dae-package: tools:::Rd_package_title("dae")

Description

tools:::Rd_package_description("dae")

Version: utils::packageDescription("dae", fields="Version")

Date: utils::packageDescription("dae", fields="Date")

Arguments

Index

(i) Data
`ABC.Interact.dat`	Randomly generated set of values indexed by
	three factors
`BIBDWheat.dat`	Data for a balanced incomplete block experiment
`Casuarina.dat`	Data for an experiment with rows and columns from
	Williams (2002)
`Exp249.munit.des`	Systematic, main-plot design for an experiment to be
	run in a greenhouse
`Fac4Proc.dat`	Data for a 2^4 factorial experiment
`LatticeSquare_t49.des`	A Lattice square design for 49 treatments
`McIntyreTMV.dat`	The design and data from McIntyre (1955) two-phase
	experiment
`Oats.dat`	Data for an experiment to investigate nitrogen response
	of 3 oats varieties
`Sensory3Phase.dat`	Data for the three-phahse sensory evaluation
	experiment in Brien, C.J. and Payne, R.W. (1999)
`Sensory3PhaseShort.dat`	Data for the three-phase sensory evaluation
	experiment in Brien, C.J. and Payne, R.W. (1999),
	but with short factor names
`SPLGrass.dat`	Data for an experiment to investigate the
	effects of grazing patterns on pasture
	composition
	(ii) Factor manipulation functions
	Forms a new or revised factor:
	`fac.combine`
Combines several factors into one	`fac.multinested`
Creates several factors, one for each level of a nesting.fac
and each of whose values are either generated within
those of the level of nesting.fac or using the values
of a nested.fac	`fac.nested`
Creates a factor, the nested factor, whose values are
generated within those of a nesting factor	`fac.recast`
Recasts a factor by modifying the values in the factor vector
and/or the levels attribute, possibly combining
some levels into a single level.	`fac.recode`
Recodes factor 'levels' using possibly nonunique
values in a vector. (May be deprecated in future.)	`fac.uselogical`
Forms a two-level factor from a logical object	`mpone`
Converts the first two levels of a factor into
the numeric values -1 and +1
Forms multiple new factors:
`fac.divide`	Divides a factor into several separate factors
`fac.gen`	Generate all combinations of several factors and,
	optionally, replicate them
`fac.genfactors`	Generate all combinations of the levels of the supplied
	factors, without replication
`fac.split`	Splits a factor whose levels consist of several delimited
	strings into several factors.
`fac.uncombine`	Cleaves a single factor, each of whose levels has delimited
	strings, into several factors using the separated strings.
	Operates on factors:
	`as.numfac`
Convert a factor to a numeric vector	`fac.match`
Match, for each combination of a set of columns
in 'x', the row that has the same combination
in 'table'
(iii) Design functions
Designing experiments:
`designLatinSqrSys`	Generate a systematic plan for a Latin Square design.
`designRandomize`	Randomize allocated to recipient factors to produce
	a layout for an experiment. It supersedes `fac.layout`.
`no.reps`	Computes the number of replicates for an experiment
`detect.diff`	Computes the detectable difference for an experiment
`power.exp`	Computes the power for an experiment
	Plotting designs:
	`blockboundaryPlot`
This function plots a block boundary on a plot
produced by 'designPlot'. It supersedes
blockboundary.plot.	`designBlocksGGPlot`
Adds block boundaries to a plot produced by `designGGPlot`.	`designGGPlot`
Plots labels on a two-way grid of coloured cells using `ggplot2`
to represent an experimental design.	`designPlot`
A graphical representation of an experimental design
using labels stored in a matrix.
It superseded design.plot.	`designPlotlabels`
Plots labels on a two-way grid using `ggplot2`.
Assessing designs:
`designAmeasures`	Calculates the A-optimality measures from the
	variance matrix for predictions.
`designAnatomy`	Given the layout for a design, obtain its anatomy via
	the canonical analysis of its projectors to show the
	confounding and aliasing inherent in the design.
`designTwophaseAnatomies`	Given the layout for a design and three structure formulae,
	obtain the anatomies for the (i) two-phase,
	(ii) first-phase, (iii) cross-phase, treatments, and
	(iv) combined-units designs.
`marginality.pstructure`	Extracts the marginality matrix from a
	`pstructure.object`
`marginality.pstructure`	Extracts a list containing the marginality matrices from
	a `pcanon.object`
`print.aliasing`	Prints an aliasing data.frame
`summary.pcanon`	Summarizes the anatomy of a design, being the
	decomposition of the sample space based on its
	canonical analysis.
	(iv) ANOVA functions
	`fitted.aovlist`
Extract the fitted values for a fitted model
from an aovlist object	`fitted.errors`
Extract the fitted values for a fitted model	`interaction.ABC.plot`
Plots an interaction plot for three factors	`qqyeffects`
Half or full normal plot of Yates effects	`resid.errors`
Extract the residuals for a fitted model	`residuals.aovlist`
Extract the residuals from an aovlist object	`strength`
Generate paper strength values	`tukey.1df`
Performs Tukey's
one-degree-of-freedom-test-for-nonadditivity	`yates.effects`
Extract Yates effects
(v) Matrix functions
Operates on matrices:
`elements`	Extract the elements of an array specified by
	the subscripts
`mat.dirprod`	Forms the direct product of two matrices
`mat.dirsum`	Forms the direct sum of a list of matrices
`mat.ginv`	Computes the generalized inverse of a matrix
`Zncsspline`	Forms the design matrix for fitting the
	random effects for a natural cubic smoothing
	spline.
	Compute variance matrices for
supplied variance component values:
`mat.random`	Calculates the variance matrix for the
	random effects from a mixed model, based
	on a formula or a supplied matrix
`mat.Vpred`	Forms the variance matrix of predictions
	based on supplied matrices
`mat.Vpredicts`	Forms the variance matrix of predictions,
	based on supplied matrices or formulae.
	Forms matrices using factors
stored in a data.frame:
`fac.ar1mat`	Forms the ar1 correlation matrix for a
	(generalized) factor
`fac.sumop`	Computes the summation matrix that produces
	sums corresponding to a (generalized) factor
`fac.vcmat`	Forms the variance matrix for the variance
	component of a (generalized) factor
	Forms patterned matrices:
	`mat.I`
Forms a unit matrix	`mat.J`
Forms a square matrix of ones	`mat.ncssvar`
Forms a variance matrix for random cubic
smoothing spline effects
Forms correlation matrices:
`mat.cor`	Forms a correlation matrix in which all
	correlations have the same value
`mat.corg`	Forms a general correlation matrix in which
	all correlations have different values
`mat.ar1`	Forms an ar1 correlation matrix
`mat.ar2`	Forms an ar2 correlation matrix
`mat.ar3`	Forms an ar3 correlation matrix
`mat.arma`	Forms an arma correlation matrix
`mat.banded`	Forms a banded matrix
`mat.exp`	Forms an exponential correlation matrix
`mat.gau`	Forms a gaussian correlation matrix
`mat.ma1`	Forms an ma1 correlation matrix
`mat.ma2`	Forms an ma2 correlation matrix
`mat.sar`	Forms an sar correlation matrix
`mat.sar2`	Forms an sar2 correlation matrix
	(vi) Projector and canonical efficiency functions

Projector class:
`projector`	Create projectors
`projector-class`	Class projector
`is.projector`	Tests whether an object is a valid object of
	class projector
`print.projector`	Print projectors
`correct.degfree`	Check the degrees of freedom in an object of
	class projector
`degfree`	Degrees of freedom extraction and replacement
	Accepts two or more formulae:
	`designAnatomy`
An anatomy of a design, obtained from
a canonical analysis of the relationships
between sets of projectors.	`summary.pcanon`
Summarizes the anatomy of a design, being the
decomposition of the sample space based on its
canonical analysis	`print.summary.pcanon`
Prints the values in an 'summary.pcanon' object	`efficiencies.pcanon`
Extracts the canonical efficiency factors from a
list of class 'pcanon'
Accepts exactly two formulae:
`projs.2canon`	A canonical analysis of the relationships between
	two sets of projectors
`projs.combine.p2canon`	Extract, from a p2canon object, the projectors
`summary.p2canon`	A summary of the results of an analysis of
	the relationships between two sets of projectors
`print.summary.p2canon`	Prints the values in an 'summary.p2canon' object
	that give the combined decomposition
`efficiencies.p2canon`	Extracts the canonical efficiency factors from
	a list of class 'p2canon'
	Accepts a single formula:
	`as.data.frame.pstructure`
Coerces a pstructure.object to a data.frame	`print.pstructure`
Prints a pstructure.object	`pstructure.formula`
Takes a formula and constructs a pstructure.object
that includes the orthogonalized projectors for the
terms in a formula	`porthogonalize.list`
Takes a list of `projectors` and constructs
a `pstructure.object` that includes projectors,
each of which has been orthogonalized to all projectors
preceding it in the list.	Others:
	`decomp.relate`
Examines the relationship between the
eigenvectors for two decompositions	`efficiency.criteria`
Computes efficiency criteria from a set of
efficiency factors	`fac.meanop`
Computes the projection matrix that produces means	`proj2.eigen`
Canonical efficiency factors and eigenvectors
in joint decomposition of two projectors	`proj2.efficiency`
Computes the canonical efficiency factors for
the joint decomposition of two projectors	`proj2.combine`
Compute the projection and Residual operators
for two, possibly nonorthogonal, projectors	`show-methods`
Methods for Function 'show' in Package dae
(vii) Miscellaneous functions
`extab`	Expands the values in table to a vector
`get.daeTolerance`	Gets the value of daeTolerance for the package dae
`harmonic.mean`	Calcuates the harmonic mean.
`is.allzero`	Tests whether all elements are approximately zero
`rep.data.frame`	Replicate the rows of a data.frame by repeating
	each row consecutively and/or repeating all rows
	as a group.
`rmvnorm`	Generates a vector of random values from a
	multivariate normal distribution
`set.daeTolerance`	Sets the value of daeTolerance for the package dae

Author

tools:::Rd_package_author("dae")

Maintainer: tools:::Rd_package_maintainer("dae")