simulateRegression.fnc: Simulate regression data and compare models
In languageR: Analyzing Linguistic Data: A Practical Introduction to Statistics

Description Usage Arguments Value Author(s) See Also Examples

This function creates a user-specified number of simulated regression datasets, and compares mixed-effects regression with random regression, by-subject regression, by-item regression, and by-subject plus by-item regression. Optionally, an effect of learning or fatigue can be incorporated.

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simulateRegression.fnc(beta = c(400, 2, 6, 4), nitem = 20, nsubj = 10, 
stdevItem = 40, stdevSubj = 80, stdevError = 50, nruns = 100, learn = FALSE, 
learnRate = 10, ...)

`beta`	A numeric vector with beta weights for the intercept and three numeric predictors.
`nitem`	A number specifying the number of items.
`nsubj`	A number specifying the number of subjects.
`stdevItem`	A number specifying the standard deviation of the Item random effect.
`stdevSubj`	A number specifying the standard deviation of the Subject random effect.
`stdevError`	A number specifying the standard deviation of the Residual Error.
`nruns`	A number specifying the required number of simulated datasets.
`learn`	A logical that if TRUE, allows an effect of learning or fatigue into the model.
`learnRate`	A number specifying the learning rate (if negative) or the effect of fatigue (if positive).
`...`	other parameters to be passed through to plotting functions.

A list with components

`alpha05`	proportion of runs in which predictors are significant at the 05 significance level.
`alpha01`	proportion of runs in which predictors are significant at the 01 significance level.
`ranef`	mean estimated random effects.

As this may take some time, the index of each completed run is shown on the output device.

R. H. Baayen

See Also make.reg.fnc.

## Not run: 
	library(lme4)
  simulateRegression.fnc(beta = c(400, 2, 6, 4), nruns = 5)

  \dontrun{simulateRegression.fnc(beta = c(400, 2, 6, 0), nruns = 1000, learn = TRUE)}

## End(Not run)