# manylm.fit: workhose functions for fitting multivariate linear models In mvabund: Statistical Methods for Analysing Multivariate Abundance Data

 manylm.fit R Documentation

## workhose functions for fitting multivariate linear models

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

`manylm`