| slm.methods {SparseM} | R Documentation |
Summarize, print, and extract objects from slm objects.
summary.slm(object, correlation, ...) summary.mslm(object, ...) print.slm(x, digits, ...) print.summary.slm(x, digits, symbolic.cor, signif.stars, ...) fitted.slm(object, ...) residuals.slm(object, ...) coef.slm(object, ...)
object,x |
object of class slm. |
digits |
minimum number of significant digits to be used for most numbers. |
symbolic.cor |
logical; if TRUE, the correlation of coefficients
will be printed. The default is FALSE |
signif.stars |
logical; if TRUE, P-values are additionally encoded
visually as ``significance stars'' in order to help scanning
of long coefficient tables. It defaults to the
`show.signif.stars' slot of `options'. |
correlation |
logical; if TRUE, the correlation matrix of the
estimated parameters is returned and printed. |
... |
additional arguments passed to methods. |
print.slm and print.summary.slm return invisibly.
fitted.slm, residuals.slm, and coef.slm
return the corresponding components of the slm object.
Roger Koenker
Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for R,
http://www.econ.uiuc.edu/~roger/research
slm
data(lsq)
X <- model.matrix(lsq) #extract the design matrix
y <- model.response(lsq) # extract the rhs
X1 <- as.matrix(X)
slm.time <- unix.time(slm(y~X1-1) -> slm.o) # pretty fast
cat("slm time =",slm.time,"\n")
cat("slm Results: Reported Coefficients Truncated to 5 ","\n")
sum.slm <- summary(slm.o)
sum.slm$coef <- sum.slm$coef[1:5,]
sum.slm
fitted(slm.o)[1:10]
residuals(slm.o)[1:10]
coef(slm.o)[1:10]