similar to: kernel discriminant analysis with binary data

Dear R Users, I'm very very interested in learning how to use R to carry out a classification of data using discriminant function analysis. I've found the MASS package and the lda function, but the examples in the help system are a bit over my head. I'm not exactly sure how to interpret the output, for example, of if the inputs I've chosen are best suited to my needs. I

Dear All, I am using a specific approach for my master thesis. In essence, a supervised reclassification is used as an intermediate step to find chemical parameters which are able to reclassify defined groups. These variables will be used in a next step where location and scale estimators of the groups are important. Traditionally linear discriminant analysis is used for reclassification which

Hi listers, Does anybody knows if the function qda for quadratic discriminant analysis provides the coefficients of quadratic discriminants... Well, I find out that for the linear discriminant analysis lda, the fonction provides the coefficients... Thanks in advance, Marcio -- View this message in context: http://www.nabble.com/Quadratic-Discriminant-Analysis-%28qda%29-tp22747647p22747647.html

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

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 <-

Kindly let me know the process to carry out a Linear discriminant analysis...thanks in advance Arup -- View this message in context: http://www.nabble.com/Linear-Discriminant-Analysis-tp22199424p22199424.html Sent from the R help mailing list archive at Nabble.com.

Hi, How can I calculate and mark each group centroid in a linear discriminant analysis plot (using ggplot2)? Script: ## originate from http://r.789695.n4.nabble.com/LDA-and-confidence-ellipse-td4671308.html require(MASS) require(ggplot2) iris.lda<-lda(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris) LD1<-predict(iris.lda)$x[,1]

Hello, I am switching from SPSS-Windows to R-Linux. My university is very SPSS-oriented so maybe that's the cause of my problems. I am a beginner in R and my assignments are SPSS-oriented, so I hope I don't annoy anyone with my questions... Right now I've got 2 problems: -I have to evaluate discriminant functions I have calculated with lda(MASS). I can't find a measure that

Hi listers, I am working on a program in order to create an ellipse as a limit of my observations there are well classified. I have two independent variables for an quadratic discriminant function between 2 groups where my mean is zero and my covariance matrix is proportional. Q=y'Ay+c My program follows below with unreal data... My ellipse it's not surrounding the correct number of

Hello, I've been playing with the manova() function to do some pretty straightforward multivariate analyses, and I can't for the life of me figure out how to get at the discriminant functions used. When predicting several variables simultaneously, it's important to be able to gauge how much each variable is contributing to the analysis...a simple p-value isn't really enough. I

Hello R people I now know how to run my discriminant analysis with the lda function in MASS: lda.alain=lda(Groupes ~ Ht.D0 + Lc.Dc + Ram + IDF, gr, CV = FALSE) and it works fine. But I am missing a test and cannot find any help on how to get it, if it exist. The "S" equivalent: discrim(structure(.Data = Groupes ~ Ht.D0 + Lc.Dc + Ram + IDF, class = "formula"), data = gr,

Hi All, I am currently trying to use R to perform some statistical discriminant analysis on a data set using ANOVA. I'm new to anova and R as well. As I understand it, ANOVA can be used to perform discriminant analysis by measuring the value of the f-test when given a data set with a grouping variable and the dependent variable. What I would like to do in R is perform ANOVA and extract the

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

Ooops...I meant "formulations shouldn't be difficult" -------- Original Message -------- Subject: Re: [R] Kernel Fisher Discriminant in R? Date: Wed, 01 Dec 2004 08:05:38 +0200 From: Gorden Jemwa <jemwa at sun.ac.za> To: huh at rti.org CC: r-help at stat.math.ethz.ch You could us the kernlab package as a building block. Implementation of Kernel Fisher Discriminant Analysis

Greetings everyone, I was wondering if anyone had experience in using fda (flexible discriminant analysis) in the mda package. Specifically, I can run the model but I cant locate/produce much of anything beyond the predicted values and confusion matrix. I've been searching for more examples or documentation but cant seem to locate any specific to the program itself. I need to be able to find

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.

Hello, I am using the mda package and in particular the fda routine to classify in term of gear a set of 20 trips. I preformed a flexible discriminant analysis (FDA) using a set of 151 trips. FDAT1 <- fda(as.factor(gear) ~ . , data =matrizR) A total of 22 predictors were considered. 20 of the predictors are "numeric" and 2 are "factors/discrete". The resulting FDA

I don't understand which is the problem Could you help me please? Thanks in advance Marta > str(data_indiciN2) 'data.frame': 200 obs. of 36 variables: $ gruppo: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ... $ I001N2: num 19.32 8.22 28.35 7.24 14.7 ... $ I002N2: num 2.92 2.54 0.11 1.6 7.12 ... $ I003N2: num -22.362 -0.222 -19.291

Dear R gurus, Thank you all for continuous support and guidance -- learning without you would not be efficient. I have a question regarding LD analysis and how to best code it up in R. I have a file of (V52 and 671 time points across all columns) and another file of phonetic features (each vowel is aligned with a distinct binary sequence, i.e. E 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0

While I don't know anything about Box's M test, I googled around and found a Matlab M-file that computes it. Below is my straight-forward translation of the code, without knowing Matlab or the formula (and done in a few minutes). I hope this demonstrates one of Prof. Ripley's point: If you really want to shoot yourself in the foot, you can probably program R to do that for you. [BTW: