similar to: Constraints in projection pursuit regression

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,

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

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

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

Hi, I would appreciate if someone could help me on track with this problem. I want to compute some parameters from a system of equations given a number of sample observations. The system looks like this: sum_i( A+b_i>0 & A+b_i>C+d_i) = x sum_i( C+d_i>0 & C+d_i>A+b_i) = y sum_i( exp(E+f_i) * ( A+b_i>0 & A+b_i>C+d_i) = z A, C, E are free variables while the other

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

Dear R-developers, I am trying to experiment with projection pursuit (PP), and different indexes for the same, especially using the tourr package. However, I've noticed that a PP index in the said package is only a function of the projected data. Could I modify the function so that the index sees the projection bases instead (or in addition to the data) ? Alternatively, if some other package

Hello, I am using projection pursuit regression parameters selection. Does anyone has experience on the range to test for df parameter (spline kernel) and gcvpen (gcvspline kernel)? I don't find any information about this. Thanks in advance. Joao Moreira [[alternative HTML version deleted]]

The pursuit projection packages ppr is an excellent contribution to R. It is great for one-to-three ridge fits, often somewhat intuitive, and for multi-ridge fits, where it at least describes a lot of variance. Like many folk, I need to report the fits obtained from ppr to the greater, outside, non-R world. It is fairly obvious how to use the terms alpha and beta to report on

Hi there, I'm quite new to R (and statistics), and I like it (both)! But I'm a bit lost in all these packages, so could someone please give me a hint whether there exists a package for fitting exponential curves (of the type t --> \sum_i a_i \exp( - b_i t)) on a noisy signal? In fact monoexponential decay + polynomial growth is what I'd like to try. Thanks in advance,

>>>>> "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.

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,

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

Dear helpers Does R have a package that performs projection pursuit density estimation? Or anyone knows code in Matlab or C for example to do this? Thank you all Luis

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

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

I always kind of liked this title (the title of Davids super Rails presentation), but then I saw this quote today: "The pursuit of happiness is a most ridiculous phrase; if you pursue happiness you''ll never find it." by C.P Snow I''d have to agree with this quote. It''s the same as love... go looking for it, and you''ll never find it. Soooo... David,

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions

Dear all, Given a LME model (following the notation of Pinheiro and Bates 2000) y_i = X_i*beta + Z_i*b_i + e_i, is it possible to extract the variance-covariance matrix for the estimated beta_i hat and b_i hat from the lme fitted object? The reason for needing this is because I want to have interval prediction on the predicted values (at level = 0:1). The "predict.lme" seems to