Create a table of downweighted observations based on a rrfit object within a cgOneFactorFit object. A cgOneFactorDownweightedTable class object is created.
A fit object of class
It has no default and must be specified as a numeric between 0 and 1
exclusive. It is a threshold. All
observations that fall beneath the threshold will be
identified. For example, a
One of three valid values:
Additional arguments. None are currently defined for this method.
If no observations meet the cutoff criteria, a text message of the
cgOneFactorDownweightedTable content emptiness is output
The reported weights are in the scale of the observation, not the
sum of squared errors representation for the likelihood. Thus they are
derived from the square root of the
$w component from
rlm fit object.
An object of class
cgOneFactorDownweightedTable, with the
A data frame where each row is an observation from the fitted data set that meets the cutoff criteria, and these columns:
The group identified from the fitted data.
The observed response value.
The weight associated to the observation from the resistant / robust fit.
An expression of the weight in terms of percent reduction from the maximum of 1.
If no observations meet the cutoff criteria,
contents slot is set to
Taken from the specified
A list of settings carried from the
cgOneFactorFit object, and the addition
of the specified
cutoffwt argument in the method call above. These are used
invoked for example when
Contact firstname.lastname@example.org for bug reports, questions, concerns, and comments.
Bill Pikounis [aut, cre, cph], John Oleynick [aut], Eva Ye [ctb]
Venables, W. N. and Ripley, B. D. (2002), Modern Applied Statistics with S. Fourth edition. Springer.
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data(canine) canine.data <- prepareCGOneFactorData(canine, format="groupcolumns", analysisname="Canine", endptname="Prostate Volume", endptunits=expression(plain(cm)^3), digits=1, logscale=TRUE, refgrp="CC") canine.fit <- fit(canine.data) canine.dwtable <- downweightedTable(canine.fit, cutoff=0.95) downweightedTable(canine.fit, cutoff=0.75) ## No observation ## downweighted at least 25%
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