simulateRegression.fnc {languageR}

Simulate regression data and compare models

Description

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.

Usage

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

Arguments

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.

Value

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.

Author(s)

R. H. Baayen

See Also

See Also make.reg.fnc.

Examples

## 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)