Skip to contents

Plot the treated and control outcome fits from a causal model

plot.mixgpd_fit.Rd

plot.causalmixgpd_causal_fit() is a convenience router to the underlying one-arm diagnostic plots for the treated and control fits.

Uses ggmcmc to produce standard MCMC diagnostic plots. Works with 1+ chains.

S3 method for visualizing fitted values from fitted.mixgpd_fit(). Produces a 2-panel figure: Q-Q plot and residuals vs fitted.

Usage

# S3 method for class 'causalmixgpd_causal_fit'
plot(x, arm = "treated", ...)

# S3 method for class 'mixgpd_fit'
plot(x, family = "traceplot", params = NULL, nLags = 50, ...)

# S3 method for class 'mixgpd_fitted'
plot(x, y = NULL, ...)

Arguments

x

Object of class mixgpd_fitted from fitted.mixgpd_fit().

arm

Integer or character; 1 or "treated" for treatment, 0 or "control" for control, or "both" for both arms.

...

Additional arguments (ignored).

family

Character vector of plot names (ggmcmc plot types) or a single one. The default "traceplot" returns one diagnostic. Use "all" to include all plots supported for the available number of chains/parameters. "auto" is accepted as an alias for the default single diagnostic. Supported types:

  • "histogram": posterior histograms

  • "density": posterior density curves

  • "traceplot": MCMC trace plots

  • "running": running mean plots

  • "compare_partial": partial chain comparisons

  • "autocorrelation": autocorrelation plots

  • "crosscorrelation": cross-correlation matrix

  • "Rhat": Gelman–Rubin R-hat (2+ chains)

  • "grb": Gelman–Rubin–Brooks (2+ chains)

  • "effective": effective sample size

  • "geweke": Geweke diagnostic

  • "caterpillar": caterpillar/forest plots

  • "pairs": pairwise scatter plots (2+ params)

params

Optional parameter selector:

  • character vector of parameter patterns (exact names or partial matches)

  • a single regex string (e.g. "alpha|threshold|tail_")

  • NULL (default): plots all monitored parameters

nLags

Number of lags for autocorrelation (ggmcmc).

y

Ignored; included for S3 compatibility.

Value

The result of the underlying plot call (invisibly).

Invisibly returns a named list of ggplot objects.

Invisibly returns a list with the two plots.

Details

Each arm-specific outcome model is itself a mixgpd_fit, so this method delegates to plot.mixgpd_fit() for the selected arm. With arm = "both", it returns a named list of treated and control diagnostics so the two fitted outcome models can be assessed side by side.

These are MCMC diagnostics for the nuisance outcome models, not plots of causal estimands. Use plot() on objects from predict.causalmixgpd_causal_fit(), qte(), or ate() when the goal is to visualize treatment effects rather than chain behavior.

The supported plots diagnose posterior simulation quality rather than data fit. Depending on the selected family, they show chain traces, marginal posterior densities, autocorrelation, cross-correlation, running means, or Gelman-style convergence summaries for the monitored parameters.

These graphics should be read before interpreting posterior summaries or treatment-effect results. Poor mixing or strong autocorrelation in the MCMC output can invalidate downstream summaries even when the fitted model itself is correctly specified.

These diagnostics compare the fitted values implied by the posterior summary on the training design against the observed responses. The first panel checks how closely fitted and observed values align, while the second panel looks for residual structure that would indicate lack of fit or remaining mean trends.

This method is distinct from posterior predictive simulation on new data. It is a training-sample diagnostic built from fitted.mixgpd_fit() and the corresponding residuals.

See also

plot.mixgpd_fit, predict.causalmixgpd_causal_fit, ate, qte.

Examples

# \donttest{
y <- abs(stats::rnorm(25)) + 0.1
bundle <- build_nimble_bundle(y = y, backend = "sb", kernel = "normal",
                             GPD = TRUE, components = 3,
                             mcmc = list(niter = 100, nburnin = 50, thin = 1, nchains = 1))
fit <- run_mcmc_bundle_manual(bundle)
#> [mixgpd] Validating configuration
#> [mixgpd] Checking build/compile cache
#> [mixgpd] Building model and MCMC configuration
#> [mixgpd] Compiling NIMBLE model
#> [mixgpd] Initializing chains
#> [mixgpd] Running MCMC
#> [mixgpd] Finalizing WAIC and diagnostics
#> [mixgpd] Assembling fit object
plot(fit, family = c("traceplot", "density"))
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> 
#> === traceplot ===

#> 
#> === density ===

# }