dae-package: Functions Useful in the Design and ANOVA of Experiments
In dae: Functions Useful in the Design and ANOVA of Experiments

dae-package

R Documentation

Functions Useful in the Design and ANOVA of Experiments

Description

The content falls into the following groupings: (i) Data, (ii) Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v) Matrix functions, (vi) Projector and canonical efficiency functions, and (vii) Miscellaneous functions. There is a vignette describing how to use the design functions for randomizing and assessing designs available as a vignette called 'DesignNotes'. The ANOVA functions facilitate the extraction of information when the 'Error' function has been used in the call to 'aov'. The package 'dae' can also be installed from <http://chris.brien.name/rpackages/>.

Version: NA

Date: NA

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

`as.numfac`	Convert a factor to a numeric vector
`fac.combine`	Combines several factors into one
`fac.divide`	Divides a factor into several separate factors
`fac.gen`	Generate all combinations of several factors and,
	optionally, replicate them
`fac.gen`	Generate all combinations of the levels of the supplied
	factors, without replication
`fac.match`	Match, for each combination of a set of columns
	in 'x', the row that has the same combination
	in 'table'
`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.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.
`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

(iii) Design functions

`blockboundaryPlot`	This function plots a block boundary on a plot
	produced by 'designPlot'. It supersedes
	blockboundary.plot.
`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.
`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.
`designLatinSqrSys`	Generate a systematic plan for a Latin Square 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`.
`designRandomize`	Randomize allocated to recipient factors to produce
	a layout for an experiment. It supersedes `fac.layout`.
`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
`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
`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

`elements`	Extract the elements of an array specified by
	the subscripts
`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
`mat.I`	Forms a unit matrix
`mat.J`	Forms a square matrix of ones
`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.ncssvar`	Forms a variance matrix for random cubic
	smoothing spline effects
`mat.sar`	Forms an sar correlation matrix
`mat.sar2`	Forms an sar2 correlation matrix
`mat.dirprod`	Forms the direct product of two matrices
`mat.dirsum`	Forms the direct sum of a list of matrices
`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
`mat.Vpredicts`	Forms the variance matrix of predictions,
	based on supplied matrices or formulae.
`Zncsspline`	Forms the design matrix for fitting the
	random effects for a natural cubic smoothing
	spline.

(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
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