similar to: Plotting an array of histograms

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

Hello again folks, Thanks to all for the advice on getting hold of "grid" and "lattice". I think I'm getting the hang of it. This produces very satisfying arrays of histograms. The typical command I am using is histogram( ~ DATA.df$X | DATA.df$F, \ type=c("count"), layout=c(5,5) ) Now I'd like to ask a question slightly more subtle than

Hi R users, I would like to know why these following curve densities don't appear correctly on the histograms. Thank you for your help library(lattice) library(grid) resp <- rnorm(2000) group <- sample(c("G1", "G2", "G3", "G4"), replace = TRUE, size = 1000) histogram(~ resp | group, col="steelblue", panel = function(x, ...){ std

Hi Folks, I'm wondering if there are people out there who would be interested in what would be involved in developing an interface between R graphics and the 'pic' language. Explanation; 'pic' has been part of the Unix 'troff' typesetting suite since very early days (1970s), and also of the GNU troff: 'groff'. Its function is to act as a preprocessor,

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

Something I don't understand ... (!) With the lattice library loaded, I have a loop for( Z in ... ) { ... xyplot(y~x | t, xlab=..., ylab=... ) } and no plot appears on the R graphics device. However, when I run the commands within {...} separately for each instance of Z, I get the plot displayed each time. So it looks as though "xyplot" is not outputting to the graphics

> >>>>> "PD" == Peter Dalgaard BSA <p.dalgaard at biostat.ku.dk> writes: > > PD> "Venables, Bill (CMIS, Cleveland)" <Bill.Venables at cmis.CSIRO.AU> > PD> writes: > >> The fact that every elementary book on statistics does it this way > >> does not make it correct. To be helpful, a histogram really

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") >

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

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, 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, 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

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, 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, 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

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, I'm completely new to R, I am all at sea with the interface and the confusing help files, so would appreciate some help to do a simple task. Need to present the mean and variance of 100 different samples of poisson distributions (N=1000, with fixed lambda) in a file in two columnns, and then produce histograms. So far I have figured out: > N <- 1000 > x <- rpois(N, 3.1) ,

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, The spreadsheet-like layout displayed when 'data.entry' is invoked is very useful simply for legible display of data, quite apart from its intended use for the purpose of entering or editing data. If one wants to use it _simply_ as a display device, so that one can look around inside a data-set while working on it, then it is not a good idea to have its _editing_ capabilities