similar to: mutlivariate normal and t distributions

[Hope this is the right list where to send...] An attempt to update package 'mnormt' involves the addition of a small new function called 'pd.solve'. When I come to the package checking stage, an error occurs in parsing pd.solve.Rd. The full transcript of the outcome is copied below (it includes details on my installation) but the critical point is where the \examples{} section

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

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

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 Prof Azzalini, You have an interesting example of recursive use of uniroot(). [re-cited at the end] However, note that R currently has the same problem as S-plus: Uniroot() doesn't work reliably, recursively. When you found it to be better, you were just lucky. The relevant file in R's source, src/main/optimize.c says /* WARNING : As things stand, these routines should

# 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

Since I have updated my Linux system, and consequently re-installed R, I am unable to see HTML documentation which I used to access via a web browser. To be more specific, my R installation is declared to be here: > R RHOME /usr/local/lib/R However, if I look at a location such as /usr/local/lib/R/library/base/html this contains only two entries File: 00Index.html File: R.css

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:

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

On 17/11/2017 10:47 AM, Adelchi Azzalini wrote: > Since I have updated my Linux system, and consequently re-installed R, I > am unable to see HTML documentation which I used to access via a web > browser. To be more specific, my R installation is declared to be here: > > > R RHOME > /usr/local/lib/R > > However, if I look at a location such as >

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

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, there. Does anyone know the R function for calculating the cdf of bivariate normal distribution function? Thanks. Yulei [[alternative HTML version deleted]]

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",

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

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 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]]

Hello... Is it possible to use "density" or another kernel density estimator to identify the mode of a distribution? When I use 'density', the resulting density plot of my data is much cleaner than the original noisy histogram, and I can clearly see the signal that I am interested in. E.g., suppose my data is actually drawn from two or more normal (or other)