ARGUMENT FUN IS MISSING WITH NO DEFAULT AN

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

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

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

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

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

R Basics • • Do not write code directly into the R interface! #Comment #Stats. Are. Cool #Rarrrgh • • R is case sensitive • • Yes the # lets you add comments to your code A≠a <- is the assignment operator • A <- 3; a <- 4

R Basics • Creating objects in R – Creating a scalar • – Creating

R Basics • Creating objects in R – Creating a scalar • – Creating a vector • – X <- c(2, 2, 4, 5) Creating a matrix • • – X <- 2 X <- matrix(c(1, 1, 2, 2, 3, 3), nrow=2, ncol=3) Y <- matrix(c(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 Boolean Operators 2 + 2; 2 -2; 2*3; 2/3 >

R Basics Arithmetic 2 Boolean Operators 2 + 2; 2 -2; 2*3; 2/3 > 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

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 Importing from a

I’m an import-exporter: Database Management Importing from a text file Dataset Importing from a csv file Dataset <- read. table(‘filelocation. txt’) <- 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

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, ]

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 –

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

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

Caution! I will not be talking about any of theoretical underpinnings as to when

Caution! I will not be talking about any of theoretical underpinnings as to when or why you should use one statistical method over another. We’ll just be doing some Pn. P statistics…

General Linear Model Read Edwards & Lambert, 2007 Z M X Y

General Linear Model Read Edwards & Lambert, 2007 Z M X Y

Generalized Linear Model Uses the generalized linear modeling function glm() Can handle dvs that

Generalized Linear Model Uses the generalized linear modeling function glm() Can handle dvs that are binomial, poisson, multinomial, guassian glm(y ~ x 1 + x 2, family=binomial, data=LRDS)

Hierarchical Linear Model HLM allows you to look at between and within group variation

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 First we have to setup a measurement model: LV 1 X

Latent Variable Modeling First we have to setup a measurement model: LV 1 X 2 LV 2 X 3 X 4 Y 1 Y 2 LV 3 Y 4 Y 5 Y 6 Y 7 Y 8

Latent Variable Modeling Then we have to setup the structural model: LV 2 LV

Latent Variable Modeling Then we have to setup the structural model: LV 2 LV 1 LV 3