[R] SAR: nonlinear and linear mixed model comparison using mmSAR - package
Bernd Lenzner
bernd_lenzner at gmx.de
Thu Sep 19 16:03:11 CEST 2013
Hello,
I have a problem trying to compare model-fit between linear and
non-linear mixed models. I am using the mmSAR-package from Guilhaumon
(Version 1.0) to fit different models (power, exponential, lomolino,
weibull etc.) to my data. Besides that I am as well fitting a linear
model to the data.
My dataset looks like this:
a s
1 21.28038 2.944439
2 21.22179 3.091042
3 22.09917 3.526361
4 21.66947 2.197225
5 21.66702 3.713572
6 21.58499 3.044522
7 22.03311 3.465736
8 22.03289 2.397895
9 22.16306 3.610918
.
.
.
with a = area estimate and s = species richness. Both variabels are log
- transformed to reach normality and variance homogeneity.
Now I want to compare the model fit of the linear model with the fit of
the non-linear models by using information theory. I use corrected AIC
values to estimate model fit but when I use the "multiSAR" function from
the mmSAR package to genereate non-linear model fit I obtain negative
AICc values:
>mod.selection <- multiSAR(MODELS,DATA)
>mod.selection$filtOptimRes
p1 p2 p3 AICc
power 1.349178e-01 9.999853e-01 0.00000000 -55.64722
expo -1.201421e+01 4.856363e+00 0.00000000 -56.93415
negexpo 1.063781e+08 1.268287e-09 0.00000000 -55.64728
logist 3.762325e+00 3.364975e-01 6.02467719 -54.79978
ratio -2.427444e-02 8.295920e-02 -0.01806225 -54.56568
lomolino 3.925678e+00 5.436657e+02 18.23351785 -54.79876
weibull 3.550748e+00 2.352926e-06 4.39606412 -54.81074
on the other hand I get a positive AICc value for the linear model
>lin <- lm(log(s) ~ log(a))
>AICc(lin)
> 181.50
How do I interpret these results? Visually inspecting the regressions
shows that the exponential regression line is almost identical to the
linear one so I would expect that the AICc value should be somewhere in
the same range.
Thanks for the help or suggestions.
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