plot.vine_copula_fit: Goodness-of-fit plots for the fitted copula models

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Goodness-of-fit plots for the fitted copula models

Description

plot.vine_copula_fit() plots simple goodness-of-fit plots for the vine copula model fitted with fit_copula_ContCont(), fit_copula_OrdCont(), and fit_copula_OrdOrd().

Usage

## S3 method for class 'vine_copula_fit'
plot(x, ...)

Arguments

x	S3 object returned by fit_copula_ContCont(), fit_copula_OrdCont(), or fit_copula_OrdOrd().
...	Additional parameters. Currently not implemented.

Marginal Goodness-of-Fit

Continuous Endpoints

The estimated model-based marginal density for each continuous endpoint is plotted alongside a histogram based on the observed data.

Ordinal Endpoints

The estimated model-based marginal probabilities for each ordinal endpoint is plotted alongside the empirical proportions (red). Red whiskers represent the 95% confidence intervals for the empirical proportions. These are based on the delta method with the logit transformation for the proportion.

Goodness-of-Fit of Association Structure

Ordinal-Ordinal

For each possible value for the surrogate, a plot is produced with (i) the model-based estimated conditional probabilities, P(T = t | S), and (ii) the corresponding empirical conditional probabilities (red). Red whiskers represent the 95% confidence intervals for these empirical proportions. These are based on the delta method with the logit transformation for the proportion.

Ordinal-Continuous

The model-based estimated regression function E(T | S = s) is plotted alongside a semiparametric estimate using mgcv::gam(y~s(x), family = stats::quasi()) (red). Dashed lines represent pointwise 95% confidence intervals based on the semiparametric estimate. These confidence intervals are not trustworthy as they are based on a constant variance assumption.

Continuous-Continuous

The model-based estimated regression function E(T | S = s) is plotted alongside a semiparametric estimate using mgcv::gam(y~s(x), family = stats::quasi()) (red). Dashed lines represent pointwise 95% confidence intervals based on the semiparametric estimate.

Surrogate documentation built on April 11, 2025, 6:09 p.m.