ggplot2 autoplot for brs models

Produces ggplot2 diagnostics tailored to interval-censored scale models.

Usage

autoplot.brs(
  object,
  type = c("calibration", "score_dist", "cdf", "residuals_by_delta"),
  bins = 10L,
  scores = NULL,
  newdata = NULL,
  n_grid = 200L,
  max_curves = 6L,
  residual_type = "rqr",
  ...
)

Arguments

object

A fitted "brs" object.

type

Plot type: "calibration", "score_dist", "cdf", or "residuals_by_delta".

bins

Number of bins used in calibration plots.

scores

Optional integer vector of scores for "score_dist". Defaults to all scores from 0 to ncuts.

newdata

Optional data frame of covariate scenarios used by type = "cdf".

n_grid

Number of points on \((0,1)\) used to draw CDF curves.

max_curves

Maximum number of CDF curves shown when newdata is not provided.

residual_type

Residual type passed to residuals.brs for type = "residuals_by_delta".

...

Currently ignored.

Value

A ggplot2 object.

Details

type = "calibration" bins predictions and compares mean observed vs mean predicted response in each bin.

type = "score_dist" compares observed score frequencies against expected frequencies implied by the fitted beta interval model.

References

Hawker, G. A., Mian, S., Kendzerska, T., and French, M. (2011). Measures of adult pain: Visual Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire (MPQ), Short-Form McGill Pain Questionnaire (SF-MPQ), Chronic Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF-36 BPS), and Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care and Research, 63(S11), S240-S252. doi:10.1002/acr.20543.

Hjermstad, M. J., Fayers, P. M., Haugen, D. F., et al. (2011). Studies comparing Numerical Rating Scales, Verbal Rating Scales, and Visual Analogue Scales for assessment of pain intensity in adults: a systematic literature review. Journal of Pain and Symptom Management, 41(6), 1073-1093. doi:10.1016/j.jpainsymman.2010.08.016.

Examples

# \donttest{
if (requireNamespace("ggplot2", quietly = TRUE)) {
  set.seed(100)
  dat <- data.frame(x1 = rnorm(120), x2 = rnorm(120))
  sim <- brs_sim(
    formula = ~ x1 + x2, data = dat,
    beta = c(0.1, -0.3, 0.2), phi = 0.2, ncuts = 100, repar = 2
  )
  fit <- brs(y ~ x1 + x2, data = sim, repar = 2)

  autoplot.brs(fit, type = "calibration")
  autoplot.brs(fit, type = "score_dist")
}

# }