similar to: LDA newbie question

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

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,

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

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

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

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, 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:

Hi list, I'm looking about lda function. I'd like to know how calcolate the value of the discriminant functions for the original datas. I see that in the result object "lda" there is $scaling a matrix which transforms observations to discriminant functions, normalized so that within groups covariance matrix is spherical. I'd like to have the value of the discriminant

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

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.

I'm sory for my weak english. I need to analyze this subject : x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 y 0 0 1 0 0 1 0 0 1 0 czarne 1 1 0 0 0 0 1 0 0 0 rude 0 0 1 0 0 1 1 0 0 0 braz 0 0 1 0 1 0 1 0 0 0 blond 1 0 0 0 0 1 0 0 0 1 rude 1 1 0 0 0 0 0 0 0 1 blond 0 0 1 1 0 0 0 0 1 0 czarne 1 0 0 1 0 0 1 0 0 0 blond 0 0 1 0 0 1 1 0 0 0 blond 1 0 0 0 0 1 1 0 0 0 czarne 0 0 1 0 0 1 0 0 0 1 czarne 1 0 1 0 0 0

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, 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

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

Dear R-helpers, if I am right a discriminant analysis can be done with "lda". My questions are: 1. What method to discriminate the groups is used by "lda" (Fisher's linar discriminant function, diagonal linear discriminant analysis, likelihood ratio discriminant rule, ...)? 2. How can I see, which method is used? (Typing just lda does not give me any code). Thank you in

Hi !! I am trying to analyze some of my data using linear discriminant analysis. I worked out the following example code in Venables and Ripley It does not seem to be happy with it. ============================ library(MASS) library(stats) data(iris3) ir<-rbind(iris3[,,1],iris3[,,2],iris3[,,3]) ir.species<-factor(c(rep("s",50),rep("c",50),rep("v",50)))

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

Hi! Will you please ask a newbie's question? Need linear discriminant analysis, need to capture discrim. function coefficiemts for further processing. Using R ver 1.4.0 package MASS function lda() Help says resulting object should have "components" (among others): means, scaling, svd, but program returns NULL for all of them Thank you!

HI, Dear community, I am using the linear discriminant analysis to build model and make new predictions: > dim(train) #training data [1] 1272 22 > dim(valid) # validation data [1] 140 22 lda.fit <- lda(out ~ ., data=train, na.action="na.omit", CV=TRUE) # model fitting of linear discriminant analysis on training data > predict(lda.fit, valid) # make prediction on

Hello, I would like to plot the results of a LDA analysis plotting the discriminant scores with the decission boundaries on it with rggobi. I have GGobi already installed on my computer. I have three classes, so the plot would be LD1xLD2 plus the decission boundaries. Here there is the code I use make the plot: library(MASS) data <- zgcppr273K.pca$x[,1:7] Tumor <-