workhose functions for fitting multivariate linear models

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

Ulrike Naumann and David Warton <David.Warton@unsw.edu.au>.

See also

manylm