Trondheim mini-symposium

Notes for a workshop here.

# Schedule

- introductions (problem descriptions?): $\approx$30 min
- data manipulation and visualization overview: 30 min
- data manip/vis lab (??), R troubleshooting: 1 hour
- break
- GLMM outline lecture: 1 hour
- GLMM lab: the rest of the morning
- lunch
- work on projects!
- present projects (??)

# Data manipulation and visualization

## Data manipulation

- Basic ideas: metadata and formats
- (Big data, spatial data: specialized tools (RDBMS, GIS):
**not covered**but see Spatial task view, High-performance computing task view, R Data manual) - Wide vs long vs relational table format (multitable package)
- Data frames
- Data manipulation in R
- Alternative paradigms
- Programming paradigm: [, [[, indexing
- Logical (high-level) paradigm:
`subset`,`transform`,`with`,`merge`(**not**`attach`)

`apply`operations- general-purpose vs special purpose (
`colMeans`etc.); performance, readability benefit vs. remembering more specific tools

- general-purpose vs special purpose (
`plyr`package: especially`ddply`- Dates and times:
**not covered**but see chron, lubridate, Datetime … always use the least complex date/time format possible

- Alternative paradigms

## Data visualization

- Three goals:
*exploratory*: understand patterns in data (efficiently, nonparametrically)*diagnostic*: understand departures from model (nonparametrically)*presentation*: tell a story (elegantly but honestly: trying to match the statistical model used, but not always)

- Tools
- for graphics generally
- Base R (programming/canvas paradigm): mish-mosh of functions at different levels of abstraction (
`plot`;`lines`;`segments`,`rect`,`arrows`,`text`). Many, many, many extensions (`plotrix`package). `grid`graphics: a low-level toolbox which can be used on its own. Object- rather than canvas-oriented. Murrell book.`lattice`: built on`grid`, much higher-level`ggplot(2)`: also built on`grid`, similar to`lattice`but even higher-level (and weird) (Wilkinson, Wickham books).- weird/advanced (
**not covered**): dynamic/3D/alternative:`rgl`,`rggobi`,`playwith`,`animation`

- Base R (programming/canvas paradigm): mish-mosh of functions at different levels of abstraction (
- for diagnostics:
- model methods (e.g.
`plot.lm`), more flexible versions in`lme`(`xyplot.lme` - or write your own
`fortify`in`ggplot`

- model methods (e.g.
- Mixed model-focused approaches:
- Pooled and fixed-effect (grouped) models

- for graphics generally

# GLMMs

## Reminder about GLMs

- families (i.e. distributions): Poisson, binomial, neg binom (1 and 2), Gamma, lognormal …
- overdispersion

## Reminder about mixed models

- "fixed" vs "random"
- nested vs crossed

## Estimation

- PQL: glmmPQL
- Laplace/AGHQ: lme4, glmmADMB
- MCMC: MCMCglmm

## Inference

- Wald
- LRTs (profiles)
- Posterior densities
- IC-based approaches
- conditional AIC

- Inference on RE variances
- Parametric bootstrap

## Challenges

- Estimation glitches
- Convergence problems
- Zero variance estimates/Perfect correlations

- $p$ values: finite-size corrections
- Spatial structure
- Temporal structure
- Phylogenetic/pedigree structure
- Nonlinearity (GAMs)
- Ordinal data
- Zero-inflation

# Data files etc.

- Banta_trondheim.pdf, Banta_trondheim.R, Banta_trondheim.Rnw
- Banta_TotalFruits.csv
- glmm_funs.R
- Banta_glmmADMB_fits.RData
- Banta_MCMCglmm_fit.RData
- glmmADMB_0.6.4.tar.gz

# Raw materials

- Harvard Forest, UWO, Concordia talks
- Banta examples
- other worked examples on this site (Owls, glycera, Culcita)
- sparrows & moose examples
- Vonesh seed predation example (lab 2 from the Book)
- examples at NCEAS site: wildflowers??

page revision: 22, last edited: 31 Aug 2011 06:58