similar to: 2 LDA

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

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

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

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

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

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,

The Partek package (www.partek.com) allows only two selections for Multiple Test Correction: Bonferroni and Dunn-Sidak. Can anyone suggest why Partek implemented Dunn-Sidak and not the other methods that R has? Is there any particular advantage to the Dunn-Sidak method? R knows about these methods (in R 2.1.1): > p.adjust.methods [1] "holm" "hochberg" "hommel"

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

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-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 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

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)))

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, 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

Hi, I am wondering if it is possible to get an estimate of standard error of the predicted posterior probability from LDA using lda() from MASS? Logistic regression using glm() would generate a standard error for predicted probability with se.fit=T argument in predict(), so would it make sense to get standard error for posterior probability from lda() and how? Another question about standard

Hello, I'm tring to make a linear discriminant analysis eith lda (MASS). In the value of the resulting object is there information about the value of the centroids of the discriminated group for the discriminant functions? Thank you in advance for your help! Leonardo -- Leonardo Lami lami at faunalia.it www.faunalia.it Via Colombo 3 - 51010 Massa e Cozzile (PT), Italy Tel:

Hi all, I am trying to plot the results of a discriminant analysis done with lda(MASS) but my groups appear in two different plots (in the same graphics device) and I want to combine them in one plot. My code looks like: BirdTrain.lda <- lda(Bdisperser~., data=BirdTrain.mx) predict(BirdTrain.lda) plot(BirdTrain.lda) I have two types of Bdisperser, so I only get one linear discriminant

Hi. I asked a question about lda() and got some answers. However, one question remains (which is not independent of the earlier ones): What output does lda() produce which I can use to compute the posteriors? I know predict(lda())$posterior will give me precisely the posteriors, but suppose I'd like to compute them myself, outside of R. So far, I have not been able to use

In the following code I have created the posterior density for a Bayesian survival model with four parameters. However, when I try to use the adapt function to perform integration in four dimensions (on my old version of R I get an error message saying that I have applied a non-function, although the function does work when I type kernel2(param0, theta0), or on the newer version of R the computer