plot.vine_copula_fit {Surrogate} | R Documentation |
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 |
... |
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.