% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/model-engines.R
\name{fit_gazepoint_model}
\alias{fit_gazepoint_model}
\title{Fit a governed Gazepoint model}
\usage{
fit_gazepoint_model(
  data,
  task,
  predictors = NULL,
  engine = NULL,
  preprocessor = NULL,
  preprocessor_args = list(),
  engine_args = list(),
  seed = 1L,
  threshold = 0.5
)
}
\arguments{
\item{data}{Analysis data used to fit the model.}

\item{task}{A governed \code{gp3ml_task} object.}

\item{predictors}{Optional character vector of predictor columns.}

\item{engine}{Engine name or controlled custom-engine object.}

\item{preprocessor}{Optional fitted preprocessing object.}

\item{preprocessor_args}{Arguments passed to preprocessing fitting.}

\item{engine_args}{Arguments passed to the model engine.}

\item{seed}{Deterministic random seed.}

\item{threshold}{Classification probability threshold.}
}
\value{
A governed \code{gp3ml_model} object containing the fitted engine, preprocessing object, task contract, predictors, and training metadata.
}
\description{
Fit a governed Gazepoint model
}
\examples{
example_data <- data.frame(
  participant_id = rep(sprintf("P\%02d", 1:12), each = 2),
  trial_id = sprintf("T\%02d", 1:24),
  stimulus_id = rep(c("S01", "S02"), 12),
  condition = rep(c("A", "B"), 12),
  fixation_duration = 180 + seq_len(24),
  pupil_change = sin(seq_len(24) / 3),
  stringsAsFactors = FALSE
)
example_data$quality_status <- factor(
  c(
    "pass", "review", "pass", "review", "review", "pass",
    "review", "pass", "pass", "review", "review", "pass",
    "review", "pass", "review", "pass", "pass", "review",
    "pass", "review", "review", "pass", "pass", "review"
  ),
  levels = c("pass", "review")
)
task <- declare_gazepoint_task(
  data = example_data,
  outcome = "quality_status",
  purpose = "Predict predefined recording-quality review status",
  task_type = "classification",
  unit_id = "trial_id",
  participant_id = "participant_id",
  stimulus_id = "stimulus_id",
  generalization_target = "new_participants",
  positive = "review"
)
model <- fit_gazepoint_model(
  data = example_data,
  task = task,
  predictors = c("fixation_duration", "pupil_change"),
  engine = "glm",
  seed = 101L
)
model
}
