gqc {grt}

General Quadratic Classifier

Description

Fit a general quadratic classifier (a.k.a. quadratic decison-bound model).

Usage

gqc(formula,
    data,
    category,
    par = list(),
    zlimit = Inf,
    fixed = list(),
    opt = c("nlminb", "optim"),
    lower=-Inf,
    upper=Inf,
    control=list())

Arguments

formula

A formula of the form response ~ x1 + x2 + ... where the response specifies the grouping factor (generally a participant's response) and the right hand side specifies the feature values of the classified stimuli.

data

A data frame from which variables specified in formula are taken.

category

(Optional.) A factor specifying the true category membership of the stimuli.

par

object of class gqcStruct or named list containing a set of initial parameters used to fit the data.

zlimit

numeric. The z-scores (or discriminant scores) beyond the specified value will be truncated. Default to Inf

fixed

A named list of logical vectors specifying whether each of pnoise, cnoise, coeffs, and bias parameters should be fixed to the initial value. Default to all FALSE. A fatal error will result if set to all TRUE.

opt

A character string specifying the optimizer to be used: either nlminb (the default) or optim. If "optim", "L-BFGS-B" method is used (see ‘Details’ of optim)

lower, upper

Bounds on the parameters. Default values of lower and upper are c(.1, .1, rep(-Inf, length(unlist(par))-2)), and c(5000, 5000, rep( Inf, length(unlist(par))-2)), respectively.

control

A list of control parameters passed to the optimizer. See ‘Details’ of nlminb or optim

Details

If par is not fully specified in the argument, the function attempts to calculate the initial parameter values by internally calling the functions mcovs and qdb. The response specified in the formula is used as the grouping factor in mcovs.

Value

object of class gqc, i.e., a list containing the following components:

terms

the terms object used.

call

the matched call.

model

the design matrix used to fit the model.

category

the category vector as specified in the input.

initpar

the initial parameter used to fit the model.

par

the fitted parameter.

logLik

the log-likelihood at convergence.

References

Alfonso-Reese, L. A. (2006) General recognition theory of categorization: A MATLAB toolbox. Behavior Research Methods, 38, 579-583.

Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, & Cognition, 14, 33-53.

Ashby, F. G. (1992) Multidimensional models of perception and cognition. Lawrence Erlbaum Associates.

See Also

glc, qdb, logLik.gqc, logLik.gqcStruct, plot.gqc, plot3d.gqc

Examples

data(subjdemo_2d)
fit.2dq <- gqc(response ~ x + y, data=subjdemo_2d,
    category=subjdemo_2d$category, zlimit=7)