similar to: candisc

Hello, I have a file with two dependent variables (three and five) and one independent variable. I do i.mod <- lm(cbind(three, five) ~ species, data=i.txt) and get the following output: Coefficients: three five (Intercept) 9.949 9.586 species -1.166 -1.156 I do a" i.can<-candisc(i.mod,data=i): and get the following output: Canonical Discriminant Analysis

Thanks David, Yes, I am talking about the MASS package.Thank you for pointing out that these scale the same. My question is, how do I get from the V1 data: V1 1 164.4283 2 166.2492 3 170.5232 4 156.5622 5 127.7540 6 136.7704 7 136.3436 to the other set of data: + 1 -2.3769280 + 2 -2.7049437 + 3 -3.4748309 + 4 -0.9599825 + 5 4.2293774 + 6 2.6052193 + 7 2.6820884 On Mon, Sep 28, 2009

I am having a problem understanding the lda package. I have a dataset here: [,1] [,2] [,3] [1,] 2.95 6.63 0 [2,] 2.53 7.79 0 [3,] 3.57 5.65 0 [4,] 3.16 5.47 0 [5,] 2.58 4.46 1 [6,] 2.16 6.22 1 [7,] 3.27 3.52 1 If I do the following; "names(d)<-c("y","x1","x2") d$x1 = d$x1 * 100 d$x2 = d$x2 * 100 g<-lda( y ~ x1 + x2, data=d) v2

Hello! I have 2 maps - both created in ssplot and both identical in terms of outline. Is there any way to superimpose Map1 (which has black borders between Canadian provinces) onto Map2 (which is also a map of Canada)? Thanks a lot for your hints! Dimitri ### A. Reading in Canada data at the province and then at the county level: library(raster) getData('ISO3') # Canada's code is

I'm working on some functions for generalized canonical discriminant analysis in conjunction with the heplots package. I've written a candisc.mlm function that takes an mlm object and computes a candisc object containing canonical scores, coeficients, etc. But I'm stumped on how to construct a mlm for the canonical scores, in a function using the *same* right-hand-side of the model

Hello, I am having trouble loading the package candisc onto my R distribution. I am using 2.7.1-2. I do a "> install.packages("candisc" and get the following output. Warning in install.packages("candisc") : argument 'lib' is missing: using '/usr/local/lib/R/site-library' --- Please select a CRAN mirror for use in this session --- Loading Tcl/Tk

Hi, I am doing canonical discriminant analysis using candisc function from the candisc package. My input is a table of species distribution (columns = abundance of each species in each sample) in samples that are split by categories (rows), and I want to know whether each category is associated with a particular set of species and their abundances. I have 20 rows (samples) split into 6

Hi listers, I am working on an canonical discriminant analysis, but I am having some trouble to use the CANDISC function... I just installed the last R version and installed the package CANDISC... But, I am getting the following message because about a permission: The downloaded packages are in C:\Users\Marcio\AppData\Local\Temp\Rtmpz2kFUm\downloaded_packages updating HTML package descriptions

I've just released the latest R-Forge versions of heplots 0.9-3 and candisc 0.5-18 to CRAN. They should appear there within a day or two. == heplots The heplots package provides functions for visualizing hypothesis tests in multivariate linear models (MANOVA, multivariate multiple regression, MANCOVA, etc.). They represent sums-of-squares-and-products matrices for linear hypotheses and for

I'm happy to announce the candisc package, v 0.5-9, now on CRAN. Generalized Canonical Discriminant Analysis Description This package includes functions for computing and visualizing generalized canonical discriminant analyses for a multivariate linear model. They are designed to provide low-rank visualizations of terms in a mlm via the plot method and the heplots package. The methods

I'm happy to announce the candisc package, v 0.5-9, now on CRAN. Generalized Canonical Discriminant Analysis Description This package includes functions for computing and visualizing generalized canonical discriminant analyses for a multivariate linear model. They are designed to provide low-rank visualizations of terms in a mlm via the plot method and the heplots package. The methods

I'm trying to write a formula method for canonical correlation analysis, that could be called similarly to lm() for a multivariate response: cancor(cbind(y1,y2,y3) ~ x1+x2+x3+x4, data=, ...) or perhaps more naturally, cancor(cbind(y1,y2,y3) ~ cbind(x1,x2,x3,x4), data=, ...) I've adapted the code from lm() to my case, but in this situation, it doesn't make sense to include an

[Using R 2.7.2 on Windows XP] After re-building our heplots package, I've begun to get the following error from sessionInfo(), even though it passes R CMD check and builds without errors: > sessionInfo() Error in x$Priority : $ operator is invalid for atomic vectors In addition: Warning message: In FUN(c("MASS", "heplots", "car", "rgl",

Dear R developers A question about the class 'listof', defined in package 'stats'. Other than its definition and use in the code for 'anova', we can't see that the class 'listof' is used for anything else (in recommended packages, or elsewhere). In the spatstat package we have been using a 'listof' to represent a list of spatial objects of the same

Dear all, I'm testing the effect of species and sex in my sample by using the principal component scores of a PCA analysis. I have 30 PCs and I tried to see if there is any significant difference from males to females, given that there is a significant effect of phylogeny (factor with several species). I didi it like this: Y<-PCA$pc.scores[,1:30] fit <- manova(Y ~ sp*sex)

Hi Sergio Doing those tests 30 times is going to give you a huge Type I error rate, even if there was a function that did that. There is a reason why TukeyHSD doesn't make it easy. In general, if there are useful comparisons among the species, you are better off setting up and testing contrasts than doing all-pairwise Tukey tests. Also, the PCA scores are ordered in terms of variance

Dear R list, I'm trying to calculate Mahalanobis distances for 'Species' of 'iris' data as obtained below: Squared Distance to Species From Species: Setosa Versicolor Virginica Setosa 0 89.86419 179.38471 Versicolor 89.86419 0 17.20107 Virginica 179.38471 17.20107 0 This distances above were obtained with proc

I am trying to create a frequency distribution and I am a bit confused. Here are the commands I have entered: > data <- read.csv(file="40609_sortedfinal.csv",head=TRUE,sep=",") > NumberOfActionsByStatus = data$STATUS > NumberOfActionsByUser = data$ETS_LOGIN > NumberOfBidOffer = data$BID_OFFER > NumberOfActionsByUser.freq = table(NumberOfActionsByUser) >

Hello everybody After doing a MANOVA on a bunch of data, I want to be able to make some comment on the amount of variation in the data that is explained by the factor of interest. I want to say this in the following way: XX% of the data is explained by A. I can acheive something like what I want by doing the following: X <- structure(c(9, 6, 9, 3, 2, 7), .Dim = as.integer(c(3,

Dear R list, I'm trying to calculate Mahalanobis distances for 'Species' of 'iris' data as obtained below: Squared Distance to Species From Species: Setosa Versicolor Virginica Setosa 0 89.86419 179.38471 Versicolor 89.86419 0 17.20107 Virginica 179.38471 17.20107 0 These distances were obtained with proc 'CANDISC'