similar to: Mark each group centroid in a linear discriminant analysis plot

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

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 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

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 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(),

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

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

Given a data set with a group factor, I want to translate the numeric variables to their centroid, by subtracting out the group means (adding back the grand means). The following gives what I want, but there must be an easier way using sweep or apply or some such. iris2 <- iris[,c(1,2,5)] means <- colMeans(iris2[,1:2]) pooled <- lm(cbind(Sepal.Length, Sepal.Width) ~ Species,

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,

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.

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,

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 ^

Dear R community, I would be greatful if somebody could shed light on the following. I have created a set of 6 points to check how centroid agglomeration works in cluster analysis: > Y <- data.frame(x=c(-1,1,1,-1,10,12),y=c(1,1,-1,-1,0,0)) It is quite intuitive to understand that the last clusters to be joined will be {1,2,3,4} with {5,6}. Now, the centroid for the first cluster has

Helo there. I am using kmeans of base package to cluster my customers. As the results of kmeans is dependent on the initial centroid, may I know: 1) how can we specify the centroid in the R function? (I don't want random starting pt) 2) how to determine the optimal (if not, a good) centroid to start with? (I am not after the fixed seed solution as it only ensure that the

1) It's crashing because LD1 is produced due to LegalOperations=false in pre-legalize pass. Then Legalization does not know how to handle it so it asserts on a default case. I don't know if it's a reasonable expectation or not but we do not have support for it. I have not tried overriding shouldReduceLoadWidth. 2) I see, that makes sense to some degree, I'm curious if you can

Dear UseRs,i want to find centroid of clusters, which i generated by hclust. Is there a way doing that? i took mean to elements in each cluster to get centroid but i am not sure if i am right? thanks in advanceeliza [[alternative HTML version deleted]]

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,