Displaying 6 results from an estimated 6 matches for "aictab".

2017 Dec 26

1

identifying convergence or non-convergence of mixed-effects regression model in lme4 from model output

....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])...

2012 Feb 13

2

R's AIC values differ from published values

...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...

2006 Jul 04

1

lmer print outs without T

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?

2012 Nov 08

2

Comparing nonlinear, non-nested models

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,

2010 Jan 26

1

AIC for comparing GLM(M) with (GAM(M)

...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....

2008 Dec 19

4

Akaike weight in R

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.