SparseM (version 0.52)

slm.fit: 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 $$\sum w_i*e_i^2$$ The length of weights must be the same as the number of observat

...

additional arguments.

Value

• A list of class slm consisting of:
• coef
  estimated coefficients
• chol
  cholesky object from fitting
• residuals
  residuals
• fitted
  fitted values
• terms
  terms
• call
  call
• ...

example

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)),"")

regression

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.

References

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

See Also

slm