similar to: Newbie question about by() -- update

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) +

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

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 <-

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

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

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

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 list, I am a little confused as to when to use apply, sapply, tapply, vapply, replicate. I've encountered this several times, This is time, this is what I am working on, mat <- matrix(c(seq(from=1, to=10), rnorm(10)), ncol=2) a=1; b=5 newfun <- function(x, y, a, b) { x*y+a+b } sapply(i=1:10, newfun(x=mat[i, 1], y=mat[i, 2], a=a, b=b)) Error in match.fun(FUN) : argument

Hi all, The function call series genericForPrimitive -> .findBasicFuns -> .findAll happens 4400 times while the GenomicRanges package is loading. Each time .findAll follows a chain of environments to determine that the methods namespace is the only one that holds a variable called .BasicFunsList. This accounts for ~10% of package loading time. I'm sure there is some history to that

Doing it like this: genericForPrimitive <- function(f, where = topenv(parent.frame()), mustFind = TRUE) { ans = .BasicFunsList[[f]] ## this element may not exist (yet, during loading), dom't test null if(mustFind && identical(ans, FALSE)) stop(gettextf("methods may not be defined for primitive function %s in this version of R",

> > If you want to define an alias symbol "bar" to "foo" (which is an > extension you want to provide), one way is to create an object file that > defines "bar" and "__imp_bar" as aliases to "foo" and "__imp_foo", > respectively, and add that object file to the import library. As a result, > the import library file

I am running into a limitation of the grid::grid.newpage function, for which I would like to overwrite this function with a slightly modified one. Hopefully this is a temporary working solution until the package gets updated. I found a way to overwrite the function in the package:grid namespace. However, lattice imports grid rather than depending on it. So I need a way to overwrite this imported

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, I'm running samba 2.2.1a (RPM install) on RedHat 7.2 I've connected a Canon LBP-8 IV laserprinter to the network via a HP Jetdirect box. I've configured the printer within linux and printing goes fine. Now I'm trying to access the printer via samba from my win2k clients. Now, the problem is: When I'm trying to print to the printer (e.g. a testpage) I get the message

> > The header of libuser32b.a says that it defines MessageBoxB and > __imp_MessageBoxB, but the import library file in the archive actually > defines MessageBoxA (not B). So the archive file is broken. You may want to > fix the header and try again. Yes this is how I done the alias. If you consider this invalid then look at libuser32.a The header defines MessageBoxA and

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

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 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

Hello everyone, Reposting with after having applied the (minor) fixes suggested by Wei and Jan. Allen, if you can tell us what you think about this, or suggest someone else to ask some feedback to, if you''re no longer involved with VT-d, that would be great! :-) -- As already discussed here [1], dealing with IOMMU faults in interrupt context may cause nasty things to happen, up to

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,