Predict pairwise win probabilities from an alpha-adjusted Bradley-Terry fit

Calculate plogis(theta1 - theta2), the probability that the first item wins. These plug-in probabilities do not integrate over uncertainty and have no tie, lapse, or positional parameter.

Usage

# S3 method for class 'pairwiseLLM_bt_alpha'
predict(object, newdata = NULL, ...)

Arguments

object

An alpha-adjusted fit from fit_bt_model() with engine = "alpha".

newdata

A data frame containing object1 and object2 item IDs. NULL uses the original comparisons in their original order. IDs must be nonmissing and present in the fitted model. Repeated pairs are allowed; comparing an item with itself returns 0.5.

...

Reserved; additional arguments are rejected.

Value

A numeric vector of first-item win probabilities in input row order.

See also

fit_bt_model(), summarize_bt_fit()

Other frequentist models: bootstrap_bt_model(), build_bt_data(), build_elo_data(), fit_bt_model(), fit_elo_model(), predict.pairwiseLLM_bt_firth(), predict.pairwiseLLM_bt_lapse(), scale_separation_reliability(), summarize_bt_fit()

Examples

comparisons <- data.frame(object1 = c("a", "a", "b"),
                          object2 = c("b", "c", "c"), result = c(1, 1, 1))
fit <- fit_bt_model(comparisons, engine = "alpha", alpha = 0.5)
predict(fit)
#> [1] 0.7586094 0.9080573 0.7586094
predict(fit, data.frame(object1 = "c", object2 = "a"))
#> [1] 0.09194274