rcr: Random Coefficients Regression

In phonTools: Tools for Phonetic and Acoustic Analyses

Random Coefficients Regression

Description

Carry out a random coefficients regression (rcr) using repeated calls to glm, individually for the data from each participant/data cluster.

Usage

rcr(formula, participants, dataframe, ...)

Arguments

formula	A symbolic description of the model to be fitted.
participants	A vector indicating which row in the dataframe belongs to which participant. Length must equal the number of rows in the dataframe.
dataframe	The dataframe containing the data for the model.
...	Additional arguments to be passed to the internal glm() function call. For example, family, if not 'gaussian', should be specified.

Details

This function fits a model to the data from each participant individually using repeated calls to glm(). Significance testing is then carried out on the coefficients fit for each participant using the methods established in Gumpertz & Pantula (1989) and Lorch & Myers (1990).

In perceptual experiments there is frequently a high number of data points collected from each participant, and the data collected from each participant is balanced by design. In these situations rcr performs comparably to mixed-effects models. In the event that only a small number of observations are made from each listener, or the data is not balanced, rcr may not be appropriate.

A call to summary() on an rcr object performs a one-sample t-test on each coefficient to test whether it is significantly different from zero.

A call to anova() on an rcr object performs a one-sample t-test in the case of single coefficients, and a one-sample Hotelling T2 test in the event that multiple coefficients are associated with a single factor, to test that they are not all equal to zero.

A call to plot() on an rcr object displays the density corresponding to the distribution of all fitted coefficients. These are compared to a normal distribution with the same mean and standard deviation.

Value

An object of class 'rcr', a list containing the elements:

formula	The formula used to call and create the rcr object.
call	The exact call used to create the rcr object.
participants	A vector indicating the labels used to identify each individual participant as indicated by 'participants'.
factors	A vector indicating the grouping of the explanatory variables.
factor.names	A vector containing the names of each group of coefficients.
coefficients	A dataframe containing the coefficients fit individually for each participant.
coefficient.means	The mean value of each coefficient across all participants.
coefficient.names	The name of each individual coefficient.
varExp	The percent of variance or deviance explained by the model for each participant.

Author(s)

Santiago Barreda <[e-mail]>

References

Gumpertz, M., & Pantula, S. G. (1989). A Simple Approach to Inference in Random Coefficient Models. The American Statistician, 43(4), 203-210.

Lorch, T. F. & Myers, J. L. (1990). Regression analyses of repeated measures data in cognitive research. J. Exp. Psychol. Learn. Mem. Cogn. 16: 149-157.

Examples

#data (pb52)

## runs an rcr model on the Peterson & Barney (1952) vowels to test 
## for the predictive value of the speaker's f0 and F3
#rcr.model = rcr (f1 ~ f0 * f3, pb52$speaker, pb52)
#rcr.model

## test for the significance of each individual coefficient 
#summary (rcr.model)

## a similar analysis can be run using vowel category as the predictor
#rcr.model = rcr (f1 ~ vowel, pb52$speaker, pb52)
#rcr.model

## here, summary() tests each coefficient individually
#summary (rcr.model)

## while anova() tests associated coefficients together
#anova (rcr.model)

phonTools documentation built on Nov. 21, 2023, 1:07 a.m.