ARGUMENT 'FUN' IS MISSING, WITH NO DEFAULT: AN R WORKSHOP

Outline

The R “sales pitch” R Basics Data Management Descriptive Statistics in R Inferential Statistics in R

General Linear Model Generalized Linear Model Hierarchical Linear Modeling Latent Variable Modeling

Why Should I Use R?

Free 99

It’s as powerful as SAS and as user friendly as SPSS…really…

You ain’t cool unless you use R

It’s free…seriously

R Basics

• Do not write code directly into the R interface!

• #Comment #StatsAreCool #Rarrrgh• Yes the # lets you add comments to your

code• R is case sensitive

• A ≠ a • <- is the assignment operator

• A <- 3; a <- 4

R Basics

• Creating objects in R – Creating a scalar • X <- 2

– Creating a vector • X <- c(2,2,4,5)

– Creating a matrix • X <- matrix(c(1,1,2,2,3,3),nrow=2, ncol=3)• Y <- matrix(c(1,1,1,1,1,1),nrow=3,ncol=2)

– Creating a dataframe • A <- c(1,2,3,4)• B <- c('T','F','T','F')• ds <- data.frame(A,B)

R Basics

Arithmetic 2 + 2; 2-2; 2*3;2/3

Boolean Operators 2 > 3; 3 < 6; 4 == 4

Matrix Algebra X%*%Y t(X) ginv(X)

R Basics

Packages in R Like SPSS modules, but free… Upside: Thousands of packages to do just

about anything Downside: Placing your trust in freeware…

which I’m fine with, but some aren’t library(MASS)

ginv(X)

I’m an import-exporter: Database Management Importing from a text file

Dataset <- read.table(‘filelocation.txt’) Importing from a csv file

Dataset <- read.csv(‘filelocation.csv’) Foreign package to read SPSS data files

package(foreign) Dataset <- read.spss(‘filelocation.sps’)

Database Management

Exporting R dataframes to csv write.csv(dataframe, ‘filelocation.csv’)

Exporting R dataframe to text file write.table(dataframe, ‘filelocation.txt’)

Variables in a dataframe Adding: ds$C <- c(4,3,2,1) Deleting: ds <- ds[,-3] Referencing: ds$A or ds[,1]

Database Management

Indexing Dataframes ds[,2] gives you column 2 of ds ds[1,] gives you row 1 of ds ds[2,2] gives you row 2 column 2 of ds

Descriptive Statistics

Measures of central tendency Mean – mean(X) Median – med(X) Mode – table(X) (A little round about, but oh

well) Measures of dispersion

var(X) sd(X)

Descriptive Statistics

Measures of Covariation cov(X,Y) – Covariance cor(X,Y) – Correlation

Caution!

I will not be talking about any of the theoretical underpinnings as to when or why you should use one statistical method over another.

We’ll just be doing some PnP statistics…

General Linear Model

Read Edwards & Lambert, 2007

X

M

Y

Z

Generalized Linear Model

Uses the generalized linear modeling function glm() Can handle dvs that are binomial, poisson,

multinomial, guassian

glm(y ~ x1 + x2, family=binomial, data=LRDS)

Hierarchical Linear Model

HLM allows you to look at between and within group variation Employees nested within organizations Repeated measures nested within an

individual Variance Components Analysis

Latent Variable Modeling

LV1

X1X2 X3

X4

LV2

Y1Y2 Y3

Y4

LV3

Y5Y6 Y7

Y8

First we have to setup a measurement model:

Latent Variable Modeling

LV1

LV2

LV3

Then we have to setup the structural model: