manylm.fit: workhose functions for fitting multivariate linear models

In mvabund: Statistical Methods for Analysing Multivariate Abundance Data

Description

These are the workhorse functions called by manylm used to fit multivariate linear models. These should usually not be used directly unless by experienced users.

Usage

manylm.fit(x, y, offset = NULL, tol=1.0e-010, singular.ok = TRUE, ...)
manylm.wfit(x, y, w, offset = NULL, tol=1.0e-010, singular.ok = TRUE, ...)

Arguments

x	design matrix of dimension n * p.
y	matrix or an mvabund object of observations of dimension n*q.
w	vector of weights (length n) to be used in the fitting process for the manylm.wfit functions. Weighted least squares is used with weights w, i.e., sum(w * e^2) is minimized.
offset	numeric of length n). This can be used to specify an a priori known component to be included in the linear predictor during fitting.
tol	tolerance for the qr decomposition. Default is 1.0e-050.
singular.ok	logical. If FALSE, a singular model is an error.
...	currently disregarded.

Value

a list with components

coefficients	p vector
residuals	n vector or matrix
fitted.values	n vector or matrix

weights	n vector — only for the *wfit* functions.
rank	integer, giving the rank
qr	(not null fits) the QR decomposition.
df.residual	degrees of freedom of residuals
hat.X	the hat matrix.
txX	the matrix (t(x)%*%x).

Author(s)

Ulrike Naumann and David Warton <[e-mail]>.

See Also

manylm

mvabund documentation built on May 2, 2019, 6:10 p.m.