similar to: ROC

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

Whether the two variables have the same units does not matter. Moreover, even if there were some way of converting cm to kg the correlation would still be the same because the correlation is invariant under unit conversion as it is invariant under multiplication of its arguments by a constant. As for your second question, the correlation estimator is a continuous function of each of the

Hello, I have a several time series, which I would like to check for their best fitted Arima model (I am checking for the lowest aic value). Which lets me raise two questions: 1) is there are more efficient way, than using 6 for-loops? 2) sometimes the system cannot calculate with given parameters - is there a more efficient solution than I found? I hope, you can help me to make this

Dear statistitians / R users, I was told to analyse the effects of the four factors using lm or update for aov. The following is the result from lm. As you can see that in the last few lines enclosed coefficients. 1)Does this mean I can write a formula, like yield =0.26 + 0.03*H + 1.48*T + 0.04*L + 0.004*C 2) in the two levels design, is there any difference between I use (-1, 1) to

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'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

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

Dear R Users, I am working for the United Nations to construct a complete life table from an abridged table. I want to use the code of Hydman Filter by Rob J Hydman but an error sentence always appears and it simply doesn't run-- source("C:/R/Jamie/HymanFilter.R") Error in .C("spline_coef", method = as.integer(method), n = nx, x = x, : C symbol

Can anyone please recommend a good site for crime related time series? Thanks! Erin mailto: hodgess at uhddx01.dt.uh.edu -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To:

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'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

>>>>> 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)

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]]

Hello After conducting a simulation R closes down reporting the problem I have listed below. These are extracted from the event viewer option under the control panel option in windows. This problem occurs quite often but is as far as I can tell fairly sparadic. I can run identicle programs which run successfully the first time but on the second run it does not (and vise-versa also).

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-helpers, I would appreicate if someone can help me on the transfer parameter in ARIMAX and also see what I am doing is correct. I am using ARIMAX with 2 Exogeneous Variables and 10 years data are as follows: DepVar Period, depVar, IndepVar1 Period, indepVar1, IndepVar2 Period, indepVar2 Jan 1998,708,Jan 1998,495,Jan 1998,245.490 Feb 1998,670,Feb 1998,421.25,Feb 1998,288.170 Mar

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

I've some demand data, for which I wish to judge what will be the direction in the forecast period (Up/Down). What will be a best ML method to do this? Currently I'm using the data given below - 9/4/2016 241 9/11/2016 233 9/18/2016 226 9/25/2016 282 10/2/2016 291 10/9/2016 282 10/16/2016 308 10/23/2016 291 10/30/2016 268 11/6/2016 262 11/13/2016 273 11/20/2016 262 11/27/2016 309 12/4/2016

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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