similar to: standardized coefficients in lda

I used the "lda" function in the MASS library of S-Plus (R) to do a linear discriminant analysis, and got the linear coefficients, say b1 and b2 for the 2 predictors x1 and x2. I have trouble to calculate the discrimiant scores for each observation, I used 3 ways to try to repeat the scores returned by the "predict" function in S-Plus: 1. b1*x1+b2*x2 2. b1*(x1-mean of

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, For a simple problem with 1 predictor (x) and 2 classes (0 and 1), the linear discriminant function should be something like 2(mu_0 - mu_1)/var x + x-independent-terms where var is the common variance.

Hello all- I am a relatively new user of R and am working through a graduate course in Statistics that uses Minitab, SAS and some Matlab. I like using R but am having some trouble lining up the output from lda() to that of the other programs' results. The dataset below is a modified set of wine data from the Pinot Noir data set as an illustration of the 2 group LDA scenario. Mo Ba

Friends Briefly... In the documentation for lda in MASS it describes the value 'scaling' as 'a matrix which transforms observations into discriminint functions...'. How? Verbosely... I have a matrix of data. 9 independent variables and describing 3-classes. About 100 observations in total. A 10x100 matrix of data. I am trying to generate two discriminant functions and i

Hi, I have tried the (MASS) LDA example (Iris) that's given on the help pages but I don't fully understand the results. Perhaps someone could be so kind to explain it to me. On the help pages it says as return values: prior means scaling svd N call What I get as results are prior means coefficients of linear discriminants proportion of trace OK, so the first 3 appear to be the same

Hello, I am using linear discriminant analysis (lda) from the MASS library to classify data in two classes. 1. How do I get the full LDA model? The function lda reports coefficients but not the constant term? Currently I run the linear model function lm on the LDA scores and variables to find out the complete model. Is there an easier way? 2. How do I get the cut-point (the point at which the

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

Hi, it seems that the lda function in MASS library doesn''t give out the constant for the linear discriminant function under the situation that we don''t use standardized variable, anyone knows how to obtain the constant in order to construct the linear discriminant function? I understand that if the priors are set to be 1/2, the threshold of the discriminant score used to

Dear list members, In sem, std.coef() will give me standardized coefficients from a sem model. But is there a trick so that path.diagram can use these coefficients rather than unstandardized ones? Thanks Steve Powell From: John Fox <jfox_at_mcmaster.ca> Date: Wed 28 Feb 2007 - 14:37:22 GMT Dear Tim, See ?standardized.coefficients (after loading the sem package). Regards, John John

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

Hi, I am trying to solve a discriminant analysis in the same way as SPSS does it. I mean, given an amount of data, to train the discriminant analysis I obtain the Fisher's discriminant functions, an array of coefficients per group, so if I have 8 groups I get 8 linear functions, that allow me to operate with them easily and without a great cost of time. My main problem is that I need to

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 R folks, How can I generate the discriment function from lda? I have an unbalanced data set. one class has about 25 entries and another class has about 200 entries. I used lda for classification > z<- lda(V3 ~ V1+V2, data) > z Prior probabilities of groups: 0 1 0.91111111 0.08888889 Group means: V1 V2 0 0.4445161 0.04723951 1 0.4058900

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:

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

Hi everyone, how can I calculate standardized logistic regression coefficients using R? I used "glm" resp. "lrm" from the design-package in order to calculate logistic regression coefficients but I'm wondering if there's a possibility to get standardized logistic regression coefficients? Thanks in advance! Alexander Roth

Dear all, I am looking for ways to compute standardized logistic regression coefficients. I found papers describing at least 6 different ways to standardize logistic regression coefficients. I also found a very old (Thu May 12 21:50:36 CEST 2005) suggestion by Frank E Harrell (one of the colleagues who frequently contribute on this list) saying... Design doesn't implement those because they

Hello, first of all: I'm new to R and have only used SPSS befor this (which can't do this at all...). I'm trying to output some regression results to latex. The regressions are normal OLS and I'm trying to output the results with standardized regression coefficients and t-statistics based on "Huber-White sandwich estimator for variance". The final result should be

Dear all When plotting the results of lda and predict.lda, I expect the axis dimensions to remain essentially the same - predict.lda projects new observations onto linear discriminants. However, I am getting different ranges of values along the x-axis when producing plots following each: plot(lda) yields values in the range approximately -3 to +4, while plot(predict.lda) yields values for the