similar to: density estimation from interval-censored data

I am making my first attempts at using some Fortran code with R, and so far it's going OK. To print from my Fortran programs, it seems I need subroutines dblepr, realpr and intpr. From the excellent "Writing R Extensions" document: "Three subroutines are provided to ease the output of information from FORTRAN code. subroutine dblepr(label, nchar, data, ndata)

Is there a straightforward way to convert character information from decimal representation to the octal one used by R? I'd like something like a function ascii(number,base=10), such that > ascii(91) [1] "\133" I can easily do the mapping from 91 to 133, but what is a good way to operate on 133 to deliver "\133"? Would a lookup table be a better solution? David

Hi David, I hope all is well w/ you. Co-operation beetwen R and Ox would be most welcome. A possible problem lies in the fact that, although free for academic use, Ox is not open source. I too have been using Ox for computer-intensive programming. I also code some of my programs in C, but I always try Ox first, and in most cases it is efficient enough. For details, see

Is there some neat way of storing the printed representation of an object as a character string? I can see how it could be done via disk using sink() and then scan(), but that's ugly. Something like Lisp-Stat's "with-output-to-string" macro perhaps? David Firth Phone +44 1865 278544 Nuffield College Fax +44 1865 278621 Oxford OX1

I am having a little problem in relation to sending output to the R Console window, when a function is called by pushing a button in a Tk window. Actually there seem to be two components to the problem: -- the R prompt does not re-appear until <return> is pressed -- text output to the R Console window *disappears* after <return> is pressed The following, in R 1.1.1 under

I suppose this could be described as a feature (it seems to be similar in S-Plus), but it looks to me more like a bug. Why can't the assignment below to a row of "emptyframe" (or "anotherframe") be made? This with R --vanilla (version info below). Regards -- David David Firth Phone +44 1865 278544 Nuffield College Fax +44 1865

> ---------------------------------------------------------------------- > > Message: 1 > Date: Wed, 9 Jul 2003 10:53:27 +0100 > From: David Firth <david.firth at nuffield.oxford.ac.uk> > Subject: [R] packaged datasets in .csv format > To: r-help at stat.math.ethz.ch > Message-ID: > <307D34CE-B1F3-11D7-A8D2-0050E4C03977 at nuffield.oxford.ac.uk> >

Q: Is there an easy way to plot log histograms? The command "hist" draws a standard histogram with observed frequency on the y-axis. I would like to be able to draw the log frequency on the y-axis, but I can't see whether I can use "hist" for this. The purpose of such a plot is to see whether data are normal or Laplace or something else. - and the same question for

This is some kind of follow-up to my previous posts. I have further improved the speed of my program 6 times by eliminating all the apply(). It turns out that apply is slow, is slower than direct loop, it is an order slower than a matrix operation alternative. Here is one example. The first apply version runs 19 seconds, the second loop version runs 13 seconds, the third matrix version runs 1

Full_Name: Emmanuel Charpentier Version: 1.2.0 OS: Linux 2.2.18 (Debian 2.2) Submission from: (NULL) (193.251.31.31) When trying "mass" installation of available packages, the package logspline does not compile, at least when installed through install.packages("logspline"). It seems that it is a small bug, such a missing #include <math.h> or -libm linking switch ?

Hi all, I've successfully compiled R-1.2 on a Linux box (Mandrake 7.1). However, when I installed packages from sources, I run into problems with the packages logspline and tseries. The error messages are as follows. Can anyone help? The compiler is gcc 2.95.3, if that helps. Andy ================================================ Installing source package `logspline' ... libs gcc

Dear R-list, I have made a simple kernel density estimation by x <- c(2,1,3,2,3,0,4,5,10,11,12,11,10) kde <- density(x,n=100) Now I would like to know the estimated probability that a new observation falls into the interval 0<x<3. How can I integrate over the corresponding interval? In several R-packages for kernel density estimation I did not found a corresponding function. I

I've been trying to figure this out for a while, but my knowledge of R is obviously still too limited. The context is as follows: I have some time series, and I would like to estimate their densities, and then use the actual densities in a monte carlo simulation. Now, I can easily estimate the density using density(); I can write a random number generator to fit an arbitrary density

Full_Name: David Firth Version: 1.2.3 OS: Windows NT4 Submission from: (NULL) (163.1.103.107) Two things that look like bugs: 1. RCMD INSTALL does not seem to cope with quotes in the Windows path. With my path set to PATH="C:\R\Rtools";"C:\R\rw1023\bin";C:\Perl\bin\;C:\WINNT\system32;C:\WINNT;C:\ mingw2\bin I get, for example, C:\R>RCMD INSTALL boot RCMD

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

Dear List, I have a simple model as follows: x <- rnorm(500) library(logspline) fit <- logspline(x) n <- 1000000 y <- replicate(n, sum(rlogspline(rpois(1,10), fit))) # last line The problem I keep getting is R crashes when doing the last line. It seems to be fine if n is small, but not if n is 1000000. The message I keep getting is: "R for Windows GUI front-end has

Hi interested readers, I have a function that creates several functions within a loop and I would like them to be returned for further use as follows: Main.Function(df,...){ # df is a multivariate data funcList<-list(NULL) for (i in 1:ncol(df)){ temp<-logspline(df[,i],...) # logspline density estimate funcList[[i]]<-function(x){expression(temp,x)} } return(funcList) } I have tried

Hi, I got two questions and would really appreciate any help from here. First, is the penalized maximum likelihood estimation(Firth Type Estimation) only fit for binary response (0,1 or TRUE, FALSE)? Can it be applied to multinomial logistic regression? If yes, what's the formula for LL and U(beta_i)? Can someone point me to the right reference? Second, when I used *logistf *on a dataset with

Hey, Does anyone know of a package in R that provides univariate kernel density estimation with boundary correction ? or how to easily extend an existing bivariate kernel density estimation function (e.g. lambdahat in the spatialkernel package) with boundary corrections to allow univariate density estimation? Thanks a lot, Steve B. -- View this message in context: