similar to: problem wih plotrix:cluster.overplot

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

Moving from S-plus to R I encountered many great features and a much more stable system. Currently, I am left with 2 problems that are handled differently: 1) I did lots of "overplots" in S-Plus using par(new=T,xaxs='d',yaxs='d') to fix the axes ->What is the workaround in R ? 2) In S-Plus I could fix "scientific notation" or "normal notation" in

require(gplots) data(rtPCR) overplot( RQ ~ Conc..ug.ml. | Test.Substance, data=rtPCR, subset=Detector=="ProbeType 7" & Conc..ug.ml. > 0, same.scale=TRUE, log="xy", f=3/4, main="Detector=ProbeType 7", xlab="Concentration (ug/ml)", ylab="Relative Gene Quantification" ) # Error in lowess.default(mf[[-response]], mf[[response]], f = f,

In applying multidimensional scaling, it seems to me that sometimes the underlying dimensionality of the matrix is 1. However I found a case where cmdscale failed when I tried k=1. Here it is: m<-matrix( c(.5,.81,.23,.47,.61, .19,.5,.06,.17,.28, .77,.94,.5,.74,.85, .53,.83,.26,.5,.64, .39,.72,.15,.36,.5), nrow=5) # BTW I think cmdscale uses only the lower triangle--how to enter only # that

Hi We have a few questions regarding the use of the "isoMDS" function. When we run "isoMDS" function using 60,000 x 60,000 data matrix, we get the following error message: ------------------------------------ cmdscale(d, k) : invalid value of 'n' Calls: isoMDS -> cmdscale ------------------------------------ We checked the source code of "cmdscale" and

I'm looking for someone to explain the difference between these procedures. The function prcomp() does principal components anaylsis, and the function cmdscale() does classical multi-dimensional scaling (also called principal coordinates analysis). My confusion stems from the fact that they give very similar results: my.d <- matrix(rnorm(50), ncol=5) rownames(my.d) <-

hi, i have just begun using R, so please bear with me. i am trying to use cmdscale and display the result. i read the data using read.table(), calculate the proximity matrix using dist() and the display the result using the cmdscale(). this is very fine. in addition, i want the display to distinguish between two classes of records in my data. i have my data records marked as "1" or

Sorry to bother you kind folks again with my questions. I am trying to learn as much as I can about all this, and I will admit that I don't have the proper background, but I hope that someone can at least point me in the correct direction. I have created a test matrix for what I want to do: s1 s2 s3 s4 s5 s1 10 5 0 8 7 s2 5 10 0 0 5 s3 0 0 10 0 0 s4 8 0 0 10 0 s5 7

Dear R-users, I haven't found a way in the searchable archive to overplot a contour (lines) over a surface. I have a (n,m) matrix that represents sea surface temperature that I have plotted using image.plot(), filled.contour() or image(). I would like to overplot this image with some contour lines of mixed layer depth values(same size matrix). How can I do this? Any help is appreciated,

Hello, I have a dilemma that I'm hoping the R gurus will be able to help resolve. For background: My data is in the form of a (dis)similarity matrix created from taking the inverse of normalized reaction times. That is, each cell of the matrix represents how long it took to distinguish two stimuli from one another-- a square matrix of 45X45 where the diagonal values are all zero (since this

The following problem occurs in R 1.4.0 and 1.3.1 for Windows95, but not in R 1.2.0 for Windows95. The problem does not occur in R 1.4.0 for Linux PC, Linux Alpha and HP-UX. Sometimes, the type of 'Size' of an object of type 'dist' changes from integer into double. Running cmdscale on such a 'dist' object gives invalid results. I don't know what should be considered

Hello, We are trying to find a function to compute "stress" in our multidimensional scaling analysis of a dissimilarity matrix. We've used "dist()" to create the matrix and "cmdscale()" for the scaling. In order to determine the number of dimensions we would like to plot stress vs. dimensions. However, we cannot find a pre-made command. It seems that other

Hi. I know matrices that use distances between places works fine when using cmdscale. However, what about matricies such as: A B C D E A 0 1 23 12 9 B 1 0 10 12 3 C 23 10 0 23 4 D 12 12 23 0 21 E 9 3 4 21 0 i.e. matrices which do not represent physical distances between places (as they would not make sense for real distances such as the one above)

Full_Name: Laurent Gautier Version: 1.3.0-patched OS: IRIX 6.5 Submission from: (NULL) (130.225.67.199) Hello, The function La.eigen, called by cmdscale in the package mva behaves an unexplicable way (for me). The following lines show what happened. I tried the very same on linux, and it worked fine. >a <- matrix(c(1,2,3,2),3,3) >a [,1] [,2] [,3] [1,] 1 2 3 [2,]

Hi, I am looking for the best multidimensional configuration for my data (47*47 distance matrix). I ve tried classical metric (cmdscale) and non metric MDS (isoMDS, nmds) but it is now difficult to choose the best solution because of the uncertainties in the definitions of the "stress" function. So, same problem, several questions : 1. Statistical consideration : With

If tbl is an object of class 'dist', you can do this: a <- sammon(tbl, k=3) But you can't do this: b <- isoMDS(tbl, k=3) Wouldn't it be sensible to have identical interfaces to sammon() and isoMDS() ? I think all that would be needed is to change this: isoMDS <- function(d, y=cmdscale(d, 2), maxit=50, trace=TRUE) { ...into this: isoMDS <-

Hi WizaRds, I'd like to overplot UK fuel consumption per quarter over the course of five years. Sounds simple enough? Unless I'm missing something, the following seems very involved for what I'm trying to do. Any suggestions on simplifications? The way I did it is awkward mainly because of the first call to plot ... but isn't this necessary, especially to set limits for the

Hello! I am working with signals and a plot of several signals on the same axes can get quite messy. With lines that are very fractured, distinction by only the linestyle is not very clear. If I add symbols to the plot however, there are so many symbols, that they overplot and the whole plot is unreadable once again. I am looking for advice on how to make a plot with continuous lines and symbols

I have a dataframe like this (toy example): x y z "a" "a" 0 "a" "b" 1 "a" "c" 2 "b" "a" .9 "b" "b" 0 "b" "c" 1.3 "c" "a" 2.2 "c" "b" 1.1 "c" "c" 0 The observations are from a matrix like this: c 2.2 1.1 0.0 b 0.9 0.0

If do: > library("e1071") > example(svm) I get: svm> data(iris) svm> attach(iris) svm> ## classification mode svm> # default with factor response: svm> model <- svm(Species ~ ., data = iris) svm> # alternatively the traditional interface: svm> x <- subset(iris, select = -Species) svm> y <- Species svm> model <- svm(x, y) svm>