similar to: using kernel density estimates to infer mode of distribution

Hi.... I am assembling an R package (under Windows XP, R v.2.3) and have a very basic question. Running R CMD build does not generate a pdf. R CMD check generates a .dvi, but I cannot figure out how to automatically create the pdf. I thought from reading the "writing r extensions' manual that R CMD build was supposed to generate the pdf. I am having no success using various

Hello. I am creating an R package that I'd like to submit to CRAN (OS Windows XP). How do I distinguish among 'public' functions, e.g., those that are intended to be called by users of the package and for which I am providing documentation & examples, and 'private' functions, which are used internally by the 'public' functions, but for which I do not wish to

This might be of interest: http://math.usu.edu/~minnotte/research/software/modetree.r (I was not able to get to the link, but google has a cached version.) Prof. Marron's SiZer maps may also be of interest, but AFAIK the code is in Matlab only. Andy From: Dan Rabosky > > > Hello... > > Is it possible to use "density" or another kernel density > estimator to

Hello... I would like to use R for 'pipelining' data among several programs. I'm wondering how I can use R to call another program, feed that program a set of parameters, and retrieve the output. E.g., I have an executable that, when opened, prompts the user to enter a set of parameters. The program then executes & prompts the user for the name of an output file. I need to

Hi, I've estimated a simple kernel density of a univariate variable with density(), but after I would like to find out the CDF at specific values. How can I do it? thanks for your help, with it I am very close to finish my first little bit more serious work in R, Viktor

I have written the following function to calculate the weighted mean difference for univariate data (see http://www.xycoon.com/gini_mean_difference.htm for a related formula). Unsurprisingly, the function is slow (compared to sd or mad) for long vectors. I wonder if there's a way to make the function faster, short of creating an external C function. Thanks very much for your advice. gmd

Hi all, I'd like to quantize a variable to map it into a limited set of integers for use with a colormap. "image" and filled.contour" do this mapping inside somewhere, but I'd like to choose the colors for plotting a set of polygons. Is there a pre-existing function that does something like this well? i.e., is capable of using 'breaks'?

Hello, How can I obtain the likelihood ratio of a Poisson regression model? Regards. _____________________________________________ dr. Marziliano Ciro Facolta' di Economia Universita' degli Studi di L'Aquila p.zza del Santuario, 19 67040 Roio Poggio, L'Aquila tel.: 0862 434836 fax: 0862 434803

Dear list, Does anyone know how to fit the power law distribution? I have the empirical distribution and would like to check whether it fits the power law (with the power estimated from the data). Any hints are appreciated. Tanks a lot! Galina [[alternative HTML version deleted]]

Hi R-users,  I have a data from multi skew t and would like to transform each of the data to uniform data.  I tried using 'pmst' but only got one output:   > rr1 <- as.vector(r1);rr1  [1]  0.7207582  5.2250906  1.7422237  0.5677233  0.7473555 -0.6020626 -2.1947872 -1.1128313 -0.6587316 -1.1409261     > pmst(rr1, xi=rep(0,10), Omega=diag(10), alpha=rep(1,10), df=5) [1] 3.676525e-09

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Caio Lucidius > Naberezny Azevedo > Sent: 01 September 2005 12:09 > To: Help mailing list - R > Subject: [R] Multivariate Skew Normal distribution > > > Hi all, > > Could anyone tell me if there is any package (or function)

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

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

I am using the MASS library function fitdistr(x, dpois, list(lambda=2)) but I get Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : Function cannot be evaluated at initial parameters In addition: There were 50 or more warnings (use warnings() to see the first 50) and all the first 50 warnings say 1: non-integer x = 1.452222 etc Can anyone tell me what I am doing

Hello! I am writing some numbers and character vectors to an ascii file and would like to get rid of [1] ... as shown bellow (a dummy example) R> runif(20) [1] 0.653574 0.164053 0.036031 0.127208 0.134274 0.103252 0.506480 0.547759 [9] 0.912421 0.584382 0.987208 0.996846 0.666760 0.053637 0.327590 0.370737 [17] 0.505706 0.412316 0.887421 0.812151 I have managed to work up to remove quotes

I have justed upgraded R v1.7.0 on Windows NT 4 and have installed the latest RMySQL (version 0.5-1)and DBI (version 0.1-5) packages. When I issue the following commands (tactfully adjusted) R just crashes and disappears, any ideas? require(RMySQL) m <- dbDriver("MySQL") con <- dbConnect(m, dbname="xxx", user="xxx", password="xxx",

I am learning glm function, but how do you fit data using exponential distribution with glm? In the help file, under "Family Objects for Models", no ready made option seems available for the distribution as well as for other distributions satisfying GLM requirements not listed there.

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

Hello, I am interested in estimating this type of random effects panel: y_it = x'_it * beta + u_it + e_it u_it = rho * u_it-1 + d_it rho belongs to (-1, 1) where: u and e are independently normally zero-mean distributed. d is also independently normally zero-mean distributed. So, I want random effects for group i to be correlated in t, following an AR(1) process. Any idea of how

Hi. I'm trying to take a data set with two independent and one dependent variable and enter a x,y value to predict the dependent with a nonparametric technique. I've been using interpp in the akima package, (windows xp, R 2.4.1), but get values that are orders of magnitude off when the predictors are slightly out of the range of the data set. Can you recommend a function for me?