% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/btl.R
\name{plot_btl_icc}
\alias{plot_btl_icc}
\title{Plot an object characteristic curve}
\usage{
plot_btl_icc(fit, object, group = NULL, grid = NULL, min_n = 10)
}
\arguments{
\item{fit}{An object from \code{\link{btl}}.}

\item{object}{Object name.}

\item{group}{Optional judge grouping for a DIF overlay: either one value
per comparison row of \code{fit$comparisons} or a vector named by
judge. Observed means are then drawn separately per group, as
\code{\link{plot_icc}} draws person groups.}

\item{grid}{Opponent-location grid, in logits.}

\item{min_n}{An opponent's observed point is drawn only when the object
(or, in the grouped display, that judge group) met it at least this many
times; sparser pairs from incomplete or unbalanced designs are omitted.}
}
\value{
Called for its plotting side effect; invisibly the names of the
  opponents drawn (the ungrouped display), or \code{NULL} for the grouped
  display.
}
\description{
The paired-comparison counterpart of the item characteristic curve: the
model expected response for one object as a function of opponent
location (the win probability, or the expected graded response), with
the observed mean response against each opponent overlaid at that
opponent\'s estimated location. Observed points shrink in toward the
curve as the model holds; an object of inconsistent quality shows
points straying from it, exactly as a misfitting item does.
}
\examples{
set.seed(1)
beta <- c(A = -1, B = -0.3, C = 0.4, D = 0.9)
pr <- t(combn(names(beta), 2))
d <- data.frame(a = rep(pr[, 1], each = 30), b = rep(pr[, 2], each = 30))
p <- plogis(beta[d$a] - beta[d$b])
d$win <- ifelse(runif(nrow(d)) < p, d$a, d$b)
plot_btl_icc(btl(d, "a", "b", winner = "win"), "C")
}
