| slm.fit {SparseM} | R Documentation |
Fitting functions for sparse linear model fitting.
slm.fit(x,y,method, ...) slm.wfit(x,y,weights,...) slm.fit.csr(x, y, ...)
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. |
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.
A list of class slm consisting of:
coef |
estimated coefficients |
chol |
cholesky object from fitting |
residuals |
residuals |
fitted |
fitted values |
terms |
terms |
call |
call |
...
Roger Koenker
Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for R,
http://www.econ.uiuc.edu/~roger/research
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")