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Plot for a Parity Regression Model

Source: R/plot.savvyPR.R
plot.savvyPR.Rd

Generates various visualizations for a fitted parity regression model object. It supports plotting estimated coefficients and risk contributions based on the specified plot_type.

Usage

# S3 method for class 'savvyPR'
plot(
  x,
  plot_type = c("estimated_coefficients", "risk_contributions"),
  label = TRUE,
  ...
)

Arguments

x

A fitted model object of class "savvyPR" returned by savvyPR.

plot_type

Character string specifying the type of plot to generate. Can be "estimated_coefficients" or "risk_contributions". Defaults to "estimated_coefficients".

label

Logical; if TRUE, labels are added based on the plot type:

estimated_coefficients

Numeric values are added to the coefficient plot.

risk_contributions

Numeric labels are added above the bars.

Default is TRUE.

...

Additional arguments passed to the underlying ggplot function.

Value

A ggplot object representing the requested plot.

Details

Plot for a Parity Regression Model

This function offers two types of plots, depending on the value of plot_type:

Estimated Coefficients

Generates a line plot with points for the estimated coefficients of the regression model. If an intercept term is included in the model, it will be labeled as beta_0. Otherwise, the coefficients are labeled sequentially as beta_1, beta_2, etc., based on the covariates. This plot helps to visualize the contribution of each predictor variable to the model. If label = TRUE, numeric values are displayed.

Risk Contributions

If the model includes a risk parity component, the function will check if the optimization results (e.g., orp_fit$weights for the budget method, or orp_fit$x for the target method, along with orp_fit$relativeRiskContrib) are available. If available, two bar plots are created:

  • Optimization Variables: A bar plot that visualizes the optimal variables assigned to each covariate and the response variable (weights for budget, target parameters for target).

  • Risk Contributions: A bar plot that visualizes the relative risk contributions of each covariate and the response variable.

If label = TRUE, numeric labels are added above the bars for clarity. If they are not found, a warning is issued.

See also

savvyPR

Author

Ziwei Chen, Vali Asimit and Pietro Millossovich
Maintainer: Ziwei Chen <ziwei.chen.3@citystgeorges.ac.uk>

Examples

# Example usage for savvyPR with Correlated Data:
set.seed(123)
n <- 100
p <- 12
# Create highly correlated predictors to demonstrate parity regression
base_var <- rnorm(n)
x <- matrix(rnorm(n * p, sd = 0.1), n, p) + base_var
beta <- matrix(rnorm(p), p, 1)
y <- x %*% beta + rnorm(n, sd = 0.5)

# Fit a Budget-based parity regression model
result_budget <- savvyPR(x, y, method = "budget", val = 0.05, intercept = TRUE)
plot(result_budget, plot_type = "estimated_coefficients", label = FALSE)

plot(result_budget, plot_type = "risk_contributions", label = TRUE)


# Fit a Target-based parity regression model
result_target <- savvyPR(x, y, method = "target", val = 1, intercept = TRUE)
plot(result_target, plot_type = "risk_contributions", label = TRUE)