manylm.fit: workhose functions for fitting multivariate linear models

Description Usage Arguments Value Author(s) See Also

View source: R/manylm.R

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

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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 <David.Warton@unsw.edu.au>.

See Also

manylm


mvabund documentation built on May 29, 2017, 9:50 a.m.