similar to: lda

Dear R-helpers, I try to perform glm's with negative binomial distributed data. So I use the MASS library and the commands: model_1 = glm.nb(response ~ y1 + y2 + ...+ yi, data = data.frame) and predict(model_1, newdata = data.frame) So far, I think everything should be ok. But when I want to perform a glm with a subset of the data, I run into an error message as soon as I want to predict

Hi, I'm a R newbie and I have a supervised 2-class classification problem. To find out the best representation of my data (dim = 45). I want to perform LDA und QDA on the diffrent data representations to find out, which is best to discriminate the 2 sets. For LDA there exists a method plot.lda shows (in the 2 class case) a histogramm of the data, projected onto the linear discriminants (pleas

Dear R-helpers, let us assume, that I have the following dataset: a <- rnbinom(200, 1, 0.5) b <- (1:200) c <- (30:229) d <- rep(c("q", "r", "s", "t"), rep(50,4)) data_frame <- data.frame(a,b,c,d) In a first step I run a glm.nb (full code is given at the end of this mail) and want to predict my response variable a. In a second step, I would

Question: How can I get access to the bias value of a boot statistic? Details: Boot function: boot(data, statistic, R, sim="ordinary", stype="i", strata=rep(1,n), L=NULL, m=0, weights=NULL, ran.gen=function(d, p) d, mle=NULL, ...) When I create an object, containing the bootstrap statistic (object <- boot (....))I can call it and will get an output

Hi, I have done different discriminant function analysis of multivariat data. With the CV=True option I was not able to perform the predict() call. What do I have to do? Or is there no possibility at all? You also need the predicted values to produce a plot of the analysis, as far as I know. Here my code: pcor.lda2<-lda(pcor~habarea+hcom+isol+flowcov+herbh+inclin+windprot+shrubcov+baregr,

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 all I've got a problem with unresolved (at least I guess that) LDAP Aliases and Samba. That's my LDAP Setup: ou=alvhaus,ou=ch { base } ou=People,ou=alvhaus,ou=ch { posix and samba accounts } ou=Group,ou=alvhaus,ou=ch { posix and samba groups } ou=Samba,ou=alvhaus,ou=ch { samba base dn } ou=Idmap,ou=Samba,ou=alvhaus,ou=ch ou=Machines,ou=Samba,ou=alvhaus,ou=ch

Dear community, Apologies, I'm still pretty newbie. Anyway, I am performing linar regression analysis. As a common cause of non-normally distributed residuals is non-normally predictor variables, i'm interested in achieving the best transformation of the predictors. I've seen some commands at R, but I would like to know if it exists a command equivalent to gladder (graphic display)

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

Hi, I'm using mda library to solve a discriminant analysis. I get results, but the thing is that I want to use Fisher's method to obtain the classification functions and I'm lost in what I should do: libraries to use, ... Can anybody give me a clue?? Thanks. Carlos Niharra L??pez

Hi, my name is Gastón. I just read a question, made by Walter Durka, about a nonparametric discriminant analysis. It wasn´t any answer, and I was wondering if at this moment there is one. I have the same problem as W. Durka. I´m trying to classify and to cross-validate samples of three tunicates species based on morphometric data and to identify the variables that best discriminate between

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

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:

Hello, Does the "lda" function (package MASS) perform or can it perform classic two-group Fisher discriminant analysis? R-version: 1.9.1, MASS package (latest available) Thank you, Borut Rajer

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)))

I apologize for posting this in essence the second time (no light at the end of the tunnel yet..): is there a way to enforce that "nls" takes both, the data *and* the model definition from the parent environment? the following fragment shows the problem. #======== cut here========== wrapper <- function (choose=0) { x <- seq(0,2*pi,len=100) y <- sin(1.5*x); y <-

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

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-helpers, Using the package Lattice, I performed a PCA. For example pca.summary <- summary(pc.cr <- princomp(USArrests, cor = TRUE)) The Output of "pca.summary" looks as follows: Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.5748783 0.9948694 0.5971291 0.41644938 Proportion of Variance 0.6200604

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