EMC2 (version 3.1.0)

recovery.emc: Recovery Plots

Description

Plots recovery of data generating parameters/samples. Full range of samples manipulations described in get_pars

Usage

# S3 method for emc
recovery(
  emc,
  true_pars,
  selection = "mu",
  layout = NA,
  do_CI = TRUE,
  correlation = "pearson",
  stat = "rmse",
  digits = 3,
  CI = 0.95,
  ci_plot_args = list(),
  ...
)
recovery(emc, ...)

Value

Invisible list with RMSE, coverage, and Pearson and Spearman correlations.

Arguments

emc

An emc object

true_pars

A vector of data-generating parameters or an emc object with data-generating samples

selection

A Character vector. Indicates which parameter types to plot (e.g., alpha, mu, sigma2, correlation).

layout

A vector indicating which layout to use as in par(mfrow = layout). If NA, will automatically generate an appropriate layout.

do_CI

Boolean. If TRUE will also include bars representing the credible intervals

correlation

Character. Which correlation to include in the plot. Options are either pearson or spearman

stat

Character. Which statistic to include in the plot. Options are either rmse or coverage

digits

Integer. How many digits to round the statistic and correlation in the plot to

CI

Numeric. The size of the credible intervals. Default is .95 (95%).

ci_plot_args

A list. Optional additional arguments to be passed to plot.default for the plotting of the credible intervals (see par())

...

Optional arguments that can be passed to get_pars or plot.default (see par())

Examples

# Make up some values that resemble posterior samples
# Normally this would be true values that were used to simulate the data
# Make up some values that resemble posterior samples
# Normally this would be true values that were used to simulate the data
pmat <- matrix(rnorm(12, mean = c(-1, -.6, -.4, -1.5), sd = .01), ncol = 4, byrow = TRUE)
# Conventionally this would be created before one makes data with true values
recovery(samples_LNR, pmat, correlation = "pearson", stat = "rmse", selection = "alpha")
# Similarly we can plot recovery of other parameters with a set of true samples
true_samples <- samples_LNR # Normally this would be data-generating samples
recovery(samples_LNR, true_samples, correlation = "pearson", stat = "rmse",
         selection = "correlation", cex = 1.5,
         ci_plot_args = list(lty = 3, length = .2, lwd = 2, col = "brown"))