similar to: AIC and BIC from arima()

Hi all, I obtained negative values for AIC and BIC criteria for a particular model that I have developped... I don't remember to have negative values for these crietria for others applications, so I am a little suprised... Could anyone tell me if something is wrong or his conclusion concerning my model? Best regards, Olivier.

hello, I have a question about making n step ahead forecasts in cases where test and validation sets are availiable. For instance, I would like to make one step ahead forecasts on the WWWusage data so I hold out the last 10 observations as the validation set and fit an ARIMA model on the first 90 observations. I then use a for loop to sequentially add 9 of the holdout observations to make 1

Dear R Users: I am trying to compare several structures of the within-patient covariance such as unstructured, Autoregressive, and spatial by using the MIXED effects model. Can AIC, BIC be negative ? If yes, then in what situations they may be negative. Thanks a lot. [[alternative HTML version deleted]]

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

Hello, Is there any software project that uses R to do log file analisys? thanks gabi

Dear R-help, I am using R-3.3.2 on Windows 10. As per my previous post today, I teach on a course which has 4 computer practical sessions related to the development and validation of clinical prediction models. These are currently written for Stata and I am in the process of writing them for use in R too (as I far prefer R to Stata!) Part of the practical requires the student to fit a flexible

Is AIC or BIC available when using the nnet package? Thank you Paul Green

Hi All, How can extract AIC,BIC from a fitted Garch model? -- SUMANTA BASAK. [[alternative HTML version deleted]]

Hello I'm with a doubt using BIC and AIC. I want to know if both of then are a way to steem the best model to use. How i know which of then to choose? Talita Perciano Costa Leite Graduanda em Ci??ncia da Computa????o Universidade Federal de Alagoas - UFAL Departamento de Tecnologia da Informa????o - TCI Constru????o de Conhecimento por Agrupamento de Dados - CoCADa

Dear all, I am trying to model number of samples from a given series. The series are modelled according Gamma function. In order to estimate the # samples, I use BIC/AIC with MLE (computed from dgamma function). Here is the code I have. __BEGIN__ mlogl <- function( x_func, theta_func, samp) { # computing log_likelihood return( - sum(dgamma(samp, shape = x_func, scale=theta_func, log

Hello everybody, Please help with connecting the AIC and BIC numbers printed by summary.gls to the logLik number. 1. is the logLik number the true ML or density scaling constants have been omitted? 2. what is the formula for calculating the AIC and BIC from logLik (and how can I see it)? I tried printing summary.gls but it says object not found. Thank you very much. Stephen [[alternative

Hi all, First: I have a small line of code I'm applying to a variable which will be placed in a matrix table for latex output of accuracy measures: acc.aarima <- signif(accuracy(forecast(auto.arima(tix_ts, stepwise=FALSE), h=365)), digits=3). The time series referred to is univariate (daily counts from 12-10-2010 until 5-8-2010 (so not 2 full periods of data)), and I'm working on

Dear All 1) I'm fitting spatial CAR models using R2Winbugs and although everything seems to go reasonably well (or I think so) the next message appears from WINBUGS 1.4 window: gen.inits() Command #Bugs: gen.inits cannot be executed (is greyed out) The question is if this message means that something is wrong and the results are consequently wrong, or Can I assume it as a simple warning

Hi, I'm slowly working through Tsay's "Analysis of Financial Time Series" 3rd ed. ?I'm trying to replicate Table 2.1 on p.47, which gives PACF, AIC, and BIC for the monthly simple returns of the CRSP value-weighted index. The data: http://faculty.chicagobooth.edu/ruey.tsay/teaching/fts3/m-ibm3dx2608.txt > da <-

Hi all, I am having issues comparing models with lmer. As an example, when I run the code below the model summaries (AIC, BIC, loglik) differ between the summary() and anova() commands. Can anyone clear up what's wrong? Thank you! Darren Ward library(lme4) data(sleepstudy) fm1<-lmer(Reaction ~ Days + (1|Subject), sleepstudy) summary(fm1) fm2<-lmer(Reaction ~ Days +

R 2.3.0, all packages up to date Linux, SuSE 10.0 Hi I want to calculate AIC or BIC from several results from mle calculation. I found the AIC function, but it does not seem to work with objects of class mle - If I execute the following: ml1 <- mle(...) AIC(ml1) I get the following error messale: Error in logLik(object) : no applicable method for "logLik" Therefore I am using the

Hi all, Could anybody please help me understand AIC and BIC and especially why do they make sense? Furthermore, I am trying to devise a new metric related to the model selection in the financial asset management industry. As you know the industry uses Sharpe Ratio as the main performance benchmark, which is the annualized mean of returns divided by the annualized standard deviation of returns.

Dear R-users, I use glm() to do logistic regression and use stepAIC() to do stepwise model selection. The common AIC value comes out is about 100, a good fit is as low as around 70. But for some model, the AIC went to extreme values like 1000. When I check the P-values, All the independent variables (about 30 of them) included in the equation are very significant, which is impossible, because we

There has been a few questions on the subject lately. Is there any book on the subject, if possible with a computer processing flavor, that you would highly recommend? Many thanks in advance, -- Jean-Luc

Buenos días, Os cuento: Cargo la librería "Forecast" y ejecuto su función Arima(...) sobre una serie temporal: mimodelo <- Arima(miST$miserie, ...); Ahora si ejecuto las siguientes sentencias, voy obteniendo los resultados contenidos en "mimodelo", pero algunos de ellos no sé lo que son: mimodelo[[1]] obtengo los coeficientes del modelo ARIMA mimodelo[[2]] obtengo el