NeuDist (version 1.0.1)

pp.plot: Generic Probability-Probability(P–P) Plot Function

Description

Generates a P–P (probability–probability) plot for any custom or built-in probability distribution. The function compares the empirical probabilities of the sample data with the theoretical probabilities computed from a user-specified cumulative distribution function (CDF).

Usage

pp.plot(sample, pfun, params, fit.line = TRUE)

Value

This function returns no value; it produces a P–P plot.

Arguments

sample

A numeric vector of sample observations.

pfun

A cumulative distribution function (CDF) corresponding to the theoretical distribution (e.g., pnorm, pexp, or a custom CDF).

params

A named list of distribution parameters (e.g., list(mean = 0, sd = 1) or list(alpha = 2, lambda = 1)).

fit.line

Logical; if TRUE (default), a red least-squares regression line is added to the plot. Also, displays the regression line equation and R² value on the plot.

Details

The P–P plot is used to assess how closely the empirical distribution of a dataset matches a specified theoretical distribution. The points should ideally fall along the 45° reference line if the model fits well.

Requires user-defined function 'pfun' for the CDF of the user-defined continuous distribution.

Missing values in the sample are automatically removed with a warning.

Examples

# Example 1: Exponential distribution
set.seed(123)
x <- rexp(100, rate = 2)
pp.plot(x, pexp, list(rate = 2))

# Example 2: Customizing the fitted line
pp.plot(x, pexp, list(rate = 2),
              fit.line = TRUE)

# Example 3: Without regression line
pp.plot(x, pexp, list(rate = 2), fit.line = FALSE)

# Example 4: Display regression equation and R² value
pp.plot(x, pexp, list(rate = 2))

# Example 5: For a user defined distribution
# Exponentiated Exponential Power (EEP) Distribution
# Data
x <- waiting
pp.plot(x,
      params = list(alpha=0.3407, lambda=0.6068, theta=7.6150),
      pfun = pgen.exp.power, fit.line=TRUE)