similar to: Putting regression lines on SPLOM

Dear R-experts, I have found a splom-modification online which is given below. This works perfectly, but I would like to have a matrix of given correlation values to be used in the lower triangular part (lower.panel) of the splom-plot instead of calculated correlation values. Here is the matrix I would like to use (it can be any other convenient data structure):

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") >

I thought one nice addition to a splom figure would be to have the scatterplots in the upper triangle and a color-coordinated correlation matrix on the bottom. So I tried my hand at customizing panel.pairs(), and was rebuffed. Many times. Four hours of fruitless debugging later, I turn to you for help: panel.pairs(z=teststatfull[,6:12], pscales=0, panel.subscripts=FALSE, subscripts=,

Hi, I am trying to produce a 30x30 lattice grid. The 30 variables naturally fall into three groups of ten and I would like to add thicker axis lines around these to highlight this. However, I can only do this separately, so far, and I don't know how to superimpose the grid with the thicker lines (separating the 10x10 groups) onto the overall grid of all 30x30 plots. I enclose a

Dear ExpeRts, I have the scatter plot matrix as given below. I would like the different "sub-plots" in the scatter plot matrix to be colored differently. How do I get all points shown in the upper-left plot (on position (1,1) in the scatter plot matrix) to be plotted in blue, and the points shown in the plot to the right (on position (1,2) in the scatter plot matrix) to be plotted in

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

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 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

Hello, I have a customized pairs () fonction as follows that displays correctely my data. ------------------------------------------------------------------------ panel.cor1 <- function (x, y, digits=2, prefix="") { usr <- par("usr"); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) r <- cor(x, y,use="pairwise.complete.obs",

Dear expeRts, I would like to create a scatter plot matrix with splom(). The lower panel should contain some additional information about the samples shown in the upper panel plot, see the splom() call below. Now two questions came up: (1) The lower panels show "tau" and "alpha" on top of each other. How can I plot *three* expressions on top of each other? I tried several

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

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

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 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 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