Predict a frozen-margin pair association

With no newdata or type, this method preserves the historical fitted()-style output of the two frozen margins. type = "link" returns the association linear predictor and type = "eta" returns its bounded latent-normal association transform; type = "response" is a compatibility alias for "eta". The beta Bernoulli x ordinary-NB2 route also admits new-data prediction from its fixed-effect association formula.

Usage

# S3 method for class 'drm_pair_association'
predict(
  object,
  newdata = NULL,
  type = NULL,
  se.fit = FALSE,
  interval = c("none", "confidence"),
  level = 0.95,
  ...
)

Arguments

object

A drm_pair_association object.

newdata

Optional data frame for beta Bernoulli x ordinary-NB2 association prediction. Its terms, factor levels, contrasts, and columns must match the fitted association formula.

type

Prediction scale: "link" for X_A %*% alpha or "eta" for 0.999999 * tanh(X_A %*% alpha). "response" is an alias for "eta". Omit type together with newdata to retain the historical frozen-margin fitted output.

se.fit

Logical; return pointwise standard errors on the requested scale. Eta-scale standard errors use the delta method.

interval

"none" or "confidence". Confidence limits are pointwise link-scale Wald limits transformed monotonically to eta when type = "eta".

level

Confidence level in (0, 1).

...

Must be empty.

Value

With omitted type and newdata, the frozen marginal fitted values. Without uncertainty, a numeric association prediction on the requested scale. With interval = "confidence", a matrix with fit, lwr, and upr columns. With se.fit = TRUE, a list containing fit and se.fit; fit is the three-column matrix when an interval is requested.

See also

vcov.drm_pair_association(), confint.drm_pair_association()

Examples

if (FALSE) { # \dontrun{
set.seed(20260801)
n <- 160
dat <- data.frame(
  x1 = seq(-1.2, 1.2, length.out = n),
  x2 = rep(c(-0.5, 0.5), length.out = n)
)
z_binary <- rnorm(n)
eta <- 0.999999 * tanh(-0.1 + 0.4 * dat$x1 - 0.2 * dat$x2)
z_count <- eta * z_binary + sqrt(1 - eta^2) * rnorm(n)
dat$binary <- as.integer(
  z_binary > qnorm(plogis(-0.2 + 0.3 * dat$x1), lower.tail = FALSE)
)
dat$count <- qnbinom(
  pnorm(z_count), mu = exp(0.5 + 0.2 * dat$x2), size = 4
)
binary_fit <- drmTMB(bf(mu = binary ~ x1), binomial(), dat)
count_fit <- drmTMB(
  bf(mu = count ~ x2, sigma = ~ 1), nbinom2(), dat
)
assoc <- associate_pairs(
  binary_fit, count_fit,
  kernel = latent_normal(), association = ~ x1 + x2
)
new_dat <- data.frame(x1 = c(-1, 0, 1), x2 = 0)
eta_prediction <- predict(
  assoc,
  newdata = new_dat,
  type = "eta",
  se.fit = TRUE,
  interval = "confidence"
)
eta_prediction$fit
eta_prediction$se.fit
} # }