Description Details Author(s) References Examples

Data sets and functions accompanying 'Analyzing Linguistic Data: A practical introduction to statistics', Cambridge University Press, 2007.

Package: | languageR |

Type: | Package |

Version: | 1.0 |

Date: | 2007-01-15 |

License: | GNU public license |

The main function of this package is to make available the data sets discussed and analyzed in 'Analyzing Linguistic Data: A practical introduction to statistics using R', to appear with Cambridge University Press. The following packages should be installed, as ancillary functions in this package depend on them.

`zipfR`

for word frequency distributions

`lme4`

for mixed-effects models

`coda`

for Markov-Chain Monte Carlo estimation

`lattice`

for trellis graphics

`Matrix`

for mixed-effects modeling

The following packages need to be installed for working through specific examples.

`rms`

for regression modeling

`rpart`

for CART trees

`e1071`

for support vector machines

`MASS`

for many useful functions

`ape`

for phylogenetic clustering

The main convenience functions in this library are, by category:

- correspondence analysis
(extending code by Murtagh, 2005)

`corres.fnc`

correspondence analysis

`corsup.fnc`

supplementary data

- vocabulary richness
(supplementing current zipfR functionality)

`compare.richness.fnc`

for two texts, compare richness

`growth.fnc`

empirical vocabulary growth data for text

`growth2vgc`

conversion to vgc object of zipfR

`spectrum.fnc`

creates frequency spectrum

`text2spc.fnc`

conversion to spc object of zipfR

- lmer functions
(p-values for mixed-effects models with lme4)

`pvals.fnc`

p-values for table of coefficients including MCMC

`aovlmer.fnc`

p-values for anova tables and/or MCMC p-value for specified factor

- simulation functions
(for comparing mixed models with traditional techniques including F1, F2, and F1+F2)

`simulateRegression.fnc`

simulate simple regression design

`simulateQuasif.fnc`

simulate data for Quasi-F ratios

`simulateLatinsquare.fnc`

simulating simple Latin-square design

- miscellaneous
(convenience functions)

`pairscor.fnc`

scatterplot matrix with correlation tests

`collin.fnc`

collinearity diagnostics

`pvals.fnc`

p-values and MCMC confidence intervals for mixed models

`plot.logistic.fit.fnc`

diagnostic visualization for logistic models

`xylowess.fnc`

trellis scatterplots with smoother

`mvrnormplot.fnc`

scatterplot for bivariate standard normal random numbers with regression line

`lmerPlotInt.fnc`

offers choice of four ways to visualize an interaction between two numeric predictors in an lmer model

R. H. Baayen

University of Alberta, Edmonton, Canada

Maintainer: [email protected]

R. H. Baayen (2007) *Analyzing Linguistic Data: A practical introduction
to statistics using R*, Cambridge: Cambridge University Press.

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