similar to: Cook-distance-type plot (vertical bars)

Run this: >p <- c('a', 'c', '', ''); a <- c(10, 20, 30, 40); d1 <- >data.frame(Promoter=p, ip=a) # Note duplicate empty names in p. >p <- c('b', 'c', 'd', ''); a <- c(15, 20, 30, 40); d2 <- >data.frame(Promoter=p, ip=a) >all <- merge(x=d1, y=d2, by="Promoter", all=T) >all <-

Hi, Having dipped my toe into R a few times over the last year or two, in the last few weeks I've been using it more and more; I'm now a thorough convert. I've just joined the list, because although it's great, I do have this problem... I'm using linear discriminant analysis for binary classification, and am happy with the classification performance using predict(). What

Hi,. I've installed RSPerl on my Linux box. It runs all of the test scripts that I've been able to find. I've learned that I can pass in vectors of data as array-refs, and can use named parameters with R::callWithNames and a hash of names and values. The one problem that I'm left with is how to call a function from an add-on package ('ctest' in this case). I know that

I am new to R. Trying to find out how lda() {in MASS R1.8.0 Windows} was implemented in R. Does anyone know where to find out lda source code ? Thanks. Wei

Hi, Following on from suggestions made last week, I decided to install R 2.1.0 on my Linux machine. I'm running into a problem there however, as shown: make[1]: Entering directory `/d0/home/fgibbons/tmp/R2.1.0/R-2.1.0/src/library/Recommended' make[2]: Entering directory `/d0/home/fgibbons/tmp/R2.1.0/R-2.1.0/src/library/Recommended' begin installing recommended package VR WARNING:

How can I extract the linear discriminant functions resulting from a LDA analysis? The coefficients are listed as a result from the analysis but I have not found a way to extract these programmatically. No refrences in the archives were found. Thank you very much, Stefan

Hi, I think it would be nice to have par(ask=T) set by default in termplot(), like it is in plot.lm(). Best, Emmanuel Paradis -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !)

At 11:57 25/10/00 +0100, Brian Ripley wrote: >> Date: mer., 25 oct. 2000 12:38:55 +0200 >> From: Emmanuel Paradis <paradis@isem.univ-montp2.fr> > >> I think it would be nice to have par(ask=T) set by default in termplot(), >> like it is in plot.lm(). > >Well, it isn't really the default in plot.lm, the default for `ask' being > >interactive()

The following code demonstrates an annoyance with plot.lm(): library(DAAGxtras) x11(width=3.75, height=4) nihills.lm <- lm(log(time) ~ log(dist) + log(climb), data = nihills) plot(nihills.lm, which=5) OR try the following xy <- data.frame(x=c(3,1:5), y=c(-2, 1:5)) plot(lm(y ~ x, data=xy), which=5) The "Cook's distance" text overplots the label for the point with the

Hi Community, I'm trying to reconcile Cook's Distances computed in glm. The following snippet of code shows that the Cook's Distances contours on the plot of Residuals v Leverage do not seem to be the same as the values produced by cooks.distance() or in the Cook's Distance against observation number plot. counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9)

Dear useRs, I have some trouble with the calculation of Cook's distance in R. The formula for Cook's distance can be found for example here: http://en.wikipedia.org/wiki/Cook%27s_distance I tried to apply it in R: > y <- (1:400)^2 > x <- 1:100 > lm(y~x) -> linmod # just for the sake of a simple example >

I have a puzzle as to how R is computing Cook's distance in weighted linear regression. In this case cook's distance should be given not as in OLS case by h_ii*r_i^2/(1-hii)^2 divided by k*s^2 (1) (where r is plain unadjusted residual, k is number of parameters in model, etc. ) but rather by w_ii*h_ii*r_i^2/(1-hii)^2 divided by k*s^2,

Hi, This is more a statistics question rather than R question. But I thought people on this list may have some pointers. MY question is like the following: I would like to have a robust regression line. The data I have are mostly clustered around a small range. So the regression line tend to be influenced strongly by outlier points (with large cook's distance). From the application 's

I have help files in several formats. Up until now I have converted nroff files to S-Plus 6 sgml and html files using Splus doc_to_S ... (creates .sgml file) Splus HINSTALL ... (creates .sgm and .html files) I maintain master help files in R .Rd files, and for some of my functions converting from .Rd to .sgm using R CMD Rdconv --type=Ssgm ... produces better .sgm files than beginning with

Hi All, I'm trying to install R from source on an Intel machine running Linux (RedHat 6.2) and I am getting errors (Error 139, Error 2, and Error 1) on 'make check'. Perhaps I've forgotten something basic? --------------------------------------------------------------------- Here's what I've done: /configure --prefix=/home1/froth/R --enable-R-shlib --with-tcltk

As some of you R-devel readers may know, the plot() method for "lm" objects is based in large parts on contributions by John Maindonald, subsequently "massaged" by me and other R-core members. In the statistics litterature on applied regression, people have had diverse oppinions on what (and how many!) plots should be used for goodness-of-fit / residual diagnostics, and to my

Hi all I was playing with termplot(), and came across what appears to be an inconsistency. It would appreciate if someone could enlighten me: > # First, generate some data: > y <- rnorm(100) > x <- runif(length(y),1,2) > # Now find the log of x: > logx <- log(x) > > # Now fit two models that are exactly the same, but specified differently: > m1 <-

Thanks for your prompt reply Duncan. I had indeed assumed they were what the help file says until observation raised doubts, which is why I queried it. >From reading the code for termplot(), it seems that either the predict() function doesn't return the 1x standard error, or the curves plotted by the termplot() function are not 1x standard errors. If they're not 1x standard errors,

The bug exists on R-1.9.0-alpha compiled the 10/3. Termplot has a problem if either the model only contains a single term or if asked to plot a single term. In addition there are problems with the option se = TRUE. Analysis: termplot starts with terms <- if (is.null(terms)) predict(model, type = "terms", se = se) else predict(model, type = "terms", se = se,

Hi together, I always get an error message with using ylim in termplot(), like this: > x<-(1:10) > y<-(10:1) > l<-lm(y~x) > termplot(l,ylim=c(1,2)) Is this a bug, or is there another possibility to do that? Especially, I would like to use term.plot() for gamlss objects. Thanks for your help! Andreas -- Echte DSL-Flatrate dauerhaft f?r 0,- Euro*!