search for: distributions

...mber of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear and additive models, IRLS and vector smoothing. It is a large R package that fits many models and distributions, usually by maximum likelihood estimation. See below for a partial listing, or better, http://www.stat.auckland.ac.nz/~yee/VGAM/doc/VGAMrefcard.pdf. Some broad categories are categorical data analysis, LMS quantile regression, extreme value models, univariate and continuous distributions. The pack...

Dear community, Our team at IITH have been experimenting with loop-distribution pass in LLVM. We see the following results on few benchmarks. clang -O3 -mllvm -enable-loop-distribute -Rpass=loop-distribute file.c clang -O3 -mllvm -enable-loop-distribute -Rpass-analysis=loop-distribute file.c TORCH

Hi, I'm dealing with wind data and I'd like to model their distribution in order to simulate data to fill-in missing values. Wind direction are typically following a vonmises distribution and wind speeds follow a weibull distribution. I'd like to build a joint distribution of directions and speeds as a VonMises-Weibull bivariate distribution. First is this a stupid question? I'm

> On Oct 10, 2016, at 2:50 PM, Hal Finkel <hfinkel at anl.gov> wrote: > > > From: "Dangeti Tharun kumar via llvm-dev" <llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org>> > To: llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org> > Cc: "Santanu Das" <cs15mtech11018 at iith.ac.in <mailto:cs15mtech11018 at

Dear Siegfried, I believe your boss is wrong saying that: >He also tried to explain me that the monthly means >(based on the daily measurements) must follow a >log-normal distribution too then over the course of a year. every statistician know that increasing the sample size the sample distribution of the mean is proxy to a gaussian distribution (Central Limit Theorem) independently

The following gives two functions for producing distribution graphs: distribution-graph produces a single graph, and multiple.distribution.graph produces a number of graphs side by side. Regards, Tore Wentzel-Larsen statistician Centre for Clinical research Armauer Hansen house Haukeland University Hospital N-5021 Bergen tlf +47 55 97 55 39 (a) faks +47 55 97 60 88 (a) email

Hello, I am trying to compute the density of a variable k that is either (1) Normally distributed; (2) Log-Normally distributed; or (3) follows proportional error distribution. I tried to search R-help and the answer for normal distribution was easy to find (please see 1c). I am not sure if my formula for dlnorm is correct (please see 2c below)? I really don't know what function to use for the

I wish to generate a vector of uniformly distributed data with a defined correlation to another vector The only function I have been able to find doing something similar is corgen from the library ecodist. The following code generates data with the desired correlation to the vector x but the resulting vector y is normal and not uniform distributed library(ecodist) x <- runif(10^5) y

Hello, I have a bunch of files containing 300 data points each with values from 0 to 1 which also sum to 1 (I don't think the last element is relevant though). In addition, each data point is annotated as an "a" or a "b". I would like to know in which files (if any) the data is uniformly distributed. I used Google and found out that a Kolmogorov-Smirnov or a Chi-square

...to work - I want to put my results from the function size() into an array.At the moment I keep getting the error message:Error in res[distribution, test, samplesize] <- results : subscript out of boundsCan anyone tell me where I'm going wrong, please?!size.power.test<-function(){k<-1000distributions<-c("Normal","Uniform")tests<-c("t","Wilcoxon")samplesizes<-c(10,30,40)res<-array(0,c(length(distributions),length(tests),length(samplesizes)))dimnames(res)<-list(distributions,tests,samplesizes)for(distribution in distributions){for(test in tes...

...olatility model. For example, consider this paper: http://www.math.chalmers.se/~palbin/mattiasviktor.pdf On page 50 they are showing the hyperbolical distribution with different volatility models, how did they do this? Also, I do not understand table 6.2 on page 49: If they have estimated several distributions over the time, they have lots of estimates, but they just show one distribution? I mean, where does it come from? The 3d picture clearly differnt distributions over time, so they have estimated the distribution after 5 days (page 48), but in the table is just one specific distribution with specific...

Hi Tanya, The first thing you need to understand is that there are multiple make targets to support this feature. I'll briefly describe each here so you have an overview and then delve into the details later. * distdir - builds the distribution directory from which the distribution will be packaged * dist - builds each of the distribution tarballs (tar.gz,

I've built R functions to ``effect'' a particular distribution, and would like to find out if that distribution is already ``known'' by an existing name. (I.e. suppose it were called the ``Melvin'' distribution --- I've built dmelvin, pmelvin, qmelvin, and rmelvin as it were, but I need a real name to substitute for melvin.) The distribution is really just a toy

Reid, Could you explain in detail what make dist does? :) I'd like to see how it can be integrated into the release process. Thanks, Tanya

...ph<-function(q, u, v, ...){ # q vector of quantiles as in function pf # u number of independent variables # v number of observations if (!v > u+1) stop("n must be greater than p+1") df1 <- u df2 <- v-u+1 pf(q*df2/(v*u), df1, df2, ...) } # compare Chi-squared and Hotelling T^2 distributions for a group member u<-3 v<-10 set.seed(1) mat<-matrix(rnorm(v*u), nrow=v, ncol=u) MD2<-mahalanobis(mat, center=colMeans(mat), cov=cov(mat)) d<-MD2[order(MD2)] # select a point midway between nearest and furthest from centroid dm<-d[length(d)/2] 1-ph(dm,u,v) # probability using...

----- Original Message ----- From: "Richard A. O'Keefe" <ok at cs.otago.ac.nz> To: <paul at datavore.com> Sent: Thursday, September 04, 2003 2:56 AM Subject: Re: [R] Overlaying graphs > I do not know how to overlay the curve graphic on top of hist graphic. > > Do you know about the "add=TRUE" option for plot()? > > I am hoping to show visually

Hi guys Given a exponential curve, is there any function on r that can generate exponential distributed random numbers? in General I want an function that can transform one probability distribution into another?? Regards ****************************************************************** Bander ************************************* [[alternative HTML version deleted]]

Ok I made Jarque-Bera test to the residuals (merv.reg$residual) library(tseries) jarque.bera.test(merv.reg$residual) X-squared = 1772.369, df = 2, p-value = < 2.2e-16 And I reject the null hypotesis (H0: merv.reg$residual are normally distributed) So I know that: 1 - merv.reg$residual aren't independently distributed (Box-Ljung test) 2 - merv.reg$residual aren't indentically

Hello, after some discussions with Martin Maechler and Josef Leydold (WU Wien), we have felt the need for some package that should allow for an object-orientated approach to distributions. Our small group at Bayreuth now has developed a package "distr" which tries to fill this gap, implementing distributions by means of S4--classes. A mother class "Distribution" is introduced with slots for a parameter and - most important - for the four constitutive methods...

I'm analizing the Argentina stock market (merv) I download the data from yahoo library(tseries) Argentina <- get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03", quote="Close") merv <- na.remove(log(Argentina)) I made the Augmented Dickey-Fuller test to analyse if merv have unit root: adf.test(merv,k=13) Dickey-Fuller = -1.4645,