oneplot: Plot Observed Data and Model Predictions

Description

Generates a bar plot of observed count data and overlays predicted values from one or more models fitted using oneinfl or truncreg.

Usage

oneplot(
  model1,
  model2 = NULL,
  model3 = NULL,
  model4 = NULL,
  df,
  maxpred = NULL,
  ylimit = NULL,
  ccex = 1.5
)

Value

A plot is generated but no values are returned.

Arguments

model1: The first fitted model object, either a one-inflated model (class `"oneinflmodel"`) or a truncated model (class `"truncmodel"`).
model2: Optional. A second fitted model object, structured similarly to model1.
model3: Optional. A third fitted model object.
model4: Optional. A fourth fitted model object.
df: A data frame containing the variables used in the models.
maxpred: Optional. The maximum count value to include in the plot. Defaults to the maximum observed count.
ylimit: Optional. The upper limit for the y-axis. Defaults to 1.1 times the highest observed frequency.
ccex: Optional. A numeric value controlling the size of plot points and lines. Defaults to 1.5.

Details

This function visualizes observed count data as a bar plot and overlays predicted values from up to four models. The function automatically detects the type of model (Poisson or negative binomial; one-inflated or truncated) and adjusts the plot accordingly. Predictions are generated using the pred function.

Model types are distinguished by different point and line styles:

Poisson (PP): Dark magenta, triangle-down
Zero-truncated negative binomial (ZTNB): Red, diamond
One-inflated Poisson (OIPP): Green, triangle-up
One-inflated zero-truncated negative binomial (OIZTNB): Blue, circle

The legend in the top-right corner of the plot indicates the models displayed.

Examples

Run this code

# Example usage
df <- data.frame(x = rnorm(100), z = rnorm(100), y = rpois(100, lambda = 5) + 1)
model1 <- oneinfl(y ~ x | z, df = df, dist = "Poisson")
model2 <- truncreg(y ~ x, df = df, dist = "Poisson")
oneplot(model1, model2, df = df, maxpred = 10)