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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

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") >

Hi Folks, My recent response to Laura Quinn's query about matrix subsetting reminded of a question. I wrote: iDir <- ((Winds[,20]<45)|(Winds[,20]>315))&(!is.na(Winds[,20])) Now, I find "!is.na" a bit awkward to type, so I might prefer to type it as "nis.na". While it is possible to define nis.na <- function(x){ !is.na(x) } this involves a slight

The formula attribute of the builtin CO2 dataset seems a bit strange: > formula(CO2) Plant ~ Type + Treatment + conc + uptake What is one supposed to do with that? Certainly its not suitable for input to lm and none of the examples in ?CO2 use the above.

Hi, I'm currently doing some analyses on time of failure of a product. I found on the internet some article about the Arrhenius equation, and I can calculate the results with R. Equation: k=A*exp(-Ea/R*T) I can fit a model with lm for this purpose, so far no problem. But for the confidence interval, how can I use the Weibull distribution? Or should I use the weibull distribution at all? Or

The following can't be right, first rw2010: > 1 %% 0.001 [1] 0.001 Then rw2001: > 1 %% 0.001 [1] -2.081668e-17 > and the last seems about right. -- Kjetil Halvorsen. Peace is the most effective weapon of mass construction. -- Mahdi Elmandjra -- No virus found in this outgoing message. Checked by AVG Anti-Virus.

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

Hi Folks, I'm interested in looking (in a beginner's amateurish way) at prosodic/phonetic analysis of recorded speech. In particular I would want to use R to formulate and evaluate specific models. So I would like to ask R people for their recommendations for a program which would a) Take as input a sound file in one of the common formats (".wav", ".au") b)

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, This is off-topic R-wise, but it may be close to the heart of many R-users, so I think it may be the best place to ask! Users of 'gv' (the "front end" to ghostscript) will be aware of the little window which gives you the x-y coordinates (in points = 1/72 inch) of the position of the "cross-hair" mouse cursor. These coordinates are those of the corresponding

Hi My simulation with the followin R code works perfectly: sim <- replicate(999, sum(exp(rgamma(rpois(1,2000), scale = 0.5, shape = 12)))) But now I do not want to have values in object "sim" exceeding 5'000'000, that means that I am just using the beginning of densitiy function gamma x < 15.4. Is there a possibility to modify my code in an easy way? Thanks for any help!

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

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

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

Doese anyone know a R function to find the median of a gamma distribution? [[alternative HTML version deleted]]

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, 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

Hi Folks, While I was browsing in the R-help archives yesterday, I got curious about the time series of the sizes of the monthly archives in MB. This turned out to have an unexpected feature or two, which I leave to readers to explore for themselves. I'm now wondering at what point in time we might expect to be receiving 1000MB/month (30+MB/day). It's not that far away, it seems, but

I am looking for a way to produce a "distribution graph" as in the example: (http://cecsweb.dartmouth.edu/release1.1/datatools/dgraph.php?year=2003&geotype=STD_HRR&event=A01_DIS&eventtype=UTIL Anybody who can help? Christian von Plessen Department of Pulmonary Medicine Haukeland university hospital Bergen Norway