% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/savvy_glm2.R
\name{savvy_glm2}
\alias{savvy_glm2}
\title{Generalized Linear Models with Slab and Shrinkage Estimators}
\usage{
savvy_glm2(formula, family = gaussian, data, weights,
                 model_class = c("St", "DSh", "SR", "GSR", "LW", "QIS", "Sh"), subset,
                 na.action, start = NULL, etastart, mustart, offset,
                 control = list(...), model = TRUE,
                 method = "savvy_glm.fit2", x = FALSE, y = TRUE,
                 contrasts = NULL, use_parallel = FALSE, use_robust_start = FALSE, ...)
}
\arguments{
\item{formula}{An object of class "formula": a symbolic description of the model to be fitted. As for \code{\link[glm2]{glm2}}.}

\item{family}{A description of the error distribution and link function to be used in the model. As for \code{\link[glm2]{glm2}}.}

\item{data}{An optional data frame, list or environment containing the variables in the model. As for \code{\link[glm2]{glm2}}.}

\item{weights}{An optional vector of weights to be used in the fitting process. As for \code{\link[glm2]{glm2}}.}

\item{model_class}{A character vector specifying the shrinkage method(s) to be used in the underlying fitter \code{savvy_glm.fit2}.
Allowed values are \code{"St"}, \code{"DSh"}, \code{"SR"}, \code{"GSR"}, \code{"LW"}, \code{"QIS"}, and \code{"Sh"}. If a single value is provided, only that method is executed.
If multiple values are provided, the specified methods are evaluated (in parallel if \code{use_parallel = TRUE}) and the one with the lowest AIC is returned.
By default, the value is \code{c("St", "DSh", "SR", "GSR", "LW", "QIS")}; note that the \code{"Sh"} method is considered only if explicitly included.}

\item{subset}{An optional vector specifying a subset of observations to be used in the fitting process. As for \code{\link[glm2]{glm2}}.}

\item{na.action}{A function which indicates what should happen when the data contain NAs. As for \code{\link[glm2]{glm2}}.}

\item{start}{Starting values for the parameters in the linear predictor. As for \code{\link[glm2]{glm2}}. If \code{NULL} and \code{use_robust_start = TRUE}, robust optimization-based starting values may be calculated automatically for fragile link functions (see Details).}

\item{etastart}{Starting values for the linear predictor. As for \code{\link[glm2]{glm2}}.}

\item{mustart}{Starting values for the vector of means. As for \code{\link[glm2]{glm2}}.}

\item{offset}{An optional vector specifying a priori known component to be included in the linear predictor during fitting. As for \code{\link[glm2]{glm2}}.}

\item{control}{A list of control parameters to pass to the iterative fitting process. As for \code{\link[glm2]{glm2}}.}

\item{model}{A logical value indicating whether the model frame should be included as a component of the returned value. As for \code{\link[glm2]{glm2}}.}

\item{method}{The method to be used in fitting the model. The default is \code{savvy_glm.fit2}. This method uses IRLS
with custom optimization methods to ensure better convergence by evaluating different fitting methods and selecting the best one based on AIC.
As in \code{\link[glm2]{glm2}}, the alternative method "model.frame" returns the model frame and does no fitting.}

\item{x}{A logical value indicating whether the model matrix used in the fitting process should be returned as a component of the returned value. As for \code{\link[glm2]{glm2}}.}

\item{y}{A logical value indicating whether the response vector used in the fitting process should be returned as a component of the returned value. As for \code{\link[glm2]{glm2}}.}

\item{contrasts}{An optional list. See the contrasts.arg of \code{model.matrix.default}. As for \code{\link[glm2]{glm2}}.}

\item{use_parallel}{A logical value specifying whether to evaluate multiple shrinkage methods in parallel.
Defaults to \code{FALSE}. Setting this to \code{FALSE} forces sequential evaluation.}

\item{use_robust_start}{Logical. If \code{TRUE}, uses an optimization-based approach (via the \pkg{CVXR} package) to calculate robust starting values for fragile link functions (e.g., "log", "sqrt"). Defaults to \code{FALSE} to save computational time, as standard initialization works well for most typical datasets.}

\item{...}{Additional arguments to be passed to the low level regression fitting functions. As for \code{\link[glm2]{glm2}}.}
}
\value{
The value returned by \code{savvy_glm2} has exactly the same structure as that returned by \code{glm2}, except for:
\item{method}{the name of the fitter function used, which by default is \code{savvy_glm.fit2}.}
\item{chosen_fit}{the name of the chosen fitting method based on AIC.}
}
\description{
\code{savvy_glm2} extends the classical \code{glm2} function from the \pkg{glm2} package by embedding a set of shrinkage-based methods within the iteratively reweighted least squares (IRLS) algorithm.
These shrinkage methods (implemented via \code{savvy_glm.fit2}) are designed to improve convergence and estimation accuracy.
The user can specify one or more methods through the \code{model_class} argument. When multiple methods are provided (default is \code{c("St", "DSh", "SR", "GSR", "LW", "QIS")}),
the function can evaluate them in parallel (controlled by the \code{use_parallel} argument) and selects the final model based on the lowest AIC.
}
\details{
\code{savvy_glm2} improves upon the standard Generalized Linear Model (GLM) fitting process by incorporating
shrinkage estimator functions (\code{St_ost}, \code{DSh_ost}, \code{SR_ost}, \code{GSR_ost}, \code{LW_est}, \code{QIS_est}, and optionally \code{Sh_ost})
within the IRLS algorithm. The function begins with initial parameter estimates and iteratively updates the coefficients using the
specified shrinkage methods. When multiple methods are specified in \code{model_class}, they may be evaluated in parallel (if
\code{use_parallel = TRUE}), and the final model is selected based on the lowest AIC. In cases where any candidate model returns an \code{NA}
or non-finite AIC (such as when using quasi-likelihood families), the deviance is used uniformly as the selection criterion.

\strong{Robust Starting Values:}
In situations where the user does not provide \code{start}, \code{etastart}, or \code{mustart}, and the chosen \code{family} utilizes specific link functions
(such as \code{"log"}, \code{"sqrt"}, \code{"inverse"}, \code{"1/mu^2"}, or \code{"logit"}), standard GLM initialization can sometimes lead to infinite deviance or divergence.
If \code{use_robust_start = TRUE}, the underlying \code{savvy_glm.fit2} automatically employs a data-driven optimization approach using the \pkg{CVXR} package to calculate stable starting coefficients.
}
\examples{
set.seed(123)
n <- 200
p <- 5
X <- matrix(rnorm(n * p), n, p)
colnames(X) <- paste0("X", 1:p)

linear_predictor <- X \%*\% c(0.5, -0.5, 0.2, 0, 0)
prob <- 1 / (1 + exp(-linear_predictor))
y <- rbinom(n, 1, prob)

df <- data.frame(y = y, X)
fit1 <- savvy_glm2(y ~ ., data = df,
                   family = binomial(link = "logit"),
                   model_class = c("St", "DSh", "LW"),
                   use_parallel = FALSE)
print(fit1$chosen_fit)
print(coef(fit1))

eta <- abs(X \%*\% c(0.5, -0.2, 0.1, 0, 0)) + 1
mu_quad <- eta^2
y_quad <- rpois(n, lambda = mu_quad)
df_quad <- data.frame(y = y_quad, X)

fit2 <- savvy_glm2(y ~ ., data = df_quad,
                   family = poisson(link = "sqrt"),
                   model_class = c("SR", "St", "QIS"),
                   use_robust_start = TRUE,
                   use_parallel = FALSE)
print(fit2$chosen_fit)
print(coef(fit2))

}
\references{
Marschner, I. C. (2011). \emph{glm2: Fitting Generalized Linear Models with Convergence Problems}.
The R Journal, 3(2), 12–15. \doi{10.32614/RJ-2011-012}

Asimit, V., Avramescu, O., Chen, Z., Rivas, D., & Senatore, C. (2026). \emph{GLM Solutions via Shrinkage}.

Asimit, V., Cidota, M. A., Chen, Z., & Asimit, J. (2025). \emph{Slab and Shrinkage Linear Regression Estimation}.
Retrieved from \url{https://openaccess.city.ac.uk/id/eprint/35005/}.

Ledoit, O. and Wolf, M. (2004). \emph{A well-conditioned estimator for large-dimensional covariance matrices}.
Journal of Multivariate Analysis, 88(2):365–411. \doi{10.1016/S0047-259X(03)00096-4}

Ledoit, O. and Wolf, M. (2022). \emph{Quadratic shrinkage for large covariance matrices}.
Bernoulli, 28(3): 1519-1547. \doi{10.3150/20-BEJ1315}
}
\seealso{
\code{\link[stats]{glm}}, \code{\link[glm2]{glm2}}, \code{\link{savvy_glm.fit2}}
}
\author{
Ziwei Chen, Vali Asimit and Claudio Senatore\cr
Maintainer: Ziwei Chen <Ziwei.Chen.3@citystgeorges.ac.uk>
}
