test.data: Artificial dataset of performance outcomes and demographic...
In sumitrahman/perfman: Probability Estimates From Ordered Logistic Regression

This dataset has been created to try out the perfman package functions on. It illustrates the format that the package currently requires the data to be in to work straightforwardly.

Each row represents one person. This person's outcome from the performance management process is recorded: this is a whole number between 1 and 4 where 1 represents the worst score and 4 the best. Each person has observations recorded of their gender, ethnicity, disability and age. Ethnicity and disability are self-reported and suffer from non-response, but the non-response categories are coded for as separate categories (and not coded using NA or similar).

It is important to note that the first column has to be called outcome and has to be an ordered factor using integers starting from 1 in sequence. Note, too, that the other columns represent variables that will be fitted in the ordered logistic model, and they need to be caegorical (and therefore represented using factors).

test.data

A data frame with 200 rows and 5 variables. Each row represents a person. The first column is an ordered factor containing all the integers from 1 to 4.

outcome: Result from the performance management process for this person, where 1 is the worst category and 4 the best. There are four possible outcomes.
gender: Gender of the person. A variable with two factors "female" and "male"
ethnicity: Ethnicity of the person (self-reported). A variable with 4 factors: "BAME" for Black, Asian or Minority Ethnicity; "WB" for White British; "PNS" for people who declared they preferred not to say; and "NK" where no declaration has been made ('not known').
disability: Disability status of the person (self-reported). A variable with 4 factors: "disabled"; "not disabled"; "PNS" for people who declared they preferred not to say; and "NK" where no declaration has been made ('not known').
age: Age of person, coded as 1 for 16-29 year-olds; 2 for 30-39 year-olds; 3 for 40-49 year-olds; and 4 for staff older than 49.