snpStats (version 1.22.0)

glm.test.control: Set up control object for GLM computations

Description

Several commands depend on fitting a generalized linear model (GLM), using the standard iteratively reweighted least squares (IRLS) algorithm. This function sets various control parameters for this.

Usage

glm.test.control(maxit = 20, epsilon = 1.e-5, R2Max = 0.99)

Arguments

maxit

Maximum number of IRLS steps

epsilon

Convergence threshold for IRLS algorithm

R2Max

R-squared limit for aliasing of new terms

Value

Returns the parameters as a list in the expected order

Details

Sometimes (although not always), an iterative scheme is necessary to fit a generalized linear model (GLM). The maxit parameter sets the maximum number of iterations to be carried out, while the epsilon parameter sets the criterion for determining convergence. Variables which are judged to be "aliased" are dropped. A variable is judged to be aliased if RSS/TSS is less than (1-R2Max), where

• RSS is the residual (weighted) sum of squares from the regression of that variable on the variables which precede it in the model formula (and any stratification defined in a strata() call in th emodel formula), and
• TSS is the total (weighted) sum of squared deviations of this variable from its mean (or, when a strata() call is present, from its stratum-specific means).

The weights used in this calculation are the "working" weights of the IRLS algorithm.

See Also

snp.lhs.tests, snp.rhs.tests