Simulate Data for Diagnostic Plots

Generate simulated data sets designed to illustrate various model misspecifications and diagnostics for ordinal regression models, as described in Liu and Zhang (2017).

Usage

sim_data(
  n = 2000,
  type = c("quadratic", "heteroscedastic", "gumbel", "proportionality", "interaction"),
  ...
)

Arguments

n

Integer specifying the number of observations to simulate. Default is 2000.

type

Character string specifying the type of data/model to simulate. Default is "quadratic" (linear relation with a quadratic trend). Other options include: "heteroscedastic" (non-constant variance), "gumbel" (Gumbel error distribution), "proportionality" (non-proportional hazards), and "interaction" (interaction effect).

...

Additional optional arguments (currently ignored).

Value

A data frame containing the simulated predictor(s) and the ordered factor response variable y.

References

Liu, Dungang and Zhang, Heping. Residuals and Diagnostics for Ordinal Regression Models: A Surrogate Approach. Journal of the American Statistical Association (accepted).

Examples

# Simulate quadratic data
set.seed(101)
df <- sim_data(n = 500, type = "quadratic")
head(df)
#>   y        x
#> 1 2 3.233190
#> 2 3 1.262949
#> 3 2 5.258104
#> 4 2 4.946142
#> 5 2 2.499134
#> 6 2 2.800329