manylm.fit {mvabund} | R Documentation |
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.
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, ...)
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. |
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) . |
Ulrike Naumann and David Warton <David.Warton@unsw.edu.au>.