R: Internal slm fitting functions

slm.fit {SparseM} | R Documentation

## Internal slm fitting functions

### Description

Fitting functions for sparse linear model fitting.

### Usage

    slm.fit(x,y,method, ...)
    slm.wfit(x,y,weights,...)
    slm.fit.csr(x, y, ...)

### Arguments

`x` | design matrix.
`y` | vector of response observations.
`method` | only `csr` is supported currently
`weights` | an optional vector of weights to be used in the fitting process. If specified, weighted least squares is used with weights ‘weights’ (that is, minimizing sum w e^2 The length of weights must be the same as the number of observations. The weights must be nonnegative and it is strongly recommended that they be strictly positive, since zero weights are ambiguous.
`...` | additional arguments.

### Details

`slm.fit` and `slm.wfit` call `slm.fit.csr` to do Cholesky decomposition and then backsolve to obtain the least squares estimated coefficients. These functions can be called directly if the user is willing to specify the design matrix in `matrix.csr` form. This is often advantageous in large problems to reduce memory requirements.

### Value

A list of class `slm` consisting of:

`coef` | estimated coefficients
`chol` | cholesky object from fitting
`residuals` | residuals
`fitted` | fitted values
`df.residual` | degrees of freedom
`terms` | terms
`call` | call

...

### Author(s)

Roger Koenker

### References

Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for R,
http://www.econ.uiuc.edu/~roger/research

### See Also

`slm`

### Examples

    data(lsq)
    X <- model.matrix(lsq) #extract the design matrix
    y <- model.response(lsq) # extract the rhs
    class(X) # -> "matrix.csr"
    class(y) # -> NULL
    slm.fit(X,y)->slm.fit.o # this is much more efficient in memory usage than slm()
    slm(y~as.matrix(X)-1) -> slm.o # this requires X to be transformed into dense mode
    cat("Difference between `slm.fit' and `slm' estimated coefficients =",
    	sum(abs(slm.fit.o$coef-slm.o$coef)),"\n")

[Package SparseM version 1.77 Index]