similar to: Fisher's discriminant functions

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

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, 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]

I'm confused on how is the right way to plot a discriminant analysis made by lda function (MASS package). (I had attached my data fro reproduction). When I plot a lda object : X <- read.table("data", header=T) lda_analysis <- lda(formula(X), data=X) plot(lda_analysis) #the above plot is completely different to: plot(predict(lda_analysis)$x,

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 I'm trying to plot the lda means onto a 2 D plot of discriminant scores. Preferably I'd like these to be in a larger font compared to the discriminant scores. I tried skull.mean.pred <- predict(skulls.lda, as.data.frame(skulls.lda$means), dimen=2) from which I got skull.mean.pred $class [1] 1 2 3 4 5 Levels: 1 2 3 4 5 $posterior 1 2 3 4

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, I'm a newbie in R and don't much aobut all the modules and their capabilities, but I'm interested in solving a problem about a discriminant analysis done with SPSS tool. The thing is that I would like to make a discrimant analysis similar to the one done with SPSS, but I can't find the way to solve it. I've been playing with R and I can handle more or less my data,

Dear R Help Members, I am aware how to plot the LD1 vs LD2 from a lda in R, using the code: plot(baseline.systat.hat$x, col=baseline.systat.hat$class,pch=as.numeric(baseline.systat.hat$class)) However, I need to use the quadratic discriminant function, qda due to data properties. Is there a way to obtain the same sort of plot for from a qda object (and the output of predict qda). I have not

Greetings R experts, I'm having some difficulty recovering lda objects that I've saved within sublists using the save.image() function. I am running a script that exports a variety of different information as a list, included within that list is an lda object. I then take that list and create a list of that with all the different replications I've run. Unfortunately I've been

Dear R-List, I tried to fit a partial credit model using the "pcmdat" from eRm-package comparing the results of mirt, eRm, ltm and winsteps. The results where quite different, though. I cannot figure out what went wrong and I do not know which result I can rely on. This is what I did in R library(mirt) #load(file="u3.RData")

I'm sory for my weak english. I need to analyze this subject : x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 y 0 0 1 0 0 1 0 0 1 0 czarne 1 1 0 0 0 0 1 0 0 0 rude 0 0 1 0 0 1 1 0 0 0 braz 0 0 1 0 1 0 1 0 0 0 blond 1 0 0 0 0 1 0 0 0 1 rude 1 1 0 0 0 0 0 0 0 1 blond 0 0 1 1 0 0 0 0 1 0 czarne 1 0 0 1 0 0 1 0 0 0 blond 0 0 1 0 0 1 1 0 0 0 blond 1 0 0 0 0 1 1 0 0 0 czarne 0 0 1 0 0 1 0 0 0 1 czarne 1 0 1 0 0 0

I work with Windows, R version 2.4.1 I am trying to plot the results of a discriminant analysis done using the lda function in the MASS library. The discriminant. analysis goes like this: data.tb<-read.table('C:\\Documents and Settings\\silvia\\Desktop\\dicrim_test.txt', header=T) ## the actual made-up test matrix is pasted below train<-sample (1:36, 15) table

Friends Briefly... In the documentation for lda in MASS it describes the value 'scaling' as 'a matrix which transforms observations into discriminint functions...'. How? Verbosely... I have a matrix of data. 9 independent variables and describing 3-classes. About 100 observations in total. A 10x100 matrix of data. I am trying to generate two discriminant functions and i

Further to my earlier question, I'm perhaps a bit confused about memory serialisation. The following example, compiled using clang for the MSP430: target datalayout = "e-p:16:16:16-i8:8:8-i16:16:16-i32:16:32-n8:16" target triple = "msp430-??-??" @y = common global i16 0, align 2 @x = common global i16 0, align 2 define void @f() nounwind { entry: %0 = load i16* @y,

On Wed, Jul 27, 2011 at 3:50 PM, Matt Johnson <johnso87 at crhc.illinois.edu> wrote: > Hi Eli, > > On 07/27/2011 04:59 PM, Eli Friedman wrote: >> >> On Wed, Jul 27, 2011 at 2:28 PM, Matt Johnson >> <johnso87 at crhc.illinois.edu>  wrote: >>> >>> Hi All, >>>     I'm writing a backend for a target which only supports 4-byte,

I looked into those patches but I don't think they will help in my situation because my problems occur during instruction selection rather than scheduling. A simple and concrete example is a pattern like: [(set GR:$dst (add GR:$src (nvload addr:$mem)))] where nvload matches a load provided that isVolatile() is false. If the selection DAG looks like: | | LD1 LD2 ^

Hi Eli, On 07/27/2011 04:59 PM, Eli Friedman wrote: > On Wed, Jul 27, 2011 at 2:28 PM, Matt Johnson > <johnso87 at crhc.illinois.edu> wrote: >> Hi All, >> I'm writing a backend for a target which only supports 4-byte, >> 4-byte-aligned loads and stores. I custom-lower all {*EXT}LOAD and >> STORE nodes in TargetISelLowering.cpp to take advantage of

I'm curious about this code in ReduceLoadWidth (and in DAGCombiner in general): if (LegalOperations && !TLI.isLoadExtLegal(ExtType, ExtVT)) return SDValue <http://llvm.org/docs/doxygen/html/classllvm_1_1SDValue.html>(); LegalOperations is false for the first pre-legalize pass and true for the post-legalize pass. The first pass is target-independent yes? So that makes sense.

Hi all. I would like to understand what are the units defined on the y-axis when you plot the one-dimensional predictions (histograms) from lda() (MASS) discriminant function objects? While the helpfile suggests that a histogram is returned by default, the presumably proportion-like values for each group seem to add up to more than 1, and I'm not sure how to interpret the code from ldahist(),