May 29 �
June 2, 2017
INSTRUCTORS:�
Dr. Charles Canham, email: canhamc@caryinstitute.org
Dr. Patrick Martin, email: patrick.martin@colostate.edu
Day 1
8:30� �� 9:30��� Lecture 1:� An Introduction to Likelihood for Estimation
and Inference���
9:30� � 10:30�� Case Study 1:�� Distribution and Abundance of Tree Species
Along Climate Gradients� [Canham
and Thomas 2010]� [Case Study 1 � R Code]� [Dataset]
10:30 �
12:00� Lab�
1:� Calculating Likelihood,
Likelihood Surfaces, and Likelihood Profiles��� [R Code � Section
1]� [R Code � Section
2]
12:00 �� 1:00�� Lunch
1:00� �� 2:00��� Lecture
2:� Parameter Estimation and Evaluation
of Support
2:00� ���
4:00�� Lab 2: Global
Optimization using Simulated Annealing and Genetic Algorithms�
[R code �
anneal]�� [R code �
genoud]� [R code � optim]�
[BC Sapling
Growth Data � Excel Version] [BC Sapling
Growth Data.txt � (right click and �Save as��)]
4:00 � 5:00����� Student presentations
Recommended
Reading for Day 2:�
Day 2
8:30 � 9:30 ���� �Lecture
3:� Hypothesis Testing and Statistical
Inference Using Likelihood: The Central Role of Models
9:30 � 10:30
�� �Case Study 2:
Neighborhood Models Of The Allelopathic Effects Of An Invasive Tree Species
10:30 �
12:00�� Lab� 3:�
Likelihood Functions for Continuous and Count Data: A Plethora of PDFs
����������������������������������� [McLaughin
1993 � Compendium of Common Probability Distributions]
����������������������������������� [Normal PDF
with non-homogeneous variances]
����������������������������������� [Gamma and
Lognormal PDFs]� [Exponential PDF]�� [Beta PDF]
����������������������������������� [Poisson,
Negative Binomial, and Zero-Inflated PDFs]
12:00�� ��
1:00 Lunch
1:00�� � 2:00��� Lecture 4:� Model Selection: AIC and Akaike Weights,
Multi-model Inference
�
2:00� �� 4:00��� Lab 4:�
Model Comparison and Hypothesis Testing in a Likelihood Framework
����������������������������������� [Lab 4 R Script]
4:00 � 5:00����� Student presentations
Recommended
Reading for Day 3:�
Day 3
8:30�� � 9:30��� Lecture
5:� Model Evaluation:� How good is the best model?
9:30 � 10:30��� Case
Study 3:� Neighborhood Models of Tree
Competition
10:30 �
12:00� Lab 5:� Model Evaluation�� [Sample
R Code for Model Evaluation]
12:00� �� 1:00� Lunch
1:00� ��
2:00���� Lecture
6:� Analysis of Categorical and Ordinal
Data:� Binomial and Logistic Regression
2:00� �� 3:00��� Lab 6: Developing your own binomial and
logistic regression models in R
[R
code � Logistic regression of windthrow data]�� [Damagedata.Rdata]��� [R code � binomial
regression]
3:00 � 5:00����� Student presentations
Recommended
Reading for Day 4:
Day 4
9:30 � 10:30��� Case Study 4:� Inverse Modeling of Seed and Seedling
Dispersion
10:30 �
12:00� Lab Project:�� Fundamental vs Realized Niches of Tree
Species Along Climate Gradients
See the Course Materials page for
datasets, R scripts, and documentation for the projects
����������������������� [Course Materials Page]
12:00� �� 1:00� Lunch
1:00 ��� 5:00��� Lab
Project (continued):�
�����������������������
Day 5
8:30 � 10:00��� Lab Project (continued)
10:00 � 12:00�� Wrap-up