slm.methods {SparseM}

Methods for slm objects

Description

Summarize, print, and extract objects from slm objects.

Usage

## S3 method for class 'slm'
summary(object, correlation, ...)
## S3 method for class 'mslm'
summary(object, ...)
## S3 method for class 'slm'
print(x, digits, ...)
## S3 method for class 'summary.slm'
print(x, digits, symbolic.cor, signif.stars, ...)
## S3 method for class 'slm'
fitted(object, ...)
## S3 method for class 'slm'
residuals(object, ...)
## S3 method for class 'slm'
coef(object, ...)
## S3 method for class 'slm'
extractAIC(fit, scale = 0, k = 2, ...)
## S3 method for class 'slm'
deviance(object, ...)

Arguments

object, x, fit

object of class slm.

digits

minimum number of significant digits to be used for most numbers.

scale

optional numeric specifying the scale parameter of the model, see 'scale' in 'step'. Currently only used in the '"lm"' method, where 'scale' specifies the estimate of the error variance, and 'scale = 0' indicates that it is to be estimated by maximum likelihood.

k

numeric specifying the "weight" of the equivalent degrees of freedom ('edf') part in the AIC formula.

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.

Value

print.slm and print.summary.slm return invisibly. fitted.slm, residuals.slm, and coef.slm return the corresponding components of the slm object. extractAIC.slm and deviance.slm return the AIC and deviance values of the fitted object.

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/home.html

See Also

slm

Examples

data(lsq)
X <- model.matrix(lsq) #extract the design matrix
y <- model.response(lsq) # extract the rhs
X1 <- as.matrix(X)
slm.time <- system.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]