search for: aictab

Hi folks, I'm not sure if this is a bug or not so I thought I'd check here first. I came across it while working on an update to my package where I try to get AICtab slot from the summary of an lmer object ( summary(my_lmer)@AICtab ). The attached contains a minimal example, where the code below will work if you load the f() function by sourcing the package's R code but will throw an error ("trying to get slot "AICtab" from an object (class...

...2.15.1. I need help with code that is instead being read as text. Below is a subset of code... I actually have 12 models, but I am trying to get this to work for 2 below right now. Everything 'appears' to work through the line starting with Modnames. After that the code starting with aictab and beyond is recognized as text and not code. What am I doing incorrectly with the code? buco.models<-list ( ) buco.models [[1]] <- glm(P.BUCO~faverage+W15CNT, family=binomial(logit), data=AmphAIC) buco.models [[2]] <- glm(P.BUCO~diam1+W15CNT, family=binomial(logit), data=AmphAIC) Mod...

....data$t.val = as.numeric(summary(lmer.mod)$coefficients[,4]) #t-values ? mod.data$p.val = as.numeric(summary(lmer.mod)$coefficients[,5]) #p-values ? #extract AIC, BIC, logLik, deviance df.resid ? mod.data$AIC = as.numeric(summary(lmer.mod)$AIC[1]) ? mod.data$BIC = as.numeric(summary(lmer.mod)$AICtab[2][1]) ? mod.data$logLik = as.numeric(summary(lmer.mod)$AICtab[3][1]) ? mod.data$deviance = as.numeric(summary(lmer.mod)$AICtab[4][1]) ? mod.data$df.resid = as.numeric(summary(lmer.mod)$AICtab[5][1]) ? #add number of datapoints ? mod.data$N = as.numeric(summary(incr.best.m)$devcomp$dims[1])...

...milar, but I'm unable to figure it out, maybe someone knows the trick how to do it ... I'm trying to extract some information from the summary object for a model fitted using the lmer function from the lme4 package: smry <- summary(lmer(foo ~ bar ...)) tab <- attr(smry, "AICtab") tab$AIC When I run this code as a regular R script everything works fine, tab$AIC has the desired value. But when I try to run this code from within a package I wrote, tab$AIC is NULL. I've tried adding "import(lme4)", "importClassesFrom(lme4)" and "import...

...t ) Cand.models[[3]] <- lm ( y ~ x1 + x2 + x1 * x2, data = cement ) Cand.models[[4]] <- lm ( y ~ x3 + x4 + x3 * x4, data = cement ) ## vector of model names Modnames <- paste("MODEL", 1:4, sep=" ") ## AICc aictab ( cand.set = Cand.models, modnames = Modnames ) However, the AICc value reported by Anderson (2008) is 32.41. The AICc value obtained using RSS value (i.e., calculating AICc "manually" from the output of linear regression) is 32.41. Thanks for any help. David New R user, min...

...wever this would obviously test each of the smooths aginst a horizontal line which is not what I want. Any help is greatly appreciated, thanks! Andrea Output of 1st model gamm.11: ---------------------------------------- logLik(gamm.11$mer);deviance(gamm.11$mer);attributes(summary(gamm.11$mer))$AICtab[1];gamm.11$mer@deviance["disc"] 'log Lik.' -49054.65 (df=5) ML 98109.3 AIC 98119.3 disc 97600 > summary(gamm.11$mer);summary(gamm.11$gam) Generalized linear mixed model fit by the Laplace approximation AIC BIC logLik deviance 98119 98167 -49055 98109...

Hi, I have been having a tedious issue with lmer models with lots of factors and lots of levels. In order to get the basic information at the beginning of the print out I also have to generate these enormous tables as well. Is there a method command to leave off all of the effects and correlations? Or, do I have to go to string commands?

Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x): x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,

...ly=binomial) > gamm.0<-gamm4(dv ~ s(hours24,fx=F,k=-1,bs=“cc“),method="ML",data=sdata, family=binomial) Fit indices using the commands as shown are: > logLik(gam.0)[1];deviance(gam.0);AIC(gam.0) > logLik(gamm.0$mer);deviance(gamm.0$mer);attributes(summary(gamm. 0$mer))$AICtab[1] gam.0: logLik=1149.6, deviance=2299.3, AIC=2316.0 gamm.0: logLik=1169.0, deviance=2338.0, AIC=2342.0 The differences between the two AIC values seem to be based on two factors. First, gam uses the effective degrees of freedom > sum(gam.0$edf) [1] 8.372517 whereas gamm4 uses the value 2....

I can't find a way to retrieve z values and p-values from the output from lmer in the lme4 package. How is this done? Rick B.

...have to define logLik and AIC for glmmML objects logLik.glmmML <- function(x) { loglik <- (-x$deviance)/2 attr(loglik,"df") <- length(coef(x)) loglik } AIC.glmmML <- function(x) x$aic library(bbmle) ## now it works (the answers are pretty trivial ## in this made-up case AICtab(m,sort=TRUE,weights=TRUE,delta=TRUE)

..."summary.lmer" else "summary.mer" }, object, isG = glz, methTitle = methTitle, logLik = llik, ngrps = sapply(object at flist, function(x) length(levels(x))), sigma = .Call(mer_sigma, object, REML), coefs = coefs, vcov = vcov, REmat = REmat, AICtab = AICframe) 2: summary(emnlp.m1) 1: summary(emnlp.m1) -- David Reitter ICCS/HCRC, Informatics, University of Edinburgh http://www.david-reitter.com

...251.405 ? ? ?6.825 ? 36.84 >>>> Days ? ? ? ? ?10.467 ? ? ?1.546 ? ?6.77 >>>> >>>> Correlation of Fixed Effects: >>>> ? ? (Intr) >>>> Days -0.138 >>>> >>>>> (fm1fit <- summary(fm1)@AICtab) >>>> ? ? ?AIC ? ? ?BIC ? ?logLik deviance ?REMLdev >>>> ?1755.628 1774.786 -871.8141 1751.986 1743.628 >>>> >>>>> fm1fit$BIC >>>> [1] 1774.786 >>> >>> That's one way of doing it but i...

Odette > Wondering how can I generate "Akaike weight" with R? I know the description, > but is there any function to generate by R on the web-site or R library? > I am using GLM or GLMM (family=binomial), so would be appreciated if you > help me. You could have a look at this. http://bm2.genes.nig.ac.jp/RGM2/R_current/library/aod/man/summary.aic.html Which is in the OAD

...have to define logLik and AIC for glmmML objects logLik.glmmML <- function(x) { loglik <- (-x$deviance)/2 attr(loglik,"df") <- length(coef(x)) loglik } AIC.glmmML <- function(x) x$aic library(bbmle) ## now it works (the answers are pretty trivial ## in this made-up case AICtab(m,sort=TRUE,weights=TRUE,delta=TRUE) ------------------------------ Message: 9 Date: Thu, 25 Dec 2008 22:20:38 +0100 From: Peter Dalgaard <p.dalgaard@biostat.ku.dk> Subject: Re: [R] 4 questions regarding hypothesis testing, survey package, ts on samples, plotting To: Ben Bolker <bolke...

I am trying to create a frequency distribution and I am a bit confused. Here are the commands I have entered: > data <- read.csv(file="40609_sortedfinal.csv",head=TRUE,sep=",") > NumberOfActionsByStatus = data$STATUS > NumberOfActionsByUser = data$ETS_LOGIN > NumberOfBidOffer = data$BID_OFFER > NumberOfActionsByUser.freq = table(NumberOfActionsByUser) >