similar to: R-alpha: plot, axis & log

Here are another three problems with logarithmic scales: 1) segments() does not work with logarithmic scales. I suggest to change lines 962-973 in "plot.c": for (i = 0; i < n; i++) { if (FINITE(xt(x0[i%nx0])) && FINITE(yt(y0[i%ny0])) && FINITE(xt(x1[i%nx1])) && FINITE(yt(y1[i%ny1]))) { GP->col = INTEGER(col)[i % ncol];

There is a difference between R and S (or S-Plus, I'm not sure about that) concerning the generation of random numbers. If an error occurs during the execution of a function, the seed is restored to its old value in S-Plus, but not in R. This feature of S-Plus has just turned out to be very useful for me because I got an error message during a small simulation for a certain choice of

This problem has been mentioned by Arne Kovac <maak@stats.bris.ac.uk> on May 9-10. The patch is really a replacement of 'axis' in RHOME/src/library/base/funs/ It contains the fixes which where proposed Arne Kovac and fixes (all?) the errors he/she (?) reported on May 9. "axis" <- function (which, at, labels = TRUE, ...) { if (which%%2 == 1) { axp <-

hello. I have a query about the Matrix package for R. I wrote some code a while ago using the Matrix package version 1.6.2 with an early version of R, to do some linear least squares for regression: xn [,1] [,2] [,3] [,4] [1,] 1 0.7429352 0.5519528 0.4100652 [2,] 1 0.7443713 0.5540886 0.4124477 [3,] 1 0.7447385 0.5546355 0.4130584 [4,] 1 0.7459597 0.5564558

Hi, If any one has time I need some help understanding the P-values given in the lmer output. Using AIC for model selection I find my minimal model is FOLLOW~MOVERSTATUS+DISTANCE however it appears DISTANCE is not significant at 95% confidence, see output quoted below. However, removing DISTANCE gives a higher AIC=433.5, therefore I will keep it in, but am confused as to what is adds to the

Dear All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My

>>>>> "Marc" == Marc Schwartz <mschwartz@medanalytics.com> >>>>> on Wed, 13 Nov 2002 11:01:49 -0600 writes: Marc> Marc Schwartz wrote: >> SNIP >> >> I am guessing that this may be the result of par("ylog") >> and par("xlog") being read only and perhaps impacting the >>

Hi all, I am very new to R and this might be a simple question but I have looked everywhere you suggest before writing to you. I am trying to change font type from san-serif to a serif (Times New Romans) on all labels and axis of my boxplot. I have used this function in other plots before, e.g.: plot(residuals~lnlifespan, data=mydata, pch=psymb, font=6, xlab="ln reproductive

There probably is a way, but it involves some programming. You would need to clone a smooth constructor (e.g. for the "cr" class), and then modify it to add a linear constraint matrix C to the returned smooth object. If b are the smooth coefficients then C should? be the matrix such that s(0) = Cb (you can get this from the Predict.matrix method for the class). Then the constraint

Hi all, I've scoured the web, faq and mailing list archives to try and find an answer to this problem I'm having, but to no avail.... I'm trying to rsync a Cyrus IMAP message store "structure" from one system to another. (I say "structure" because I don't want to copy over the actual messages in the message store.) For those of you unfamiliar with the

Thanks Simon, by cloning a smooth construct do you mean copying and modifying the smooth constructor code? Could you pleas elaborate on your answer? Which is the Predict.matrix method? 2018-01-12 3:20 GMT-06:00 Simon Wood <simon.wood at bath.edu>: > There probably is a way, but it involves some programming. You would need to > clone a smooth constructor (e.g. for the "cr"

Hi again, another problem: This (below) isn't working and as far as I can see it damn well should. What I'm trying to acomplish is to run several data sets through the same graphing procedure, but for the pH data use a log y axis. using this code all graphs are drawn with a linear axis. Surely I should be able to set the ylog option to T using another object (logaxis). for (n in

Hi, I get this when I try to configure openssh-4.3p1 under Solaris (8 and 9).. configure: WARNING: lastlog.h: present but cannot be compiled configure: WARNING: lastlog.h: check for missing prerequisite headers? configure: WARNING: lastlog.h: see the Autoconf documentation configure: WARNING: lastlog.h: section "Present But Cannot Be Compiled" configure: WARNING: lastlog.h:

Full_Name: Version: 1.0.0 OS: linux Submission from: (NULL) (193.62.250.209) ?par lists and describes xlog and ylog, though these are obsolete. It does not list log. I found out about log from ?plot.defaults--so that help is OK. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

Gareth Did you ever get a solution to this? mary Mary Owen IT Manager Stride Treglown Limited Promenade House, The Promenade, Clifton Down, Bristol BS8 3NE T 0117 9157361 F 0117 974 5207 E maryowen@stridetreglown.com www.stridetreglown.co.uk

Hi all, There are quite a few of us working on LLVM in the Bristol area. I invite you all to the first Bristol LLVM social: Where: The Hope & Anchor 38 Jacobs Wells Road Bristol BS8 1DR http://www.hopeandanchor.net When: Thursday 20 September from 7pm Look forward to meeting you all there. Jeremy -- Tel:

Dear list, I have been using multivariate multiple regression (MMR) in the form lm(Y~X) where Y and X are matrices of response and predictor variables. I know that summary(mlm.object) would give the usual lm statistics for each response variable separately and that anova.mlm(mlm.object) will give the analysis of variance table of the mlm object. However, anova.mlm (also manova(mlm.object))

Brian, Thanks for doing this. As the primary author, could I have a desk copy, please? Bill Venables. Bill Venables, CMIS, CSIRO Marine Laboratories, PO Box 120, Cleveland, Qld. 4163 AUSTRALIA Phone: +61 7 3826 7251 Fax: +61 7 3826 7304 Mobile: +61 419 634 642 <mailto: Bill.Venables at csiro.au> http://www.cmis.csiro.au/bill.venables/ -----Original Message----- From: Brian

Hola! It is somewhat inconvenient to use plot.function, when add=TRUE. The following (miniscule) change makes it behave better: plot.function <- function (fn, from , to, xlim = NULL, ...) { if (!is.null(xlim)) { if (missing(from)) from <- xlim[1] if (missing(to)) to <- xlim[2] } curve(fn, from, to, xlim = xlim, ...) } The only

Hi, I'm running a GLMM on binomial choice data. The outputs I receive are sensible except for the degrees of freedom, which come out much larger than expected. Can anyone advise please? Exptl design: Response = Choice Fixed Factors = Position, Treatment and Sex Random Factor = ID nested within Treatment and Sex Covariate = Delay The model: glmmPQL(FreeChoice ~ Position * Treatment + Sex