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

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

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

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

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 <-

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

Dear List, I'd like to run a discriminate analysis on a data set, but have no idea how to go about this in R. I have attempted to locate info in the manuals, but may not be consulting the right sections or documents. Can anyone point me to appropriate documentation if such exists. Many thanks, David S. David White sdavidwhite at bigfoot.com Columbus, Ohio

is there a way of estimating derivative curves, similar to the ones we get from 'locpoly', from 'loess' estimation. i am interested in estimation of 1st and 2nd derivatives... --------------------------------- [[alternate HTML version deleted]]

Hi there, I have just started using the MASS package in R to run M-estimator robust regressions. The final output appears to only give coefficients, degrees of freedom and t-stats. Does anyone know why R doesn't compute R or R-squared and why doesn't give you any other indices of goodness of fit? Does anyone know how to compute these in R? Sophie -- View this message in context:

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

My objective is to look at differences in two species of fish from morphometric measurements. My morphometric measurements are head length, eye diameter, snout length, and measurements from tail to each fin. I want to use discrimanant function analyis to determine if there are differences between the two species. I am familiar with R but new to discrimannt function analysis. I want to learn

Hello everyone, I am totally new to the R program. I have had a look at some pdf documents that I downloaded and that explain how to do many things in R; however, I still cannot figure out how to do what I want to do, which is to perform Discriminant Correspondence Analysis on a rectangular matrix of data that I have in an Excel file. I know R users frown upon Excel and recommend converting Excel

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