search for: hyndmans

Hello R help I have been trying to use Rob Hyndman's monotonically increasing spline function. But like another user or two seem have a problem with a missing DLL (namely "spline_coef"). None of the previous help postings seemed to have any solutions to this problem. As per a Ripley suggestion I have deleted all previous versions of R and reinstalled R 2.7.0 and the problem

I am trying to get a dll compiled for use with dyn.load. I use R.1.7.1 under Windows. I have tried the following trivial example based on the "Writing R extensions" manual. rtest.h -------- class X { public: X (); ~X (); void Give7(double*); }; class Y { public: Y (); ~Y (); }; rtest.cpp --------- #include <iostream.h> #include "rtest.h" static Y y;

I've been successfully using a dll via dyn.load() with R1.6.0 for Windows, but when I try it under R1.6.1 it manages to crash the program completely. Has there been a change in how R1.6.1 handles dynamic loading? I couldn't spot any such changes in the documentation. This problem occurred on two different machines, and both run the code under R1.6.0 without a problem. Rob Hyndman

The generic stats::median method is defined as median <- function (x, na.rm = FALSE) {UseMethod("median")} I suggest that this should become median <- function (x, na.rm = FALSE, ...) {UseMethod("median")} This would allow additional S3 methods to be developed with additional arguments. Currently I have to over-ride this generic definition in the demography

I'm not sure who is responsible for quantile(), but I assume they read this list. Ivan Frohne and I have produced a revision of the quantile.default() function which enables the computation of alternative sample quantile definitions. The code and .Rd file are attached. This enables the user to produce quantiles that are equivalent to those in various statistics package. There is a type

I'm trying to fit a mixed-effects model of the form Y = a + bX + cZ + e where X are fixed effects and Z are random. i.e., c is a vector of random coefficients with mean 0. There is no "grouping" variable such as would be used with longitudinal data. Can the nlme package handle this sort of thing? Although it is a simpler model than would be used with grouped data, I can't seem

>>>>> Martin Maechler <maechler at stat.math.ethz.ch> >>>>> on Mon, 27 Feb 2017 10:42:19 +0100 writes: >>>>> Rob J Hyndman <Rob.Hyndman at monash.edu> >>>>> on Wed, 15 Feb 2017 21:48:56 +1100 writes: >> The generic stats::median method is defined as median <- >> function (x, na.rm = FALSE)

>>>>> Rob J Hyndman <Rob.Hyndman at monash.edu> >>>>> on Wed, 15 Feb 2017 21:48:56 +1100 writes: > The generic stats::median method is defined as median <- > function (x, na.rm = FALSE) {UseMethod("median")} > I suggest that this should become median <- function (x, > na.rm = FALSE, ...)

Hi, can you advise me is there any ROC(Receiver Operating Characteristic)analysis program in R? Thanks, Dechao ===== Dechao Wang Tel: (44) 01223 719718 Mob: (44) 07729 411134 __________________________________________________ Everything you'll ever need on one web page from News and Sport to Email and Music Charts

Can we please have a ... argument in median() to make it possible to pass arguments to specific methods. _____________________________ Rob J Hyndman Professor of Statistics, Monash University Editor-in-Chief, International Journal of Forecasting http://www.robhyndman.info/ [[alternative HTML version deleted]]

For some reason, the help file on quantile() says "Missing values are ignored" in the description of the x argument. Yet this is only true if na.rm=TRUE. I suggest the help file is amended to remove the words "Missing values are ignored". Rob _____________________________ Rob J Hyndman Professor of Statistics, Monash University Editor-in-Chief, International Journal of

Hi all, I've been fighting with joining my samba server (debian) to my active directory domain for 4 days now. The problem here is that users in my active directory domain on windows machines are not able to browse my samba shares without being prompted for authentication. I can: - Join the domain from samba server using net ads - View list of tickets when brownsing window shares with

Dear R-Users, How can I compute simple moving averages from a time series in R? Note that I do not want to estimate a MA model, just compute the MA's given a lenght (as excel does). Thanks ________________________________________ Ricardo Gonçalves Silva, M. Sc. Apoio aos Processos de Modelagem Matemática Econometria & Inadimplência Serasa S.A. (11) - 6847-8889 ricardosilva@serasa.com.br

Has anyone got a function for smooth monotonic interpolation of a univariate function? I'm after something like the NAG function PCHIM which does monotonic Hermite interpolation. Alternatively, montononic cubic spline interpolation. Please reply directly. Rob Hyndman ___________________________________________________ Rob J Hyndman Associate Professor & Director of Consulting

dear members, I have a heteroscedastic time series which I want to transform to make it homoscedastic by a box cox transformation. I am using Otexts by RJ hyndman and George Athanopolous as my textbook. They discuss transformation and also say the fpp3 and the fable package automatically back transforms the point forecast. they also discuss the process which I find to be

v1.0 of the forecasting bundle of packages is now on CRAN and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman

Dear all, I'm looking for how can I estimate a linear model with ar(ma) errors : y(t)=a*X(t)+e(t) with P(B)e(t)=Q(B)u(t) where u is a white noise and P, Q are some polynomes. Could you help me ? Gr?gory Benmenzer

v1.0 of the forecasting bundle of packages is now on CRAN and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman

Hi, I am using the package "demography" from Rob Hyndman for the Lee-Carter-Model. It is an amazing powerful tool but I am struggling with one issue: *I want to compute different quintiles for the cumulative survival probability derived from the Lee-Carter-Forecast (e.g. the 50%-quintile, 75%-quintile and 99%-quintile) for the next 10 years. * I am sure the package possess this

The decompose() function truncates the seasonal component unnecessarily. I've modified the function to fix this problem, and also added the original data to the object returned (to enable better plotting). I've also modified the plot.decomposed.ts() function so that it plots the original data in the top panel rather than the reconstructed data. The difference between the two is that the