similar to: ? data.entry "read-only" ?

I want to use R to run a reaction-time experiment: Something appears on the screen, I respond by typing something (one keystroke), the system measures the speed of my response. R would be great for this if only I didn't have to hit Enter to enter that keystroke. I am doing such experiments now but they require two actions per trial: hit keystroke, hit Enter. Is there some way that R can be

Hi Folks, What is the best way to avoid a function being read in anew (and masking an exiting function) when a definition of it has already been established in R? Reason: Fernando Tusell and I are working up Schafer's 'CAT' for R (basically done now, just needs some cosmetic tidying up). This uses a function 'slice.index', present in S but not in the versions of R we were

I have two CSV files (exported from Excel), say file1 and file2. The have the same number of rows, and each has several columns, with names on the first line; and some of the columns in file1 are repeated in file2. Using the "foreign" package, I can read these in separately to dataframes say d1 and d2 with > d1<-read.csv("file1") >

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

Greetings Folks, When R code (as entered or read from a courced file) is executed, is it interpreted from the input form every time having once been read in, or do subsequent invocations use an "intermediate" (pre-interpreted) form? Or, putting it another way, is the execution of R code faster second time time round (and later) because the pre-interpretation has already been done once

Hi Folks, I'm seeking advice about how to resume an outer loop following failure of a function within the loop (which issues an error message). Essentially, I'm repeating (simulating) a process which involves random sampling, EM and MCMC. I'm walking on very edge of rather thin ice -- data rather thinly spread over many parameters! Occasionally some component of the loop fails, with

Sorry Folks, I'm sure I could suss out the answer myself but I need it soon ... ! 1. Given a set of 4 variables X,Y,Z,W in a dataframe DF, I make a scatter-plot matrix using splom(DF). 2. I do all regressions of U on V using lm(U~V), where U and V are all 12 different ordered pairs from X,Y,Z,W. 3. Now I would like to superpose the regression lines from (2) onto the corresponding

Hi folks, I have a vaiable Y and an associated factor Z at several (13) levels. boxplot(Y~Z) produces a nice array of boxplots, one for each level of Z, and each duly labaelled with its level of Z. I would like to superpose on each boxplot the actual data points which it represents, i.e. do something conceptually (though not in real R) expressed as points(Y~Z) or points(Z,Y) It can

Hi Folks, I note that, while the "chisq" functions dchisq(x, df, ncp=0, log = FALSE) pchisq(q, df, ncp=0, lower.tail = TRUE, log.p = FALSE) qchisq(p, df, ncp=0, lower.tail = TRUE, log.p = FALSE) rchisq(n, df, ncp=0) all have a slot for the non-centrality parameter "ncp", of the functions for the t and F distributions: dt(x, df, log = FALSE)

Hi folks, Suppose I have a series of cases each with categorical factors A, B. What is the best way to "glue" A and B together into a single factor? For example, given A0 B1 ... A1 B1 ... A0 B2 ... A1 B0 ... A0 B0 ... A1 B2 ... then I'd like to end up with a single factor with levels A0B0, A0B1, A0B2, A1B0, A1B1, A1B2 according to all the combinations which actually occur in

Hi Folks, Sorry to raise what has probably been discussed before, but I an repeatedly struck by the comparative slowness of S-plus for Windows compared with R for Linux when doing much the same thing. I don't have a direct comparison, because they're not running on the same machine; but machine W has a faster CPU and more RAM than machine L, yet S-plus on W seems to take longer by quite

Dear list, Sometimes I have huge data.frames and the small spreadsheetlike edit.data.frame is quite handy to get an overview of the data. However, when I close the editor all data are rolled over the console window, which takes time and clutters the window. Is there a way to avoid this? Fredrik

Hi Folks, No doubt a question with a well-known answer, but I'm unfortunately not managing to find it readily ... ! I have a quantitative variable Y and a 4-level ordered factor A (with very unequal numbers at the different levels, by the way). The command lm(Y ~ A) returns (amongst other stuff) an intercept, and coefficients A.L, A.Q and A.C for the Linear, Quadratic and Cubic effects.

Hi Folks, I'm currently looking at missing data/imputation methods (including multiple imputation). S-Plus has a "missing data library". What similar resources are available within R? Or does one roll one's own? Best wishes to all, Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <Ted.Harding at nessie.mcc.ac.uk>

Hi folks, I use R in X windows on Linux. Normally, I use 'less' as pager, which is fine for scanning through 'help' (or '?') output in the R window itself; the help session is terminated by typing "q", as usual for 'less', and the R window then reverts to the R command line interface. Often, I would like to have the output from 'help' pop up in

Hi folks, I use R in X windows on Linux. Normally, I use 'less' as pager, which is fine for scanning through 'help' (or '?') output in the R window itself; the help session is terminated by typing "q", as usual for 'less', and the R window then reverts to the R command line interface. Often, I would like to have the output from 'help' pop up in

Rather than spend a tedious interactive session picking several (in fact most) of the library packages off the CRAN site one by one, it would be handy to be able to grab the lot in one go (they seem to add up to about 24MB, so it's not a huge download). Is there a way to do this? Thanks for the help. Ted. -------------------------------------------------------------------- E-Mail: (Ted

Hi Folks, I'm trying to understand the model specification formalities for 'lme', and the documentation is leaving me a bit confused. Specifically, using the example dataset 'Orthodont' in the 'nlme' package, first I use the invocation given in the example shown by "?lme": > fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age Despite the

Hi again folks, I seem to have got the trellis (lattice + grid) business working basically OK, in that I can get my 5x5 array of histograms laid out as they should be. However, there is a behaviour I can't see my way round. Even when displayed in the R Graphics Window, there is some clipping (the extreme top, bottom, left and right edges are not there, so that labels, and parts of numerical

Hi Folks, I am dealing with data which have been presented as at each x_i, mean m_i of the y-values at x_i, sd s_i of the y-values at x_i number n_i of the y-values at x_i and I want to linearly regress y on x. There does not seem to be an option to 'lm' which can deal with such data directly, though the regression problem could be algebraically