% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotting_utils.R
\name{plot_scatter_residuals}
\alias{plot_scatter_residuals}
\title{Plotting Residuals vs Predictions}
\usage{
plot_scatter_residuals(analysis_object)
}
\arguments{
\item{analysis_object}{Fitted analysis_object with 'fine_tuning()'.}
}
\value{
analysis_object
}
\description{
The \strong{plot_scatter_residuals()} function Visualizes residuals plotted
against fitted values to detect violations of ordinary least squares
assumptions including homoscedasticity (constant error variance), linearity,
and independence. Identifies heteroscedastic patterns (non-constant variance
across the predictor range), systematic curvature indicating omitted
polynomial terms, and outlier points with extreme residual magnitudes.
}
\examples{
# Note: For obtaining the residuals vs. predicted values plot the user needs
# to complete till fine_tuning( ) function of the MLwrap pipeline.
# See the full pipeline example under table_best_hyperparameters()
# Final call signature:
# plot_scatter_residuals(wrap_object)
}
\seealso{
\code{\link{table_best_hyperparameters}}
}
