similar to: lda of MASS library

Dear R folks, Thanks to all your help before I have loaded a 1-D toy data set into R and did LDA on it. The toy data has Class=0 if value>0. > XY <-- read.table ("test.xy",header=T ) > XY X.Class value 1 0 60.4897262 2 0 32.9554489 3 -1 -53.6459189 4 0 44.4450579 . . . 998 -1 -43.4183157 999 0

Hi, I am using Partek for LDA analysis. For a binary response variable, it generates 2 discriminant functions, one for each of the 2 levels of the response variable. And I can simply calculate 2 discriminant scores (say d1 and d2) for each sampples using the 2 discriminant functions, then I can use the following formula to compute the posterior probability for the sample:

With predict.lda the posterior probabilities only relate to the existing Class definitions. This is fine for Class definitions like gender but it is a problem when new data does not necessarily belong to an existing Class. Is there a classification method that gives posterior probabilities for Class membership and does not assume the new data must belong to one of the existing Classes? A new

Hello, I am using WinXP, R version 2.3.1, and SAS for PC version 8.1. I have mostly used SAS over the last 4 years and would like to compare the output of PROC DISCRIM to that of lda( ) with respect to a very specific aspect. My data have k=3 populations and there are 3 variates in the feature space. When using using the code PROC DISCRIM DATA = FOO OUT = FOO_OUT OUTSTAT = FOOSTAT

Hello: I am new to R and statistics and I have two questions. First I need help to interpret the cross-validation result from the R linear discriminant analysis function "lda". I did the following: lda (group ~ Var1 + Var2, CV=T) where "CV=T" tells the lda to do cross-validation. The output of lda are the posterior probabilities among other things, but I can't find an

Hi everyone, I'm trying to solve a problem about how to get the Fisher's discriminant functions of a "lda" (linear discriminant analysis) object, I mean, the object obtained from doing "lda(formula, data)" function of the package MASS in R-project. This object gives me the canonical linear functions (n-1 coefficients matrix of n groups at least), and only with this

Sir, I am working with multiclass discriminant analysis.(say response variable has 3classes).In R, using lda(), I get 2 sets of coefficients for the discriminant function.Now, I want to put a new x-vector(vector of independent variables) and want to check it corresponds to which class of y.Is there any formula for doing this? or how can I do this? Regards, Suman Dhara [[alternative HTML

Hi all, I obtained a strage result with LDA (MASS) function in R with NIR data. I tried both CV (leave one out cross validation) and splitting my data in odd (training) and even (prediction) sets. In all the cases the minimum error was near to 0. Due to the strange result, I tried with SPSS IBM software and it give me around 11% of minimum error with and without leave one out cross validation.

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

Hello, I have run a linear discriminant analysis for the simple 2 group case using the MASS package lda() function. With priors fixed at 0.5 and unequal n for each group, the output basically provides the group means and the LD1 value. There is no automatic output of the cutoff (decision boundary) value used to classify values of the response variable into the different groups. I have tried

Hi, I am trying to recreate the right graph on page 524 of Gelman's 2006 paper "Prior distributions for variance parameters in hierarchical models" in Bayesian Analysis, 3, 515-533. I am only interested, however, in recreating the portion of the graph for the overlain prior density for the half-Cauchy with scale 25 and not the posterior distribution. However, when I try:

Shuji, I suspect that bruto blows up because your data are linearly separable. To see this (if you didn't already know), try library(lattice) splom(~x, groups = y) and look at the first row. If you are trying to do classification, there are a few methods that would choke on this (logistic regression) and a few that won't (trees, svms etc). I would guess that bruto is in the latter

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,

Hello, Does the "lda" function (package MASS) perform or can it perform classic two-group Fisher discriminant analysis? R-version: 1.9.1, MASS package (latest available) Thank you, Borut Rajer

Dear All This is more of a statistics question than a question about help for R, so forgive me. I am using lda from the MASS package to perform linear discriminant function analysis. I have 14 cases belonging to two groups and have measured each of 37 variables. I want to find those variables that best discriminate between the two groups, and I want to visualise that and create a

Dear R-users (and developers), I am looking for an efficient framework to carry out parameter estimations based on MCMC (optionally with specified priors). My goal is as follow: * take ANY R-function returning a likelihood-value (this function may itself call external programmes or other code!) * run a sampler that covers the multidimensional parameter space (thus creating a posterior

Hi R experts. I performed an Linear Discriminant Analysis (lda) and now I want to plot the first two axes (LDA1 and LDA2). Well MASS package have the plot.lda and pairs.lda to do that. But, they don’t let me personalize them, once they don’t accept the type=’n’ plot. So I start looking at the lda object properties, trying to found out my groups (n=4) axis coordinates, curiously I couldn’t. There

Hi there, I am a master student in Denmark, and apply R to analyze the drug data. I use the function 'lda' to obtain a linear discriminant object, and then wish to use 'plot.lda' to do the plot on this object like below. drug.lda <- lda(Inhibition ~ NET_CHARGE + PKA_1 + MW + MLOGP, data = drug.class, method = "moment") plot.lda(drug.lda,

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

Hello everyone, I am working on a linear discriminant analysis and am having issues finding the full output of my lda. Specifically, there is no reporting of the Proportion of Trace that is a normal output of the procedure. I'm using a csv file and everything is reading in correctly. I've looked and looked and can't figure out why my output is not complete. Is it something simple that