similar to: sm.regression

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 have been using sm.binomial() in the Bowman and Azzalini's sm package to smooth and plot estimated probabilities as a function of a covariate. I am concerned about my choice of bandwidth, and I was hoping there was another method available in some other package, perhaps with an automatic choice of smoothing parameter. Does anyone know of one? Thanks in advance. Tom Richards

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

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

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 all, Is there a way to plot / calculate pointwise confidence bands or interval values for a non parametric regression like sm.regression? Thank you in advance. Regards, Martin

Hi R-Users, I am working with sm.ancova (in the package sm) and I have two problems with the graph, which is automatically generated when sm.ancova() is run. 1-Besides of the fitted lines, the observed data appeared automatically in the graph. I prefer that only fitted lines appear. I check the sm.options, but I could not find the way that the observed data do not appear in the graph. 2-I

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

Hi, there: It's my first time to post question in this forum, so thanks for your tolerance if my question is too naive. I am using a nonparametric smoothing procedure in sm package to generate smoothed survival curves for continuous covariate. I want to truncate the suvival curve and only display the part with covariate value between 0 and 7. The following is the code I wrote:

I have spatial data in 2 dimensions - say (x,y). The correlation between x and y is fairly substantial. My goal is to use a non-parametric approach to estimate the multivariate density describing the spatial locations. Ultimately, I would like to use this estimated density to determine the area associated with a 95% probability contour for the data. Given the strong correlation between x and

The sm package (by Adrian Bowman and Adelchi Azzalini) implements a variety of nonparametric smoothing techniques, centred on nonparametric regression for one or two covariates and density estimation for up to three variables. A new version of the package is now available on CRAN. In an earlier unannounced version (2.1), a variety of methods of bandwidth selection were added, with default

The sm package (by Adrian Bowman and Adelchi Azzalini) implements a variety of nonparametric smoothing techniques, centred on nonparametric regression for one or two covariates and density estimation for up to three variables. A new version of the package is now available on CRAN. In an earlier unannounced version (2.1), a variety of methods of bandwidth selection were added, with default

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,

Hello to everyone I''ve downloaded the version sm2 for smoothing methods and when I try the simple code y <- rnorm(50) sm.density(y, model = "Normal") I get the error message Error in if (any(omit)) { : missing value where logical needed I''m running R 1.1.1. on windows 98 Anybody can help? -------------- next part -------------- An HTML attachment was

Dear all, Does anybody know how to get more evaluation points in performing Nonparametric analysis of repeated measurements data with "sm" library. The following command gives the estimation for 50 points, by I would like to increase to 100 points But I do not know how to do that. library(sm) provide.data(citrate, options=list(describe=FALSE)) provide.data(dogs,

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 am running the sm.density.compare function amd I am getting the following error:   my code is  > sm.density.compare(LBSTRESN,COHORT,xlab="Units = umol/L"subset = LBTEST=="Creatinine")   Error in if (from == to) rep.int(from, length.out) else as.vector(c(from,  :   missing value where TRUE/FALSE needed   I do not understand the error and I have had no help when searching

Hi There I have a lm object with 4 parameters and I want to test wether 2 parameters are equal using a Wald test (basically b1=b2 or b1-b2 =0). In the help file from R it says that under ANOVA the optional arguments " Terms" or "L" test whether a linear combination is equal to 0. I tried; >anova(m1, Terms = Beta1-Beta2=0) but I get the error: Object " Beta1"

I just heard a talk about a semi-parametric model. I was quite excited by the idea. This model is fitted y= xB + g(z) + e where x is a data matrix, B a column vector, z is another data matrix, and g is a smooth model fitted by a Kernel Smoothing regression (I got the idea any smoother would do as well). The speaker said that when z is considered as a "control" variable, and there is