similar to: Calculation of AIC BIC from mle

I would like to figure out the equations for calculating "AIC" in both "step() function" and "AIC () function". They are different. Then I just type "step" in the R console, and found the "AIC" used in "step() function" is "extractAIC". I went to the R help, and found: "The criterion used is AIC = - 2*log L + k *

R 2.3.0 Linux, SuSE 10.0 Hi I have two problems with mle - probably I am using it the wrong way so please let me know. I want to fit different distributions to an observed count of seeds and in the next step use AIC or BIC to identify the best distribution. But when I run the script below (which is part of my original script), I get one error message for the first call of mle: Error in

Hi I have the following formula (I hope it is clear - if no, I can try to do better the next time) h(x, a, b) = integral(0 to pi/2) ( ( integral(D/sin(alpha) to Inf) ( ( f(x, a, b) ) dx ) dalpha ) and I want to do an mle with it. I know how to use mle() and I also know about integrate(). My problem is to give the parameter values a and b to the

Hello, I'm a Korean researcher who have been started to learn the "R" package. I want to make gam model and AIC value of the model to compare several models. I did the GAM model, but there were error for AIC. SO, how can I do? pleas help me!!! I did like below; > a.fit <- gam(pi~ s(t1r), family = gaussian(link="log")) >

Hi, I have a problem in programming for bootstrapping. I don't know why it show the error message. Please see my code below: #'st' is my original dataset. #functions of 'fml.mlogl','pcopula.fam4','ltd','invltd' are already defined boot.OR<-function(data,i) { E=data[i,] ml1<-glm(c_VAsex90_bf ~ trt,family=binomial,data=E) ml2<-glm(c_VAsex90_bm ~

Hi I hope this is the right forum - if not, point me please to a better one. I am using R 2.3.0 on Linux, SuSE 10. I have a question concerning mle (method="BFGS"). I have a few models which I am fitting to existing data points. I realised, that the likelihood is quite sensitive to the start values for one parameter. I am wondering: what is the best approach to identify the right

Hi I am doing several mle and want to store them in a list (or whatever is the right construct) to be able to analyse them later. at the moment I am doing: f <- list() f$IP <- mle(...) f$NE <- mle(...) but when I say: > summary(f) I get: Length Class Mode IP 0 mle list NE 0 mle list I don't get the output I would have, i.e. the one from >

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

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 +

Dear all, I am using the "mgcv" package by Simon Wood to estimate an additive mixed model in which I assume normal distribution for the residuals. I would like to test this model vs a standard parametric mixed model, such as the ones which are possible to estimate with "lme". Since the smoothing splines can be written as random effects, is it correct to use an (approximate)

I know this a newbie question, but I've only just started using AIC for model comparison and after a bunch of different keyword searches I've failed to find a page laying out what the differences are between the AIC scores assigned by AIC() and mle.aic() using default settings. I started by using mle.aic() to find the best submodels, but then I wanted to also be able to make comparisons

Hi, How can we set the environment for the minuslog function in mle()? The call in this code fails because the "ll" function cannot find the object 'y'. Modifying from the example in ?mle: library(stats4) ll <- function(ymax=15, xhalf=6) { -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) } fit.mle <- function(FUN, x, y) { loglik.fun <- match.fun(FUN)

Hello I'm analyzing a dichotomous dependent variable (dv) with more than 100 measurements (within-subjects variable: hours24) per subject and more than 100 subjects. The high number of measurements allows me to model more complex temporal trends. I would like to compare different models using GLM, GLMM, GAM and GAMM, basically do demonstrate the added value of GAMs/GAMMs relative to

What the maximum filesize rsync can transfer? I'm trying to rsync one of my servers to another but the rsync is croaking on a file that's barely 1GB. Tips, hints, suggestions? rsync server is AIX 4.3.3 ML11 - rsync 2.6.3 rsync client is AIX 5.3 ML1 - rsync 2.6.4 Thanks -Jeff -- Jeff Schoby Unix/Network Admin City of Columbia, Missouri 573.874.6320

Dear all, I am a little problem. In the help, AIC = - 2log L + k*edf. When the model is linear, the help said " -2log L is the deviance ". I have a model toto.lm with one output and three input where deviance(toto.lm) = 8.027 and edf =4. But AIC = -31.55. I don't understand why? Many thanks. Jean LEJEUNE Universit? de CAEN (France)

Hello all, I read the following variable > x x 1 1_A1_ML1_a.DLL 2 11_B1_ML2_a.DLL 3 4_A1_ML3_a.DLL 4 55_C1_ML4_a.DLL 5 14_C1_ML5_a.DLL I would like to disperse it in three variable such as > x1 [1] 1 11 4 55 14 > x2 [1] "A1" "B1" "A1" "C1" "C1" > x3 [1] "ML1" "ML2" "ML3"

I have an issue using mle in versions of 32 bits. I am writing a package which I want to submit to the CRAN. When doing the check, there is an example that has an error running in the 32 bits version. The problem comes from the mle function, using it with a lower constrain. In 64 bits version it works fine but when I put it in the R 32 bits it fails. (same numbers, all equal!) The call is:

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 <-

Dear everybody! Need help with graphics. I am runnig a simple lm and then using allEffects from 'effects' package: require(effects) model<-lm(Y~A+B, data=mydataframe) I am trying to build (for each predictor - A and then B) a plot of means on Y. I was successful doing it like this - in one swoop: ml.eff<-allEffects(ml1, se=F) plot(ml.eff,ylab="Title of Y") Is it