R: Methods for slm objects

slm.methods {SparseM} | R Documentation

## 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

### 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]

[Package SparseM version 1.77 Index]