pproach to robust analysis in linear and generalized linear regression
models.
License GPL (version 2)
R topics documented:
ar
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bliss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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calcium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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carinsuk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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carr
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cellular
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chapman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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derailme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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dialectric
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forbes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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fwd.combn
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fwdglm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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fwdlm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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fwdsco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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hawkins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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lakes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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leafpine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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ar
lmsglm
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mice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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molar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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mussels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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ozone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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plot.fwdglm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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plot.fwdlm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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plot.fwdsco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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poison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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salinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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scglm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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score.s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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stackloss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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summary.fwdglm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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summary.fwdlm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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summary.fwdsco
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vaso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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wool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Index
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ar
ar data
Description
The ar data frame has 60 rows and 4 columns.
Usage
data(ar)
Format
This data frame contains the following columns:
x1 a numeric vector
x2 a numeric vector
x3 a numeric vector
y a numeric vector
Details
Source bliss
3
References
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New
York: Springer, Table A.2
Examples
bliss
Bliss data
Description
The bliss data frame has 8 rows and 4 columns.
Usage
data(bliss)
Format
This data frame contains the following columns:
Dose a numeric vector
Killed a numeric vector
Total a numeric vector
y a numeric vector
Details
Source
References
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New
York: Springer, Table A.20
Examples 4
calcium
calcium
Calcium data
Description
Calcium uptake of cells suspended in a solution of radioactive calcium.
The calcium data frame has 27 rows and 2 columns.
Usage
data(calcium)
Format
This data frame contains the following columns:
Time a numeric vector
y a numeric vector
Details
Source
References
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New
York: Springer, Table A.13
Examples carinsuk
5
carinsuk
Car insurance data
Description
The carinsuk data frame has 128 rows and 5 columns.
Usage
data(carinsuk)
Format
This data frame contains the following columns:
OwnerAge a factor with levels: 17-20, 21-24, 25-29, 30-34, 35-39, 40-49, 50-59, 60+
Model a factor with levels: A, B, C, D
CarAge a factor with levels: 0-3, 10+, 4-7, 8-9
NClaims a numeric vector
AvCost a numeric vector
Details
Source
References
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New
York: Springer, Table A.16
Examples 6
carr
carr
n-Pentane data
Description
Reaction rate for Catalytic Isomerization of n-Pentane to Isopentane
The carr data frame has 24 rows and 4 columns.
Usage
data(carr)
Format
This data frame contains the following columns:
x1 partial pressure of hydrogen
x2 partial pressure of n-pentane
x3 partial pressure of iso-pentane
y rate of disappearance of n-pentane
Details
Source
References
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New
York: Springer, Table A.15
Examples cellular
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cellular
Cellular differentiation data
Description
The cellular data frame has 16 rows and 3 columns.
Usage
data(cellular)
Format
This data frame contains the following columns:
TNF Dose of TNF (U/ml)
IFN Dose of IFN (U/ml)
y Number of cells differentiating
Details
Source
References
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New
York: Springer, Table A.19
Examples 8
chapman
chapman
Chapman data
Description
The chapman data frame has 200 rows and 7 columns.
Usage
data(chapman)
Format
This data frame contains the following columns:
age a numeric vector
highbp a numeric vector
lowbp a numeric vector
chol a numeric vector
height a numeric vector
weight a numeric vector
y a numeric vector
Details
Source
References
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New
York: Springer, Table A.24
Examples derailme
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derailme
British Train Accidents.
Description
The derailme data frame has 67 rows and 5 columns.
Usage
data(derailme)
Format
This data frame contains the following columns:
Month a numeric vector
Year a numeric vector
Type a numeric vector
TrainKm a numeric vector
y a numeric vector
Details
Source
References
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New
York: Springer, Table A.18
Examples 10
dialectric
dialectric
Dialectric data
Description
The dialectric data frame has 128 rows and 3 columns.
Usage
data(dialectric)
Format
This data frame contains the following columns:
time Time (weeks)
temp Temperature (rC)
y dialectric breakdown strength in kilovolts
Details
Source
References
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New
York: Springer, Table A.17
Examples forbes
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forbes
Forbes data
Description
Forbes data on air pressure in the Alps and the boiling point of water.
The forbes data frame has 17 rows and 2 columns.
Usage
data(forbes)
Format
This data frame contains the following columns:
x Boiling point
y 100 log(pressure)
Details
Source
References
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New
York: Springer, Table A.1
Examples 12
fwd.combn
fwd.combn
Generate all combinations of elements of x taken m at a time
Description
Generate all combinations of the elements of x taken m at a time. If x is a positive integer, returns
all combinations of the elements of seq(x) taken m at a time. If argument fun is not null, applies
a function given by the argument to each point. If simplify is FALSE, returns a list; else returns
a vector or an array. Optional arguments ... are passed unchanged to the function given by
argument fun, if any.
Usage
fwd.combn(x, m, fun = NULL, simplify = TRUE, ...)
fwd.nCm(n, m, tol = 1e-08)
Arguments
x
a vector or a single value.
n
a positive integer.
m
a positive integer.
fun
a function to be applied to each combination.
simplify
logical, if TRUE returns a vector or an array, otherwise a list.
tol
optional, tolerance value.
...
optional arguments passed to fun.
Value
Returns a vector or an array if simplify = TRUE, otherwise a list.
Note
Renamed by Kjell Konis for inclusion in the Forward Library 11/2002
Author(s)
Scott Chasalow
References
Nijenhuis, A. and Wilf, H.S. (1978) Combinatorial Computers and Calculators. NY: Academic
Press. fwdglm
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Examples
fwd.combn(letters[1:4], 2)
fwd.combn(10, 5, min)
# minimum value in each combi