similar to: Y axis of 1-D Linear Discriminant Histograms

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, 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]

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 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

Hi, I am using the lda function from the MASS library. I would to find the decision boundaries of each class and subsequently plot them. I wonder if anybody can offer any help on this topic? Below I applied the lda function on a small dataset of mine. Any help will be much appreciated. > library(MASS) >

Hi there, I am a master student in Denmark, and apply R to analyze the drug data. I use the function 'lda' to obtain a linear discriminant object, and then wish to use 'plot.lda' to do the plot on this object like below. drug.lda <- lda(Inhibition ~ NET_CHARGE + PKA_1 + MW + MLOGP, data = drug.class, method = "moment") plot.lda(drug.lda,

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,

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 <-

> # >> # Generate the figures (on screen) >> # >> # Image generation - function definition >> pic_onscr<-function(matrix, title, cex_val=1) > + {x11() + par(mgp=c(5,2,0), # axis margins + # (title, > labels, line) + mar=c(7,4,4,2), # plot margins (b,l,t,r) + > las=1) # horizontal labels + plot(matrix, # data to plot + >

# lda.r > # > # Author: Amsha Nahid, Jairus Bowne, Gerard Murray > # Purpose: Perform Linear Discriminant Analysis (LDA) > # > # Input: Data matrix as specified in Data-matrix-format.pdf > # Output: LDA plot > # > # Notes: Missing values (if any) are replaced by the half of the lowest > # value in the entire data matrix. >

Hello all- I am a relatively new user of R and am working through a graduate course in Statistics that uses Minitab, SAS and some Matlab. I like using R but am having some trouble lining up the output from lda() to that of the other programs' results. The dataset below is a modified set of wine data from the Pinot Noir data set as an illustration of the 2 group LDA scenario. Mo Ba

hello, i already include the error in blue color word. i hope it can help you to understand my question. if not burden you, please give me a guide how to correct the error or maybe you can correct the coding cause error. thank you. > # lda.r > # > # Author: Amsha Nahid, Jairus Bowne, Gerard Murray > # Purpose: Perform Linear Discriminant Analysis (LDA) > # > #

Hi R folks, How can I generate the discriment function from lda? I have an unbalanced data set. one class has about 25 entries and another class has about 200 entries. I used lda for classification > z<- lda(V3 ~ V1+V2, data) > z Prior probabilities of groups: 0 1 0.91111111 0.08888889 Group means: V1 V2 0 0.4445161 0.04723951 1 0.4058900

Dear R folks, Thanks to all your help before I have loaded a 1-D toy data set into R and did LDA on it. The toy data has Class=0 if value>0. > XY <-- read.table ("test.xy",header=T ) > XY X.Class value 1 0 60.4897262 2 0 32.9554489 3 -1 -53.6459189 4 0 44.4450579 . . . 998 -1 -43.4183157 999 0

Hi, When I am using ldahist function I would like to specify different colors for each of the groups in the data I am using. Is it possible? If not does anybody know of another function to plot multiple histograms on one plot for different groups. Thanks ===== Thanks Fairouz Makhlouf

Rusers: I am trying to apply a quadratic discriminant function to find the best classification outcomes. 1 is assigned to the values greater than a threshold value; and 0 otherwise. I would like to see how the apparent error rates and the optimal error rate change with increasing threshold values. I have a 1000*10 data matrix: n=1000 and p=10. Here is what I wrote so far, but seems to be

Dear WizaRds, After consulting different sources I am still unable to understand the correct use of return() in nested functions. To illustrate the problem: f <- function(x,y,type){ est1<-function(x,y){ z=x+y out(x,y,z)} est2<-function(x,y){ z=x*y out(x,y,z)} out<-function(x,y,z) return(x,y,z) if (type=="est1") est1(x,y) if (type=="est2") est2(x,y) }

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