjects? Suppose your data consist of repeated measures on subjects belonging to two groups, say boys and girls, and you are fitting a linear mixed-effects model for the response as a function of time. The within-subject errors (residuals) have the same variance in

The print method for lme *prints out* the fitted correlation matrix for the random effects. Is there any way to get these values as an object in R? I have examined the components of the lme object (called "junk" in the example below) and the components of summary(junk) without finding these numbers. (How I did this: I dumped the entire lme object to a text file and then used egrep to

Often it is useful to keep a "codebook" to document the contents of a dataset. (By "dataset" I mean a rectangular structure such as a dataframe.) The codebook has as many rows as the dataset has columns (variables, fields). The columns (fields) of the codebook may include: ? variable name ? type (character, factor, integer, etc) ? variable label

Often I perform the same task on a series of variables in a dataframe, by looping through a character vector that holds the names and using paste(), eval(), and parse() inside the loop. For instance: thesevars<-names(environmental) environmental$ToyOutcome<-rnorm(nrow(environmental)) tableOfResults<-data.frame(var=thesevars) tableOfResults$Beta<- NA