similar to: does function predplot still exist?

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

partition.tree plots in 2d the partition of a classification tree produced by the function tree (assuming the data frame from which it was computed has two continuous predictors). I get an error when I feed a tree produced by rpart to partition.tree (since trees produced by rpart are superclasses of those produced by tree). Is there an equivalent of partition.tree for objects of class rpart?

I'd like to be able to cut dendrograms at a height I specify and then plot the resulting subtrees. I wanted to use the dendrogram object for this purpose because there doesn't seem to be a canned way to cut a hclust object and get a list of hclust objects, but there is a function (cut) that does that for dendrograms. The problem I'm having is that when I plot a dendrogram, I

I'd like to change the way plot.hclust displays an hclust object. Here's a description of how it's done now, from the R documentation of hclust: In hierarchical cluster displays, a decision is needed at each merge to specify which subtree should go on the left and which on the right. Since, for n observations there are n-1 merges, there are 2^{(n-1)} possible

I am trying to run, in R -Windows95, the example in MASS2, section 17.2. When I run predplot() on cush.lda I get > predplot(cush.lda, "LDA") Error: couldn't find function "max.col" I get the plot OK, but within predict(), it balks on max.col A more general question about using VR. Is there an easy way to use library on VR in general or do I have to move each into

I'm having trouble with save and load in R 1.4.0 (on RH 7.2) I originally noticed this with a large R object, but it happens with a tiny one as well: > a <- c(1,2,3) > save(a,file="test.RData") > a1 <- load("test.RData") > print(a1) NULL The file "test.RData" is created, and it does have something in it. When I tried saving a

I have a classification tree that I computed via the tree function (in the tree package). I'd like to compute a misclassification table (if that's the right term) on the data used to compute the tree. That is, I want to compute a table with the different classes (i.e.,levels of the response factor) on the rows and the columns, and where entry [i,j] is the number of times the tree

Hello, Does logistic regression really provide better results than lda or qda ? (my purpose is not classification but highlighting of discriminant variables). If this is true, where could I get an R script performing stepwise logistic regression ? Thanks -- Daniel AMORESE Lab. M2C "Morphodynamique Continentale et C?ti?re" UMR CNRS 6143 Caen/Rouen Centre de G?omorphologie UCBN

Hi all! I am Angela, an Italian researcher and I am looking for an application that allows working in team, making a shared codebook, share a text database and code it. I tried AnSWR (the CDC''s QDA application) but its installation is too complicated for students and it is not working on latest version of Windows. What about Weft? Is it possible to share a project or, at least, merge

Hi all, If I run LDA on the same data (2-class classification) with default(no priors specified in the lda function) vs. "prior=c(0.5, 0.5)", the results are different. The (0.5, 0.5) priors give better 1-classify-to-1 rate, and the proportional priors(default, no priors specified in the lda function) give better 0-classify-to-0 rate, for both training and testing data sets. However,

Hi, What does this mean when I have something like: > qda.boot <- boot(train, qda.bootstrap, R = 500) Error in qda.default(structure(data.matrix(x), class = "matrix"), ...) : Rank deficiency in group M with my qda.bootstrap() looks something like: > qda.bootstrap <- function(data, index) { + boot.qda <- qda(x = data[index, 2:9], group = data[index, 1]) + qda.pred

Hello, I am Souradeep Chattopadhyay and I am a graduate student at Iowa State University Department of Statistics. Can anyone please explain the mathematical formulation behind the scaling matrix returned by the qda function in MASS package. I want to understand how this scaling matrix is derived from the inputs given to the qda function. Example Code The following example is using

z <- qda(train, cl) save(z, file = "qda.dat") load("qda.dat") predict(qda.dat, test)$class I'm trying to save z where z <-qda(train, cl) and load z for later use in predict(z, test)$class. I think I successfully saved z by save(z, file = "qda.dat") but when I tried to load by load("qda.dat") and call predict(qda.dat, test)$class, it gives me error

You need to learn how to access code for nonexported methods. See ? "::" > methods(qda) [1] qda.data.frame* qda.default* qda.formula* qda.matrix* see '?methods' for accessing help and source code Shows you that the methods are not exported from the namespace. Hence you need to use the triple colon operator to see their code: > MASS:::qda Once you have the code, I

I guess the question that is being asked here is what is the scaling matrix that is being returned in the qda object. The help file on qda() says: ... scaling: for each group ?i?, ?scaling[,,i]? is an array which transforms observations so that within-groups covariance matrix is spherical. ... This is a bit ambiguous. I tried a few cases (spectral, QR decomposition, especially given that it is an

Hi Alex, Hi Weft-Users! I am looking for a tool for doing a qualitative analysis of mailing-list data. This means I have a *lot* of individual documents (the typical case: around 500-5000 emails), belonging to the same "super-document" (a mailing list). I have looked at several tools and I like that Weft is public domain and written in Ruby, so I want to figure out whether it

Dear R user, I'm using qda (quadratic discriminant analysis) function (package MASS) to classify 58 explanatory variables (numeric type with different ranges) using a grouping variable (factor 2 levels "0" "1"). I'm using the qda method for class 'data.frame' (in this way I don't need to specify a formula). Using the function:

Dear wxruby users I''ve been trying to get wxruby-based weft-qda 0.9.6 running on a linux from scratch 6.1 system (this list had a discussion about weft-qda last year and alex fenton''s also been trying to help me out - no luck so far). I get stuck with wxruby 0.60 (I think). I compiled wxruby against wxGTK 2.4.2 (without gtk2, without unicode - gtk+ version is 1.2.10) - after

Hi, I have been using some of the functions in r for classification purposes, chiefly lda, qda, knn and nnet. My problem is that the only one I can figure out how to represenent graphically is lda (using plot.lda). I have tried 'fooling' this function into accepting qda input for plotting but to no avail. I wonder if you have any suggestions? Thanks alot, Anne Marie Power Marine lab.

Dear list, (I've not upgraded to R1.4 yet) I have the following $class component in a predict.qda object: > unique(mod23S.qda.pred$class) [1] 12 17 8 10 4 9 5 13 14 19 20 15 6 3 7 1 23 11 18 21 16 2 22 NA Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Nevertheless, when I try to identify the individual(s) with NA, I get: >