wle (version 0.5)

wle.lm.summaries: Accessing Linear Model Fits for wle.lm

Description

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

Usage

coef.wle.lm(object, ...)
formula.wle.lm(object, ...)
fitted.wle.lm(object, ...)
model.frame.wle.lm(formula, data, na.action, ...)
summary.wle.lm(object, root="ALL", ...)
summary.wle.lm.root(object, root=1, ...)
print.wle.lm(x, digits = max(3, getOption("digits") - 3), ...)
print.summary.wle.lm(x, digits = max(3, getOption("digits") - 3), 
           signif.stars= getOption("show.signif.stars"),  ...)
print.summary.wle.lm.root(x, digits = max(3, getOption("digits") - 3), 
           signif.stars= getOption("show.signif.stars"),  ...)

Arguments

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 NAs are treated. The default is first, any na.action attribute of data, second a na.action setting of options, and third

root

the root to be printed, in summary.wle.lm it could be "ALL", all the roots are printed, or a vector of integers.

Value

• 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 $p \times 4$ 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 $(p, \sum{weights} - p, p*)$.
• 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.

code

wle.lm

Details

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.

See Also

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

Examples

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")