similar to: Linear Discriminant Analysis

Hi, How can I calculate and mark each group centroid in a linear discriminant analysis plot (using ggplot2)? Script: ## originate from http://r.789695.n4.nabble.com/LDA-and-confidence-ellipse-td4671308.html require(MASS) require(ggplot2) iris.lda<-lda(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris) LD1<-predict(iris.lda)$x[,1]

Dear R users, I want to do discriminant analysis on my data. I have successfully followed the discriminant analysis in V & R on the iris data: > ir <- rbind (iris3[,,1],iris3[,,2],iris3[,,3]) > ir.species <- c(rep("s",50),rep("c",50),rep("v",50)) > a <- lda(log(ir),ir.species) > a$svd^2/sum(a$svd^2) [1] 0.996498601 0.003501399 > a.x <-

Hi all. I would like to understand what are the units defined on the y-axis when you plot the one-dimensional predictions (histograms) from lda() (MASS) discriminant function objects? While the helpfile suggests that a histogram is returned by default, the presumably proportion-like values for each group seem to add up to more than 1, and I'm not sure how to interpret the code from ldahist(),

Hi, I'm using mda library to solve a discriminant analysis. I get results, but the thing is that I want to use Fisher's method to obtain the classification functions and I'm lost in what I should do: libraries to use, ... Can anybody give me a clue?? Thanks. Carlos Niharra L??pez

My objective is to look at differences in two species of fish from morphometric measurements. My morphometric measurements are head length, eye diameter, snout length, and measurements from tail to each fin. I want to use discrimanant function analyis to determine if there are differences between the two species. I am familiar with R but new to discrimannt function analysis. I want to learn

I use Widows, R version 2.4.1 I have 4 questions on lda (MASS) (code is pasted below): 1st. How can I obtain the statistics and p-value associated with discriminant analysis? Am I supposed to calculate that manually by squaring the svd value and looking the p value up in a table? I am writing the following code: training.mx<-read.table('C:\\Documents and Settings\\silvia\\My

The following command produces red axis line in a pairs plot: pairs(iris[1:4], main = "Anderson's Iris Data -- 3 species", pch = "+", col = c("red", "green3", "blue")[unclass(iris$Species)]) Trying to fool pairs in the following way produces the same plot as above: pairs(iris[1:4], main = "Anderson's Iris Data -- 3

Hi Dieter and R community: I tried both of these three versions with ylim as suggested, none work: I am getting only single (pch = 16) not overlayed (pch =3) everytime. *vs 1* require(lattice) xyplot(Sepal.Length ~ Sepal.Width | Species , data= iris, panel= function(x, y, subscripts) { panel.xyplot(x, y, pch=16, col = "green4", ylim = c(0, 10)) panel.lmline(x, y, lty=4, col =

Hello, I'm trying to pull off a certain graph using splom, and can't quite get my panel functions right. Basically, the equivalent using pairs would be something like this (using iris data set as an example): panel.corval <- function(x, y, digits=2, prefix="", cex.cor,col,pch) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(0, 1, 0, 1))

Dear All, I am using a specific approach for my master thesis. In essence, a supervised reclassification is used as an intermediate step to find chemical parameters which are able to reclassify defined groups. These variables will be used in a next step where location and scale estimators of the groups are important. Traditionally linear discriminant analysis is used for reclassification which

Hi listers, I am working on a program in order to create an ellipse as a limit of my observations there are well classified. I have two independent variables for an quadratic discriminant function between 2 groups where my mean is zero and my covariance matrix is proportional. Q=y'Ay+c My program follows below with unreal data... My ellipse it's not surrounding the correct number of

Hi, Is it possible to create a legend in a pairs() plot? For example, suppose I have the following R codes: data(iris) pairs(iris[1:4], main = "Anderson's Iris Data -- 3 species", pch = 21, bg = c("red", "green3", "blue")[codes(iris$Species)]) How can I created a legend that shows which colour represents to which Iris species? Thanks,

Kindly let me know the process to carry out a Linear discriminant analysis...thanks in advance Arup -- View this message in context: http://www.nabble.com/Linear-Discriminant-Analysis-tp22199424p22199424.html Sent from the R help mailing list archive at Nabble.com.

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>

Hi, The following code produces a plot on X11: splom(~iris[1:4], groups = Species, data = iris, panel = panel.superpose, key = list(title = "Three Varieties of Iris", columns = 3, points = list(pch = super.sym$pch[1:3], col = super.sym$col[1:3]), text = list(c("Setosa", "Versicolor", "Virginica")))) However,

Dear R I'm trying to plot the lda means onto a 2 D plot of discriminant scores. Preferably I'd like these to be in a larger font compared to the discriminant scores. I tried skull.mean.pred <- predict(skulls.lda, as.data.frame(skulls.lda$means), dimen=2) from which I got skull.mean.pred $class [1] 1 2 3 4 5 Levels: 1 2 3 4 5 $posterior 1 2 3 4

Hi there, I got a piece of code for the Iris data which allows to display correlation coefficients for each Iris species in the lower panel (color coded). I would now like to add e.g. a "*" to show the significance of each correlation next to the correlation coefficient. Furthermore I would like to make a t.test between the species "setosa" and "versicolor" for

Hi, I would like to have six dot plots on one page. I make the plots in a 'for loop' because it is six times the same graph but for different subjects (species). I tried it with "par(mfrow=c(3,2), oma=c(3,3,2,0), mar=c(2,2,2,2))"; but this does not work for dot plots apparently. Then I tried with print(). But then I had to give the dot plots names. This also does not work.

Hello everyone, I am totally new to the R program. I have had a look at some pdf documents that I downloaded and that explain how to do many things in R; however, I still cannot figure out how to do what I want to do, which is to perform Discriminant Correspondence Analysis on a rectangular matrix of data that I have in an Excel file. I know R users frown upon Excel and recommend converting Excel

Fellow R Users: I'm not extremely familiar with lda or R programming, but a recent editorial review of a manuscript submission has prompted a crash cousre. I am on this forum hoping I could solicit some much needed advice for deriving a classification equation. I have used three basic measurements in lda to predict two groups: male and female. I have a working model, low Wilk's lambda,