% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simplexreg_residuals.R
\name{residuals.simplexregression}
\alias{residuals.simplexregression}
\title{Residuals for Simplex Regression Models}
\usage{
\method{residuals}{simplexregression}(
  object,
  type = c("quantile", "pearson", "deviance", "standardized", "weighted", "variance",
    "biasvariance", "score", "dualscore", "response"),
  ...
)
}
\arguments{
\item{object}{An object of class \code{simplexregression}.}

\item{type}{Character string specifying the type of residual to extract.
Options:
\code{"quantile"} (default), \code{"pearson"}, \code{"deviance"},
\code{"standardized"}, \code{"weighted"}, \code{"variance"},
\code{"biasvariance"}, \code{"score"}, \code{"dualscore"}, \code{"response"}.}

\item{...}{Additional arguments (currently not used).}
}
\value{
A numeric vector of residuals.
}
\description{
Extracts various types of residuals for diagnostic analysis in
simplex regression models with parametric or fixed mean link functions.
}
\details{
Several types of residuals are available for model diagnostics:

\strong{Quantile residuals} (\code{"quantile"}): Proposed by Dunn and Smyth
(1996) as \eqn{r_i^Q = \Phi^{-1}(F(y_i; \hat{\mu}_i, \hat{\sigma}^2_i))},
where \eqn{\Phi(\cdot)} is the standard normal CDF and
\eqn{F(\cdot; \cdot)} is the simplex CDF (see \code{\link{psimplex}}).
Under correct model specification, these residuals are approximately standard
normal and are therefore recommended for general diagnostic use.

\strong{Pearson residuals} (\code{"pearson"}): Defined in McCullagh and
Nelder (1989) as \eqn{r_i^P = (y_i - \hat{\mu}_i) /
\sqrt{\widehat{\text{Var}}(y_i)}}, where \eqn{\widehat{\text{Var}}(y_i)} is
the estimated variance of the response
(see \code{\link{variance.simplex}}).

\strong{Deviance residuals} (\code{"deviance"}): Defined in Jørgensen (1997,
p. 115) as \eqn{r_i^D = (y_i - \hat{\mu}_i) /
(\hat{\mu}_i(1-\hat{\mu}_i)\sqrt{y_i(1-y_i)})}.

\strong{Standardized residuals} (\code{"standardized"}): Proposed by Espinheira
and Silva (2020, Eq. 15) as \eqn{r_i^\beta = \hat{u}_i(y_i - \hat{\mu}_i) /
\sqrt{\hat{\sigma}^2_i \hat{w}_i}}, where
\eqn{\hat{w}_i} and \eqn{\hat{u}_i} are weight functions.

\strong{Weighted residuals} (\code{"weighted"}): Proposed by Espinheira and
Silva (2020, Eq. 16) as \eqn{r_i^{\beta*} = \hat{u}_i(y_i - \hat{\mu}_i) /
\sqrt{\hat{\sigma}^2_i \hat{w}_i(1-\hat{h}_{ii})}}, where \eqn{\hat{h}_{ii}}
are the diagonal elements of the hat matrix (see \cr
\code{\link{hatvalues.simplexregression}}).
These residuals are recommended for simulated envelope plots.

\strong{Variance residuals} (\code{"variance"}): Proposed by Espinheira et al.
(2021, Eq. 7) as \eqn{r_i^\gamma = (\hat{d}_i - \hat{\sigma}^2_i) /
(\hat{\sigma}^2_i\sqrt{2})}, where \eqn{\hat{d}_i} is the estimated unit
deviance (see \code{\link{dev.unit.simplex }}).

\strong{Bias–variance residuals} (\code{"biasvariance"}): Proposed by Espinheira
et al. (2021, Eq. 8) as \eqn{r_i^{\beta \gamma} =
(\hat{u_i}(y_i - \hat{\mu}_i) + \hat{a}_i) / \sqrt{\hat{\sigma}^2_i
\hat{w}_i + 1/(2\hat{\sigma}^4_i)}}, where \eqn{\hat{a}_i} is a correction term.

\strong{Score residuals} (\code{"score"}): Defined in Jørgensen (1997, p. 115) as
\eqn{r_i^{S} = (y_i - \hat{\mu}_i)(\hat{\mu}_i^2 + y_i - 2y_i\hat{\mu}_i) /
(y_i(1-y_i)\hat{\mu}_i^{1.5}(1-\hat{\mu}_i)^{1.5})}.

\strong{Dual score residuals} (\code{"dualscore"}): Defined in Jørgensen
(1997, p. 115) as \eqn{r_i^{DS} = (y_i - \hat{\mu}_i)(y_i + \hat{\mu}_i -
2y_i\hat{\mu}_i) / (2\sqrt{y_i(1-y_i)}\hat{\mu}_i^2(1-\hat{\mu}_i)^2)}.

\strong{Response residuals} (\code{"response"}): Simple difference between
observed and fitted values, \eqn{r_i^R = y_i - \hat{\mu}_i}.

\strong{Recommendations}: Quantile residuals are recommended for general model
diagnostics due to their theoretical properties. Weighted residuals are
particularly useful for constructing simulated envelope plots, as they account
for both the variance structure and leverage effects.
}
\examples{
data(ReadingSkills, package = "SimplexRegression")
fit <- simplexreg(accuracy ~ dyslexia * iq | dyslexia + iq + I(iq^2),
                 data = ReadingSkills)

# Compute different types of residuals
res_quantile <- residuals(fit, type = "quantile")
res_pearson <- residuals(fit, type = "pearson")
res_weighted <- residuals(fit, type = "weighted")

}
\references{
McCullagh, P. and Nelder, J. A. (1989). \emph{Generalized Linear Models}.
2nd ed.  Chapman and Hall, London.
Jørgensen, B. (1997).
\emph{The Theory of Dispersion Models}.
Chapman and Hall, London.

Dunn, P. K. and Smyth, G. K. (1996). Randomized Quantile Residuals.
\emph{Journal of Computational and Graphical Statistics}, \bold{5}(3),
236-–244. \doi{10.2307/1390802}

Espinheira, P. L., Silva, L. C. M. and Cribari-Neto, F. (2021). Bias and
variance residuals for machine learning nonlinear simplex regressions.
\emph{Expert Systems With Applications}, \bold{185}, 115656.
\doi{10.1016/j.eswa.2021.115656}

Espinheira, P. L., Silva, A. O. (2020). Residual and influence analysis to a
general class of simplex regression. \emph{TEST}, \bold{29}, 523–-552.
\doi{10.1007/s11749-019-00665-3}
}
\seealso{
\code{\link{plot.simplexregression}}, \code{\link{halfnormal.plot}},
\code{\link{hatvalues.simplexregression}}.
}
