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: p1=exp(d1-d2)/(1+exp(d1-d2)) p2=1/(1+exp(d1-d2)) In R, the lda function only generates 1 discriminant function, and exactly the same posterior probability as the LDA function in Partek does. My question is what's the difference (or relationship) between the above 2 LDA functions, where I can find the reference for the partek LDA function? Thanks Steve
Note that p1 and p2 depend only on d1 - d2. Have a look at a book like Ripley's "Pattern Recognition and Neural Networks" or Anderson's multivariate statistics book for further information. Reid Huntsinger -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Steve Adams Sent: Wednesday, June 15, 2005 4:54 PM To: r-help at stat.math.ethz.ch Subject: [R] 2 LDA 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: p1=exp(d1-d2)/(1+exp(d1-d2)) p2=1/(1+exp(d1-d2)) In R, the lda function only generates 1 discriminant function, and exactly the same posterior probability as the LDA function in Partek does. My question is what's the difference (or relationship) between the above 2 LDA functions, where I can find the reference for the partek LDA function? Thanks Steve ______________________________________________ R-help at stat.math.ethz.ch mailing list stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! R-project.org/posting-guide.html