wle.lm.summaries: Accessing Linear Model Fits for wle.lm
In wle: Weighted Likelihood Estimation

Description Usage Arguments Details Value Author(s) See Also Examples

All these functions are methods for class wle.lm or summary.wle.lm.

## S3 method for class 'wle.lm'
coef(object, ...)
## S3 method for class 'wle.lm'
formula(x, ...)
## S3 method for class 'wle.lm'
fitted(object, ...)
## S3 method for class 'wle.lm'
model.frame(formula, data, na.action, ...)
## S3 method for class 'wle.lm'
summary(object, root="ALL", ...)
## S3 method for class 'wle.lm.root'
summary(object, root=1, ...)

## S3 method for class 'wle.lm'
print(x, digits = max(3, getOption("digits") - 3), ...)

## S3 method for class 'summary.wle.lm'
print(x, digits = max(3, getOption("digits") - 3), 
           signif.stars= getOption("show.signif.stars"),  ...)

## S3 method for class 'summary.wle.lm.root'
print(x, digits = max(3, getOption("digits") - 3), 
           signif.stars= getOption("show.signif.stars"),  ...)

`object`	an object of class `wle.lm`.
`x`	an object of class `wle.lm` or `summary.wle.lm`.
`formula`	a model formula
`data`	`data.frame`, list, `environment` or object coercible to `data.frame` containing the variables in `formula`.
`na.action`	how `NA`s are treated. The default is first, any `na.action` attribute of `data`, second a `na.action` setting of `options`, and third `na.fail` if that is unset. The “factory-fresh” default is `na.omit`.
`root`	the root to be printed, in summary.wle.lm it could be "ALL", all the roots are printed, or a vector of integers.
`digits`	number of digits to be used for most numbers.
`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`.
`...`	additional arguments.

print.summary.wle.lm and print.summary.wle.lm.root tries formatting for each root the coefficients, standard errors, etc. and additionally gives “significance stars” if signif.stars is TRUE.

The generic accessor functions coefficients, fitted.values, residuals and weights can be used to extract various useful features of the value returned by wle.lm.

The function summary.wle.lm (the summary.wle.lm.root do the same for just one selected root) computes and returns, for each selected root, a list of summary statistics of the fitted linear model given in object, using the components (list elements) "call" and "terms" from its argument, plus

`residuals`	the weighted residuals, the usual residuals rescaled by the square root of the weights given by `wle.lm`.
`coefficients`	a p x 4 matrix with columns for the estimated coefficient, its standard error, weighted-t-statistic and corresponding (two-sided) p-value.
`sigma`	the square root of the estimated variance of the random error.
`df`	degrees of freedom, a 3-vector (p, ∑{weights} - p, p)*.
`fstatistic`	a 3-vector with the value of the weighted-F-statistic with its numerator and denominator degrees of freedom.
`r.squared`	R^2, the “fraction of variance explained by the model”.
`adj.r.squared`	the above R^2 statistic “adjusted”, penalizing for higher p.
`root`	the label of the root reported.

Claudio Agostinelli

wle.lm a function for estimating linear models with normal distribution error and normal kernel, plot.wle.lm for plot method.

library(wle)
# You can find this data set in:
# Hawkins, D.M., Bradu, D., and Kass, G.V. (1984). 
# Location of several outliers in multiple regression data using
# elemental sets. Technometrics, 26, 197-208.
#
data(artificial)

result <- wle.lm(y.artificial~x.artificial,boot=40,group=6,num.sol=3)

#summary only for the first root
summary(result,root=1)
#summary for all the roots
summary(result,root="ALL")