RNR 614 Home Page

Professor: Brian McGill (614@brianmcgill.org), 621-9389, BSE 310, Office hours: W 10:10-11:00
TA: Christine Lamana (clamanna@email.arizona.edu)

	Dates	Topic	Readings (due Mondays before class)	Homework (due Fridays 5:00 PM)
1	Aug 24, 26	Scientific inference & the need for statistics	G&E 4	N/A
2	Aug 31, Sep 2	continued + probablity crash course	Lakatos (text) (MP3) Platt Quinn & Dunn G&E 1-2 (Optional: Absence of evidence) (optional: How science really works)	N/A
3	Sep 9	OLS	Brett Garcia Welsh etal G&E Ch 9:239:259 (Crawley 1-7 - especially 2&3)	Sep 11 Help session 11:30-1:30 BSE 328 R-Matlab guide
4	Sep 14, 16	Collinearity, path analysis	Freckelton Grace Freedman (Optional: Murtaugh) (Optional: Wilcox) (Optional: Whittingham) (Optional: Graham) (Guide to Path Anal in R) (Crawley 10)	1 - R Introduction - Sep 16
5	Sep 21, 23	GLM - what is it, ANCOVA	Stewart-Oaten Steidl & Thomas Brosi & Biber G&E Ch 10 (Crawley 11-12) (Steidl & Thomas bibliography)	Sep 25 Help session 11:30-1:30 BSE 328
6	Sep 28, 30	3 schools: Frequentist/Bayesian/Monte Carlo, Likliehood, AIC	Moller & Jenions Wyckoff & Clark Johnson & Omland G&E 5, Ch 9:264-267 (optional: Clark 2005 - why Bayesian) (optional: Fidler et al - why reform is slow in ecology) (optional: Ellison - Bayesian) (optional: Yoccoz - trouble with p)	2 - GLM in R Oct 2 Sample path analysis input file
7	Oct 5, 7	Generalized Linear Model (GLIM)	Zabel etal Cunningham & Lindenmayer (late breaking update: if you haven't started reading cunningham & lindenmayer yet read this one instead: Potts & Elith) (optional Muenchow 1986 (survivorship)) (optional Pollock et al 1989 (survivorship)) (Crawley 13-17)	Oct 9 Help session 11:30-1:30 BSE 328
8	Oct 12, 14	Experimental design	Hurlbert Oksanen Newman etal G&E 6-8 (Crawley 9)	3 - GLIM Oct 16
9	Oct 19, 21	GLS (nonindependence)	Garland & Ives Fortin et al (Optional: Felsenstein (PIC)) (Optional: Horton & Lipsitz on GEE) (Optional: Website on using APE) (Optional: Potvin etal)	Oct 23 Help session 11:30-1:30 BSE 328
10	Oct 26, 28	Mixed & hierarchical models	Gillies et al. McMahon & Diez Clark 2003 - Bayesian Hiearchical (optional: Bolker et al) (optional: Clark & Bjornstad 2004 - Bayesian Population Process) (Crawley 19) (guide to lmodel2) (also see this - lectures 42-48) (for good technical overview of LMM see this)	4 - GLS/Mixed/Hierarchical Oct 30
11	Nov 2, 4	Modern regression	DeAth & Fabricus - CART Cade & Noon G&E 9 p 268-281 (Crawley 18,20-21, 25) (optional: Elith et al 2006 - comparison) (optional: Guisan & Zimmerman) (optional: Elith et al 2008 - Boosted Regression Trees) (optional: Machine Learning Without Tears) (optional: Trexler & Travis (LOESS))	Nov 6 Help session 11:30-1:30 BSE 328
12	Nov 9	Multivariate statistics	Wainer James & McCulloch G&E 12 (Crawley 23) also see Vegan package tutorial also see Palmer's key to which ordination technique to use	5 - Modern Regression Nov 13
13	Nov 16, 18	Multivariate continued	Pick 3 Riiters et al (Landscape ecology, Factor Analysis) Chase (Community ecology, PERMANOVA) Jones et al (Vegetation gradients, PCA, Partitioning, space) Host etal (Land cover, PCA, Cluster) Roff & Mousseau (Evolution, MANOVA)	Nov 20 Help session 11:30-1:30 BSE 328
14	Nov 23, 25	Spatial-temporal	Halley McGill Book Chapter Statistics in Iraq (Crawley 22,24) (Optional: Dormann et al) (R code for Dormann et al)	6 - Multivariate Nov 25 (Wed)
15	Nov 30, Dec 2	Modelling, generalization, metaanalysis	Dunham & Beaupre Levins & Lewontin Arnqvist & Wooster (optional: Meta-analysis overview) (optional: McGill) (optional: Belovsky et al 2004) (optional: Schindler) (Crawley 26)	Dec 4 Help session 11:30-1:30 BSE 328
16	Dec 7	Wrap up	No class Dec 9!	7 - Time & Space Dec 9(Wed) Take home essay due Dec 11(directions)

G&E=Gotelli & Ellison - A Primer of Ecological Statistcs - Sinauer Associates 2004

Crawley = Michael J Crawley - The R Book - Wiley 2007

An overview of R functions/packages useful in ecology

Assignment/Testing Schedule/Due Dates

25% - Students must submit write-ups on 1-3 primary publications (selected by the professor) each week. These will be graded +/check/-. The lowest 3 scores will be dropped.
40% - Students must submit seven homeworks (approximately every other week). The lowest score will be dropped. [You may work with other people on this assignment, provided: a) you list their names on what you turn in and b)all computer code is actually generated by you and you alone].
20% - The term paper will be due one week after the end of classes.
15% - Class discussion/participation (quantity and quality)

No excuses will be accepted for late homeworks or reading write-ups. Missed assignments will be treated as lowest scores.

Attendance Policy

Attendance at the lectures is expected and will be reflected in the class participation portion of the grades.
Attendance at the labs is optional but help outside of the labs will not be provided to students who do not attend the labs barring exceptional circumstances.
All holidays or special events observed by organized religions will be honored for those students who show affiliation with that particular religion. Absences pre-approved by the UA Dean of Students (or Dean designee) will be honored.

The fine (but important!) print:

Classroom Behavior The Arizona Board of Regents’ Student Code of Conduct http://deanofstudents.arizona.edu/policiesandcodes/studentcodeofconduct , ABOR Policy 5-308, prohibits threats of physical harm to any member of the University community, including to one’s self. See: http://policy.web.arizona.edu/~policy/threatening.pdf.
Special Needs and Accommodations Statement Students who need special accommodation or services should contact the Disability Resources Center, 1224 East Lowell Street, Tucson, AZ 85721, (520) 621-3268, FAX (520) 621-9423, email: uadrc@email.arizona.edu, http://drc.arizona.edu/. You must register and request that the Center or DRC send me official notification of your accommodations needs as soon as possible. Please plan to meet with me by appointment or during office hours to discuss accommodations and how my course requirements and activities may impact your ability to fully participate. The need for accommodations must be documented by the appropriate office.
Student Code of Academic Integrity Students are encouraged to share intellectual views and discuss freely the principles and applications of course materials. However, graded work/exercises must be the product of independent effort unless otherwise instructed. Students are expected to adhere to the UA Code of Academic Integrity as described in the UA General Catalog. See: http://deanofstudents.arizona.edu/codeofacademicintegrity.
Confidentiality of Student Records http://www.registrar.arizona.edu/ferpa/default.htm
Subject to Change Statement Information contained in the course syllabus, other than the grade and absence policy, may be subject to change with advance notice, as deemed appropriate by the instructor.