Why R?

• Free

• Powerful (add-on packages)

• Online help from statistical community

• Code-based (can build programs)

• Publication-quality graphics

Why not?

• Time to learn code

• Very simple statistics may be faster with

“point-and-click” software

(e.g. Statistica, JMP)

Why generalized linear models (GLMs)?

Most ecological data FAIL these two

assumptions of parametric statistics:

• Variance is independent of mean

(“homoscedasticity”)

• Data are normally distributed

Taylors power law: most ecological data has 1>b>2

Mean

Variance Variance = a* Mean b

Many types of ecological data are expected to be non-normal

• Count data are expected to be Poisson

Examples: population size, species richness

• Binary (0,1) data are expected to be

binomial

Examples: survivorship, species presence

Workshop in R & GLMs

Session 1: Basic commands + linear models

Session 2: Testing parametric assumptions

Session 3: How generalized linear models

work

Session 4: Model simplification and

overdispersion

Exercise

1. Open R

“>” is the command prompt

2. Write:

x <- “hello”

x

3. What do the arrow keys do? And the “end”

key?

Ready!

Exercise

x <- 5

y<- 1

x+y; x*y; x/y ; x^y

sqrt(x); log (x); exp (x)

Careful! • Capitalization matters, Y and y are different.• Spaces do not matter, x<-5 is the same as x < - 5.

“;” means new command follows

Vectors

8

2

5

9

X <- c(8,2,5,9)

“c” means combine

Vectors

x <- rep (0,4)

x <- 1:4

x <- seq (1,7, by=2)

0,0,0,0

1,2,3,4

1,3,5,7

Create a vector called “test”

0,0,0,0,2,4,6,8,10

using all of the commands c, rep, seq

test<- c (rep(0,4), seq(2,10,by=2))

Vectors

Select an element of your vector (x = 1,3,5,7):

x[2] 3

1,5

3,5,7

x[c(1,3)]

x[2:4]

Change an element of your vector (x = 1,3,5,7):

x[1] <- 9 ; x 9,3,5,7

Matrices

Dog <- c(1,4,6,8) Cat<- c(2,3,5,7)Animals<-cbind (Dog, Cat)

Dog Cat 1 24 36 58 7

vectorvector

matrix

Logical operators

x<- 5; y<- 6x > y x< yx==yx!=y

True is the same as 1, false is the same as 0

falsetruefalsetrue

2 + (x>=y)2 + (x<=y)

23

Logical operators

x<- c(1,2,3,4); y<- c(5,6,7,8)

z <- x [y >= 7]; z

Useful for quickly making subsets of your data!

3,4

x<- c(1,0.01,3,0.02)

In this vector, change all values <1 to 0

x[x<1]<-0

Conditional operators

x<- 5 ; z<-0

if (x>4) {z<-2}; z

Could have a large program running in { }

2

Loopsy<-0; x<-0

for (y in 1:20) {x<- x+ 0.5; print(x)}

Useful for programming randomization procedures. Bootstrap example:

y<-0; x<-1:50output<-rep(0,1000)

for (y in 1:1000) {output [y] <- var (sample (x, replace=T))}

mean(output) 207.3996

Writing programs

I encourage you to use the script editor!

File > New script

Write your codeSelect the code you want to run (CTRL-A is all code)Run code (CTRL-R)

File > Save asR script files are always *.R

Entering data1. In Excel, give your data columns/rows and text data

simple one word labels (e.g."treatment")

2. Format cells so < 8 digits per cell.

3. Save as "csv" file.

4. Use the following command to find and load your

file:

diane<-read.table(file.choose(),sep=“,”,header=TRUE)

5. Check it is there! diane

Invent a dataframe name

Dataframes

• Dataframes are analogous to spreadsheets

• Best if all columns in your dataframe have the same

length

• Missing values are coded as "NA" in R

• If you coded your missing values with a different

label in your spreadsheet (e.g. "none") then:

read.table (….., na.strings="none")

Dataframes

Two ways to identify a column (called "treatment") in

your dataframe (called "diane"):

diane$treatment

OR

attach(diane); treatment

At end of session, remember to: detach(diane)

Summary statistics

length (x)

mean (x)

var (x)

cor (x,y)

sum (x)

summary (x) minimum, maximum, mean, median, quartiles

What is the correlation between two variables in your dataset?

Factors

• A factor has several discrete levels (e.g. control,

herbicide)

• If a vector contains text, R automatically assumes it

is a factor.

• To manually convert numeric vector to a factor:

x <- as.factor(x)

• To check if your vector is a factor, and what the

levels are:

is.factor(x) ; levels(x)

1. Download R on your computer.

Either go to http://www.r-project.org/ and follow the download CRAN links

or directly to http://mirror.cricyt.edu.ar/r/

2. Instruction Manuals to R are found at main webpage:

http://www.r-project.org/

follow links to Documentation > Manuals

I recommend "An Introduction to R"

Homework

3. Write a short program that:

• Allows you to import the data from Lakedata_06.csv

(posted on www.zoology.ubc.ca/~srivast/zool502)

• Make lake area into a factor called AreaFactor:

Area 0 to 5 ha: small

Area 5.1 to 10: medium

Area > 10 ha: large

hints

You will need to:

1. Tell R how long AreaFactor will be.

2. Assign cells in AreaFactor to each of the 3 levels

3. Make AreaFactor into a factor, then check that it is a factor