1 / 18

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 Statistics in R General Linear Model Generalized Linear Model Hierarchical Linear Modeling Latent Variable Modeling.

phuong
Download Presentation

argument 'FUN' is missing, with no default: An R Workshop

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. argument 'FUN' is missing, with no default: An R Workshop

  2. 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

  3. 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

  4. 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

  5. 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)

  6. 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)

  7. 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)

  8. 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’)

  9. 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]

  10. 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

  11. 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)

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

  13. 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…

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

  15. 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)

  16. 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

  17. Latent Variable Modeling First we have to setup a measurement model: LV2 LV3 LV1 X1 Y1 Y5 Y4 X4 Y8 X2 Y6 Y2 Y3 X3 Y7

  18. Latent Variable Modeling Then we have to setup the structural model: LV2 LV1 LV3

More Related