similar to: Kernel smoothing

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

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

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

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

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, 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

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 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"

Try to fit a longitudinal dataset using generalized mixed effects models via the R function gamm() as follows: library(mgcv) gamm0.fit<- gamm(y ~ x+s(z,bs="cr"), random=list( x=~1, s(z,bs="cr")=~1 ), family = binomial, data =raw) the data is

AA> In 2008, I have spotted some errors in a package, one which is AA> likely to have many users (I am not one myself). The more serious AA> errors are in the documentation, since they lead to a completely AA> distorted interpretation of the outcome; in addition, there is (at AA> least) one programming error which produces some wrong AA> computations. A few weeks later, the

Dear R-help list members, I have created a package 'mnormt' with facilities for the multivariate normal and t distributions. The core part is simply an interface to Fortran routines by Alan Genz for computing the integral of two densities over rectangular regions, using an adaptive integration method. Other R functions compute densities and generate random numbers. The starting

Dear List-members, to download a file from the net, the function download.file(..) does the job. However, before embarking on the download, I would like to find out how large the file is. Is there a way to know it? Most easily, this question has been asked before, but I am new to the list. Regards, with thanks in advance, Adelchi Azzalini ---- Adelchi Azzalini <azzalini at

In the document "R Data Import/Export", section "Output to connections", there is the following portion of code: ## convert decimal point to comma in output, using a pipe (Unix) zz <- pipe(paste("sed s/\\./,/ >", "outfile"), "w") cat(format(round(rnorm(100), 4)), sep = "\n", file = zz) close(zz) ## now look at the output

There seems to exist peculiar cases where optim does not take care of constraints on the parameters to be optimized over. The call to optim is of the form opt <- optim(cp, fn=sn.dev, gr=sn.dev.gh, method="L-BFGS-B", lower=c(-Inf, 1e-10, -0.99527), upper=c( Inf, Inf, 0.99527), control=control, X=X, y=y, hessian=FALSE) The code has worked fine

Hi all, I've been using SAS proc mixed to fit linear mixed models and would like to be able to fit the same models in R. Two things in particular: 1) I have longitudinal data and wish to allow for different repeated measures covariance parameter estimates for different groups (men and women), each covariance matrix having the same structure. In proc mixed this would be done by specifying

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

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### Today I have installed R 1.02.1 on my MSW-95 laptop; it essentially worked, but thre are two

Dear R helpers, I need to find out maximized log likelihoods, parameters estimates and standard errors (in parentheses) of r largest-order statistics model, with different values of r by using the function rlarg.fit. I want to specify required number of order statistics to the model. I attached my data file with this mail.please help me. Ruposh --- r-help-request at stat.math.ethz.ch wrote:

If I define this function R> ask <- function (message = "Type in datum") eval(parse(prompt = paste(message, ": ", sep = ""))) the following is produced as expected on a Linux/debian machine R> ask("input") input: 3 [1] 3 R> ask("input") input: 3:6 [1] 3 4 5 6 R> ask("input") input: c(3,6) [1] 3 6 If I