glm.test.control: Set up control object for GLM computations
In snpStats: SnpMatrix and XSnpMatrix classes and methods

Description Usage Arguments Details Value Author(s) See Also

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.

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

`maxit`	Maximum number of IRLS steps
`epsilon`	Convergence threshold for IRLS algorithm
`R2Max`	R-squared limit for aliasing of new terms

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.

Returns the parameters as a list in the expected order

David Clayton dc208@cam.ac.uk

snp.lhs.tests, snp.rhs.tests

snpStats documentation built on Nov. 8, 2020, 10:59 p.m.