Marginal effects for brs models — brs_marginaleffects • betaregscale

Computes average marginal effects (AME) for numeric covariates in the mean or precision submodel of a fitted "brs" object.

Usage

brs_marginaleffects(
  object,
  newdata = NULL,
  model = c("mean", "precision"),
  type = c("response", "link"),
  variables = NULL,
  h = 1e-05,
  interval = TRUE,
  level = 0.95,
  n_sim = 400L,
  keep_draws = FALSE
)

Arguments

object: A fitted "brs" object.
newdata: Optional data frame for evaluation; defaults to the data used in fitting.
model: Character; "mean" (default) or "precision".
type: Character prediction scale: "response" (default) or "link".
variables: Optional character vector of covariate names. Defaults to all numeric covariates in the selected submodel.
h: Finite-difference step for non-binary numeric covariates.
interval: Logical; compute interval estimates via simulation.
level: Confidence level for interval estimates.
n_sim: Number of parameter draws when interval = TRUE.
keep_draws: Logical; if TRUE and interval = TRUE, stores AME simulation draws in attribute "ame_draws".

Value

A data frame with one row per variable and columns: variable, ame, std.error, ci.lower, ci.upper, model, type, and n. The returned object has class "brs_marginaleffects" and attributes with analysis metadata.

Details

AMEs are computed by finite differences on predictions: $$ \mathrm{AME}_j = \frac{1}{n}\sum_{i=1}^{n} \frac{\hat{g}_i(x_{ij} + h) - \hat{g}_i(x_{ij})}{h}, $$ where $\hat{g}_i$ is the selected prediction scale.

For binary covariates coded as 0/1, the effect is computed as the average discrete difference $\hat{g}(x_j=1)-\hat{g}(x_j=0)$.

If interval = TRUE, uncertainty is approximated by asymptotic parameter simulation from $\mathcal{N}(\hat{\theta}, \hat{V})$.

References

Lopes, J. E. (2023). Modelos de regressao beta para dados de escala. Master's dissertation, Universidade Federal do Parana, Curitiba. URI: https://hdl.handle.net/1884/86624.

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{
dat <- data.frame(
  y = c(
    0, 5, 20, 50, 75, 90, 100, 30, 60, 45,
    10, 40, 55, 70, 85, 25, 35, 65, 80, 15
  ),
  x1 = rep(c(1, 2), 10),
  x2 = rep(c(0, 0, 1, 1), 5)
)
prep <- brs_prep(dat, ncuts = 100)
#> brs_prep: n = 20 | exact = 0, left = 1, right = 1, interval = 18
fit <- brs(y ~ x1, data = prep)
brs_marginaleffects(fit, model = "mean", type = "response")
#>   variable        ame std.error   ci.lower ci.upper model     type  n
#> 1       x1 -0.0548085 0.1306531 -0.3009406 0.206986  mean response 20
# }