Denote the joint distribution of \(X\) and \(Y\) by \(f_\) (i.e., if the distribution of \(X\) in the whole population is the same as the distribution of \(X\) in the sub-population defined by specific values of \(Y\)).
Suppose we have two categorical variables, denoted \(X\) and \(Y\). See below for the two-way gender and site example. You can add more then two variables (hence the …). Instead you use formula notation, which is ~variable1+variable2+… where variable1 and variable2 are the names of the variables of interest. With xtabs(), you do not list out the variables of interest separated by commas. xtabs() has the following advantages: 1) row and column labels are included automatically, set to the variable names and 2) there is a data= argument, which means you just have to reference the variable names. The function xtabs() creates a object of type xtabs and you will notice that the output of both xtabs() and tabel() is nearly identical. # Male 0.16013629 0.14480409 0.14480409 0.14991482Ī second way of creating contingency tables is using the xtabs() function, which requires the stats package (which is included in R by default, though still load the package using library()). 11.3.1 Setting the Seed: Reproducibility in Simulation Studies.11.2.1 Example: Regression Analysis as a Function Call.11.1.1 Example 1: Running many regression models.9.4 Creating your document from the R Markdown file.9.3 Understanding the R Markdown editor.9 Documenting your results with R Markdown.8.4.3 Interpreting results: time dependent covariates.8.4.2 Example: Mullen composite and Visit.8.3.3 Example 2: Categorical Covariates.8.2.5 Example 2: Categorical predictors.7.2.2 Accounting for estimation variance and hypothesis testing.7.2.1 Parameter Estimation: Mean, Median, tutorial, Quantiles.6.2 Creating Basic Tables: table() and xtabs().4.2.6 Editing factor variables: recode() and relevel().4.2.3 Spread, Gather, Separate and Unite.2.3 R and RStudio: What is the difference?.
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