# R/methods.r In SCGLR: Supervised Component Generalized Linear Regression

#### Documented in methodSR

```#'@title Regularization criterion types
#'@export
#'@param phi character string describing structura relevance used in the regularization process.
#' Allowed values are "vpi" for Variable Powered Inertia and "cv" for Component Variance. Default to "vpi".
#'@param l is an integer argument (>1) tuning the importance of variable bundle locality.
#'@param s is a numeric argument (in [0,1]) tuning the strength of structural relevance with respect to goodness of fit.
#'@param maxiter integer for maximum number of iterations of \code{SR} function
#'@param epsilon positive convergence threshold
#'@param bailout integer argument
methodSR <- function(phi="vpi",l=1,s=1/2,maxiter=1000,epsilon=1e-6,bailout=10) {
# check arguments
if(!(phi %in% c("vpi","cv")))
stop("phi should be \"vpi\" or \"cv\"")
if(!is.numeric(l) || l<1)
stop("l must be greater than 1")
if(!is.numeric(s) || s<0 || s>1)
stop("s must be between 0 and 1")
if(!is.numeric(maxiter) || maxiter<1)
stop("maxiter must be an integer greater than 1")
if(!is.numeric(epsilon) || epsilon<=0)
stop("epsilon must be a positive numeric")
if(!is.numeric(bailout) || bailout<1)
stop("bailout must be an integer greater than 1")

structure(list(
method="sr",
phi=phi,
l=l,
s=s,
maxiter=maxiter,
epsilon=epsilon,
bailout=bailout#1000
),
class="method.SCGLR",
description="Method iterative normed gradient (ING) for Structural Relevance"
)
}
```

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SCGLR documentation built on May 1, 2019, 8 p.m.