# Practical Model Selection and Multi-model Inference using R

```Practical Model Selection
and Multi-model Inference
using R
Modified from on a presentation by :
Eric Stolen and Dan Hunt
Theory
•
This is the link with science, which is
works
Indigo Snake Habitat selection
David R. Breininger, M. Rebecca Bolt, Michael L. Legare, John H. Drese, and Eric D. Stolen
Source: Journal of Herpetology, 45(4):484-490. 2011.
– Animal perception
– Evolutionary Biology
– Population Demography
http://www.seaworld.org/animal-info/animal-bytes/spooky-safari/eastern-indigo-snake.htm
Hypotheses
•
To use the Information-theoretic toolbox,
we must be able to state a hypothesis as a
statistical model (or more precisely an
equation which allows us to calculate the
maximum likelihood of the hypothesis)
http://www.seaworld.org/animal-info/animal-bytes/spooky-safari/eastern-indigo-snake.htm
Multiple Working Hypotheses
•
•
We operate with a set of multiple alternative
hypotheses (models)
objectivity, and allowing rigorous inference.
Chamberlain (1890)
Strong Inference - Platt (1964)
Karl Popper (ca. 1960)– Bold Conjectures
Deriving the model set
•
•
•
•
This is the tough part (but also the creative part)
much thought needed, so don’t rush
literature, go to meetings…
How and When hypotheses are better than What
hypotheses (strive to predict rather than describe)
Models – Indigo Snake example
David R. Breininger, M. Rebecca Bolt, Michael L. Legare, John H. Drese, and Eric D. Stolen
Source: Journal of Herpetology, 45(4):484-490. 2011.
•
•
•
•
•
•
Study of indigo snake habitat use
Response variable: home range size ln(ha)
SEX
Land cover – 2-3 levels (lC2)
weeks = effort/exposure
Science question: “Is there a seasonal difference in
habitat use between sexes?”
Models – Indigo Snake example
SEX
land cover type (lc2)
weeks
SEX + lc2
SEX + weeks
llc2 + weeks
SEX + lc2 + weeks
SEX + lc2 + SEX * lc2
SEX + lc2 + weeks + SEX * lc2
http://www.herpnation.com/hn-blog/indigo-snake-survivaldemographics/?simple_nav_category=john-c-murphy
Models – Indigo Snake example
SEX
land cover type (lc2)
weeks
SEX + lc2
SEX + weeks
llc2 + weeks
SEX + lc2 + weeks
SEX + lc2 + SEX * lc2
SEX + lc2 + weeks + SEX * lc2
Modeling
• Trade-off between precision and bias
• Trying to derive knowledge / advance learning; not
“fit the data”
• Relationship between data (quantity and quality) and
sophistication of the model
Bias 2
Model Complexity – increasing umber of Parameters
Bias 2
variance
Model Complexity – increasing umber of Parameters
Bias 2
variance
Model Complexity – increasing umber of Parameters
Kullback-Leibler Information
• Basic concept from Information theory
• The information lost when a model is used to
represent full reality
• Can also think of it as the distance between a
model and full reality
Kullback-Leibler Information
Truth / reality
G1 (best model in set)
G2
G3
Kullback-Leibler Information
Truth / reality
G1 (best model in set)
G2
G3
Kullback-Leibler Information
Truth / reality
G1 (best model in set)
G2
G3
Kullback-Leibler Information
Truth / reality
G1 (best model in set)
G2
G3
The relative difference
between models is constant
Akaike’s Contributions
• Figured out how to estimate the relative
Kullback-Leibler distance between models in a
set of models
• Figured out how to link maximum likelihood
estimation theory with expected K-L information
• An (Akaike’s) Information Criteria
• AIC = -2 loge (L{modeli }| data) + 2K
AICci = -2*loge (Likelihood of model i given the
data) + 2*K (n/(n-K-1))
or
= AIC + 2*K*(K+1)/(n-K-1)
(where K = the number of parameters estimated
and n = the sample size)
AICcmin = AICc for the model with the lowest
AICc value
Di = AICci– AICcmin
wi 
exp( 0.5  D i )
n
 exp( 0.5  D )
r
1
wi =Prob{gi | data}
Model Probability (model
probabilities)
evidence ratio of model i to model j = wi / wj
Least Squares Regression
AIC = n loge (s2) + 2*K (n/(n-K-1))
Where s2  RSS / n
Counting Parameters:
K = number of parameters estimated
Least Square Regression
K = number of parameters + 2 (for intercept &amp; s)
Counting Parameters:
K = number of parameters estimated
Logistic Regression
K = number of parameters + 1 (for intercept)
Comparing Models
Model selection based on AICc :
mod4
mod7
mod1
mod5
mod2
mod6
mod3
K
4
5
3
4
3
4
3
AICc Delta_AICc AICcWt Cum.Wt
LL
112.98
0.00
0.71
0.71 -51.99
114.89
1.91
0.27
0.98 -51.67
121.52
8.54
0.01
0.99 -57.47
122.27
9.29
0.01
1.00 -56.64
125.93
12.95
0.00
1.00 -59.67
128.34
15.36
0.00
1.00 -59.67
141.26
28.28
0.00
1.00 -67.34
Model 1 = “SEX &quot;,
Model 2 = &quot;ha.ln ~ lc2&quot;,
Model 3 = &quot;ha.ln ~ weeks &quot;,
Model 4 = &quot;ha.ln ~ SEX + lc2&quot;,
Model 5 = &quot;ha.ln ~ SEX + weeks&quot;,
Model 6 = &quot;ha.ln ~ lc2 + weeks&quot;,
Model 7 = &quot;ha.ln ~ SEX + lc2 + weeks&quot;
Model Averaging Predictions

R

Y   wi Yi
i 1
Model Averaging Predictions

R

Y   wi Yi
i 1
Model-averaged prediction
Model Averaging Predictions

R

Y   wi Yi
i 1
Prediction from modeli
Model Averaging Predictions

R

Y   wi Yi
i 1
Weight modeli
Model Averaging Parameters

R

   wi i
i 1
Model-averaged parameter estimate
Unconditional Variance Estimator
 
 
 

var      wi var  i gi    i   
  i 1  

 

R



2



Unconditional Variance Estimator
 
 
SE     var   
 
 


 
95% CI    1.96 * SE   
 


```