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,
  seed = NULL
)

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.
seed: Optional random seed for reproducibility.

Value

A data frame with one row per variable and columns: variable, ame, std.error, ci.lower, ci.upper, model, type, and n.

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

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{
set.seed(11)
n <- 150
dat <- data.frame(x1 = rnorm(n), x2 = rnorm(n), z1 = rnorm(n))
sim <- brs_sim(
  formula = ~ x1 + x2 | z1, data = dat,
  beta = c(0.2, -0.5, 0.2), zeta = c(0.2, -0.3),
  ncuts = 100, repar = 2
)
fit <- brs(y ~ x1 + x2 | z1, data = sim, repar = 2)

brs_marginaleffects(fit, model = "mean", type = "response")
#>   variable         ame  std.error     ci.lower    ci.upper model     type   n
#> 1       x1 -0.09214702 0.02300201 -0.138337967 -0.04605006  mean response 150
#> 2       x2  0.04192221 0.02433643 -0.005902896  0.08775782  mean response 150
brs_marginaleffects(fit, model = "precision", type = "link")
#>   variable        ame std.error   ci.lower   ci.upper     model type   n
#> 1       z1 -0.4099968 0.1035667 -0.6102699 -0.2172345 precision link 150
# }