similar to: Smoothing estimated probabilities

Has Bowman and Azzalini's sm library been ported to R yet (goes with Appied smoothing techniques for data analysis book)? I had a quick go but got tied up at a silly stage of ignorance caused I think (?hope) because I have never seriously used S+ at all. Rather than waste time, perhaps some kind soul has already done it. \John

Hello, I am a newbie to R and the subject of density estimation in two dimensions or more. I would like to have some advice concerning a comparison between the R packages for density estimation in bivariate or higher order problems; I mean explicitly the packages: 1) ash 2) KernSmooth 3) locfit 4) sm. My specific problem now is having a set of numerical pairs (x_i, y_i), arising from a

Hi all, I was looking for a non parametric survival analysis and I came up with the following sample from the web. However, I could not run it. Which library or function does "sm.regression" require? x <- runif(100,-2, 2) y <- x^2 + rnorm(50) sm.regression(x, y, h=0.5) Error: could not find function "sm.regression" Thanks Val [[alternative HTML version deleted]]

Dear R experts, density estimation on a 2 dimensional bounded domain --------------------------------------------------------------------- I am currently trying to estimate the probability density (PD) of cancers within the breast using the sm library with the routine sm.density Of course a practical PD must be limited by the curve of the breast outline. I don't have a clue after perusing

Dear R-Listers, I have spatial data on an equidistant rectangular grid, similar to topographic data. I know that there are quite a few R-packages or base functions that provide nice iso-contours plot, but I don't want a plot, just the smoothed isocontour line of ONE level (e.g. 10 mm). Data sets are large, so it would be preferable if the availability of regular grid data could be exploited,

The package "sm" was obtained twice, one using R's built-in updating of packages, the second directly. In both cases the USA-NC CRAN mirror was used. In both cases, loading the package under R 2.10.1 for Windows resulted in a 'package obsolete' kind of message. Switching the mirror to USA-CA-1 (Berkeley) got a good package that loaded without complaint. R version 2.10.1

I am working with a model that I have to estimate a nonparametric function. The model is partial linear i.e. Y=X$\beta$ + f(z) + $\epsilon$ I am using the ' double residual methods' Robinson (1988) Speckman (1988) where I estimate a nonparametric function for each of the parametric variables in terms of the nonparametric one i.e. X[,i]=g(Z)+ u this is done because I need the $E(

Hi, I'm using R 1001 for Windows NT and the sm library. I'm trying to create plots for my data set like Bowman and Azzalini have in Figure 1.8 (p. 9) of their book for my data (i.e. a contour plot for each group in my data set and its all plotted on 1 plot). The problem I'm having is that R is not drawing closed contour lines for each group. Sometimes it does; other times it

Is there code for bivariate kernel density estimation? For bivariate kernels there is kde2d in MASS kde2d.g in GRASS KernSur in GenKern (list probably incomplete) but none of them seems to accept a weight parameter (like density does since R 2.2.0) -- Erich Neuwirth, University of Vienna Faculty of Computer Science Computer Supported Didactics Working Group Visit our SunSITE at

Dear R-Users, I need to find a smooth function f() and coefficients a_i that give the best fit to y ~ a_0 + a_1*f(x_1) + a_2*f(x_2) Note that it is the same non-linear transformation f() that is applied to both x_1 and x_2. So my first question is how can I do it in R? A more general question is this: suppose I have a utility function U(a_i, f()), where f() is say a spline. Is there a general

Dear all, I'm trying to use the Pspline add-on package to fit a quintic spline (norder =3), but I keep running into a Singularity error. > traj.spl <- smooth.Pspline(time, x, norder=3 ) Error in smooth.Pspline(time, x, norder = 3) : Singularity error in solving equations > Playing around with the other parameters produces an "unused arguments" error: > traj.spl

I'm trying to figure out a good way to smooth binary data. The ideal approach appears to be the "sm.logit" function in library "sm", but I haven't had success with it. Below, is some code illustrating what I've come up with so far, but I'm hoping there is a better approach. I'm using R 1.2 (development) under Windows 98. -Bill library(MASS) data(birthwt)

Hi R Users, I am very new to R and would like to do something quick if possible, please help! Suppose I have a data set of y versus x, how can I generate a smoothing line of y versus x (for example, using loess) and at the same time, generate a pair of confidence intervals for the smoothing or mean plus/minus standard deviation? Yi Zhu Golder Associates Inc. USA

Full_Name: Ed Borasky Version: 1.8.1 OS: Windows XP Professional Submission from: (NULL) (208.252.96.195) R 1.8.1 seems to be running into a memory allocation problem in the "aggregate" function. I have a rather large dataset (14 columns by 223,000 rows -- almost 40 megabytes) and a script that performs some processing on it. The system is a 768 MB Pentium 4. Here's the console

I have been attempting to use Bowman & Azzalini kernel smoothing routines from the SM library to sketch confidence intervals for serially correlated longitudinal data. The rm procedure does not seem allow for cases where the variance is increasing with time. The envelope in this case is drawn with a constant width. Are there any macros around for smoothing that do this or have I

The following patch corrects a portablility issue when compiling openssh 6.0p1 on MirOS (aka. mirbsd). The issue is: sftp-server.c: In function `send_statvfs': sftp-server.c:510: error: request for member `val' in something not a structure or union sftp-server.c:510: error: request for member `val' in something not a structure or union The patch is: --- configure.ac.orig 2012-04-19

Hello all, I would like to use the Nadaraya-Watson estimator assuming a Gaussian kernel: So far I sued the library(sm) library(sm) x<-runif(5000) y<-rnorm(5000) plot(x,y,col='black') h1<-h.select(x,y,method='aicc') lines(ksmooth(x,y,bandwidth=h1)) which works fine. What if my data were clustered requiring a bandwidth that varies with x? How can I do that? Thanks in

Hi: I remember a post several days ago by Jon Baron, concerning the behavior of sum() when one sets na.rm=TRUE: the result will be a zero sum for a vector of all NA's, as here, for the second row: > ss<- data.frame(x=c(1,NA,3,4),y=c(2,NA,4,NA)) > ss x y 1 1 2 2 NA NA 3 3 4 4 4 NA > apply(ss,1,sum,na.rm=TRUE) 1 2 3 4 3 0 7 4 I am rather alarmed by that zero, because

Hello, I'm currently trying to find a method to interpolate or smooth data that represent a trajectory in space. For example, I have an ordered (=time) set of (x,y) tuples which constitute a path in a 2D space. Is there a way using R to interpolate between these points in a way similar to spline interpolation so that I get a smooth path in space? Greetings, Dieter -- Dieter Vanderelst

Hello: Can you tell me if there's a simple way to generate in R random vectors on the unit sphere in R^{n}? Additionally, are there references for this question? Thanks in advance. Tom -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or