Plot SSM CI accuracy across the amplitude ladder

Draw the empirical coverage from an ssm_ci_accuracy() run against its amplitude-ladder conditions: one panel per SSM parameter (including displacement conditional on guardrail certification), one line per profile row, with 95% Wilson score intervals as error bars, Bradley's (1978) liberal robustness band shaded, and the nominal confidence level as a dashed line. Amplitude rungs whose coverage is structurally zero (a percentile interval of strictly positive amplitude replicates cannot contain a zero truth; see ssm_ci_accuracy()) are drawn as open symbols. This is a Cartesian diagnostic plot, not a circumplex figure.

Usage

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

Arguments

x

A circumplex_ci_accuracy object from ssm_ci_accuracy().

...

Currently ignored.

Value

A ggplot2 object.

See also

Other ssm functions: ssm_analyze(), ssm_analyze_long(), ssm_ci_accuracy(), ssm_draws(), ssm_parameters(), ssm_parameters_id(), ssm_score(), ssm_sem(), ssm_sem_parameters(), ssm_table(), summary.circumplex_ssm_id()

Other visualization functions: ssm_plot_circle(), ssm_plot_contrast(), ssm_plot_curve(), ssm_plot_trajectory()

Examples

# \donttest{
data("jz2017")
set.seed(12345)
res <- ssm_analyze(
  jz2017[1:200, ],
  scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"),
  boots = 100
)
set.seed(23456)
acc <- ssm_ci_accuracy(res, reps = 25)
#> Warning: CPM Hessian is ill-conditioned (condition number 6.69e+17): angles may be clustered or parameters weakly determined.
plot(acc)

# }