similar to: Reporting ppr fits and using them externally.

Hi all, I am doing a projection pursuit regression using the ppr() function from modreg. I would also like to use predict.ppr(). However, I cannot find any information about it in the help files. There is a link to predict.ppr in the index for modreg, but that link is to the help for ppr(). Has predict.ppr() not been implemented? If not, does anyone have a suggestion as to how to implement

Hi, Recently, I installed the 1.1.0 version of R (for Windows), since it includes an implementation of Projection Pursuit (I failed to write my own version of PP as a standalone C++ program). As far as I know, R offers two interfaces/sintax for the ppr() function. The first one requieres a regression formula and a data frame. The other requieres X, a matrix with the explanatory variables, and Y,

Dear R users, I am looking for the source code of the implementation of ppr (Projection Pursuit Regression) in R. It will be great if citations of the source papers on which the implementation is based, are also provided. Thank you, Arvind Iyer, Grad student, Deptt. of Biomedical Engineering Viterbi School of Engineering University of Southern California, Los Angeles [[alternative HTML

Dear R listers -- I have several questions about using the ppr command in the modreg module. I discovered -- quite by accident -- that if I re-order the data, I obtain different results. The output below shows what I mean. I have two datasets (dataset1 and dataset2) that are identical (tested using proc compare in SAS) except for the fact that the records are in different order. Below I have

Folks, The projection pursuit regression function in the base R seems to crash when the optimization level is set to zero, i.e. the initial ridge terms are accepted without refitting. I encountered this problem in an out-of-sample prediction exercise using predict. But further investigation suggests the issue is with the ppr fit and predict just sppeds up the crash. The other optlevels seem to be

The following are now on CRAN: KernSmooth: version 2.2 of the code for Wand & Jones book on kernel smoothing. logspline: spline fits to log denisites, with automatic choice of smoothing. ppr: projection pursuit regression. rpart: recursive partitioning (CART-like) VR: Venables & Ripley libraries 5.3pl021 for 0.62.1 and in the devel section tree: a clone

The following are now on CRAN: KernSmooth: version 2.2 of the code for Wand & Jones book on kernel smoothing. logspline: spline fits to log denisites, with automatic choice of smoothing. ppr: projection pursuit regression. rpart: recursive partitioning (CART-like) VR: Venables & Ripley libraries 5.3pl021 for 0.62.1 and in the devel section tree: a clone

I used the supersmoother function in the modreg package as follows: super <- supsmu(ilogemp,award) Then I decided that I might want additional explanatory variables (other than ilogemp) in my model. The ppr function in modreg seemed a logical extension of supsmu from univariate to multidimensional explanatory variables. As a "check" I ran the following: pprest <-

>>>>> "Jose" == Jose Ramon G Albert <toots at info.com.ph> writes: Jose> I have just downloaded this freeware version of R (which seems Jose> to be a clone of S). I was wondering if anyone knows where I Jose> could obtain R or S functions which provide nonparametric Jose> regression curves, e.g. kernel estimators or smoothing Jose> splines.

I now have versions of code that is destined (I believe) for 0.63 which is in a suitable state for comment. The files are at ftp://ftp.stats.ox.ac.uk/pub/R (Our www server is being moved, so may be intermittently down, but this ftp server should be stable.) All are R packages, for the moment for personal use only (no re-distribution). Use with 0.62.3 or 0.63 (although I am aware of some

Hello everybody, I'm a new user of R and I'm working right now with the ACE function from the acepack library. I Have a question: Is there a way to predict new responses using ACE? What I mean is doing something similar to the following code that uses PPR (Projection Pursuit Regression): library(MASS) x <- runif(20, 0, 1) xnew <- runif(2000, 0, 1) y <- sin(x) a <- ppr(x, y,

Full_Name: Hugh Miller Version: 2.8.1 OS: XP Submission from: (NULL) (128.250.24.101) Hi there, I've been looking at approaches that use the projection pursuit regression function fairly (ppr) heavily, and have discovered that it crashes my R system on occasion. It only happens with the inputs are pathological in some way I don't understand. I have pasted such an example below. Any

Hello, Just a quick question about ppr in library modreg. I have looked at Ripley and Venables 2002 and it says that projection pursuit works "by projecting X in M carefully chosen directions" I want to know how it choses the directions? I presume it moves around the high-dimensional space of unit vectors finding ones that separate the response variables, but how. I looked at the

Dear all, I have been doing tests using SVM, random forests and PPR. The data is from a data stream (that is, the data for training and for test is always increasing / changing). With SVM and random forests everything is ok, but with ppr there are situations where it crashes. For the examples I have used I noticed that if one of the variables has just one value (it can happen), it crashes for

Hi, I've noted that not all `predict' methods are hidden in the namespace: > methods("predict") [1] predict.ar* predict.Arima* [3] predict.arima0* predict.glm [5] predict.HoltWinters* predict.lm [7] predict.loess* predict.mlm [9] predict.nls* predict.poly [11] predict.ppr* predict.prcomp* [13]

Sorry about the lack of detail. I am running R v.1.2.2. I can recast my question (which I think I have partially answered) more succinctly as follows: 1. This seems to work (note that group takes values 1,2,3,4, or 5): my.newfun <- function(x) myfile <- lm(award ~ ilogemp + ilogage, x) test.by <- by(wintemp, as.factor(wintemp$group), my.newfun) 2. This does not work (leaving aside

supsmu(periodic=TRUE) can crash R by reading before start of array. To reproduce: set.seed(1) xx <- runif(29000) yy <- rnorm(29000) span <- 0.49 i <- 1 while(i < 200){ cat(i,"\n") int <- supsmu(xx,yy,periodic=T,span=span) i <-i+1 } results in: 1 2 3 4 5 6 7 8 9 Program received signal SIGSEGV,

Dear R listers -- The program below does the following tasks: 1. It creates a file (wintemp4) that is a subset of alldata4 consisting of "winner" records in 50 industry groups (about 5400 obs); 2. It defines a function (myppr1) that runs the ppr function in modreg once to generate goodness of fit (sum of squared errors) measures by number of terms included in model and then reruns

Dear R list: I want to make separate estimates for each level of the variable "group." After consulting many sources I am stumped as to why the following does not work: > wintemp <- subset(alltemp, winner==1) > my.ppr <- function(x) + { + if(nrow(x) >= 50) { + pprfile <- ppr(award~ilogemp, data=x,nterms=5,max.terms=10,optlevel=3) + summary(pprfile) +

Hopefully the last patch from me to UTF-8 issues. Metaflac can now print all console supported characters from tags on the screen. It also fixes metaflac to be able to import its own exports back without non-ascii characters getting mutilated. And --no-utf8-convert now works properly with import and export commands. I updated my Windows binary archive with these changes for any interested