gcjc {grt} | R Documentation |
General Conjunctive Classifier
Description
Fit a general conjunctive classifier.
Usage
gcjc(formula, data, category, par, config = 1, zlimit = Inf,
fixed = list(), equal.noise = TRUE, opt = c("nlminb", "optim"),
lower=-Inf, upper=Inf, control=list())
Arguments
formula |
A formula of the form |
data |
A data frame from which variables specified in |
category |
(Optional.) A factor specifying the true category membership of the stimuli. |
par |
object of class |
config |
A numeric value specifying the location of the conjunctive category in relation to the category bounds. The value 1 indicates the category is on the top right (set as default), 2 indicates the top left, 3 indicates bottom left, and 4 indicates the bottom right. |
zlimit |
numeric. The z-scores (or discriminant scores) beyond the specified value will be truncated. Default to |
fixed |
A named list of logical vectors specifying whether each of |
equal.noise |
logical. If set to |
opt |
A character string specifying the optimizer to be used: either |
lower , upper |
Bounds on the parameters. see ‘Details’ for default values. |
control |
A list of control parameters passed to the internal optimization function. See ‘Details’ of |
Details
If par
is not fully specified in the argument, the function attempts to calculate the initial parameter values based on means by category or by response.
The default lower and upper values are selected based on the range of the data
input so that the decision bound is found within the range of the data and convergence can be reached.
Value
object of the class gcjc
, i.e., a list containing the following components:
terms |
the |
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
Ashby, F. G. (1992) Multidimensional models of perception and cognition. Lawrence Erlbaum Associates.
See Also
glc
,
logLik.gcjc
,
coef.gcjc
,
plot.gcjc
Examples
data(subjdemo_cj)
m.cj <- gcjc(response ~ x1 + x2, data=subjdemo_cj,
config=2, category=subjdemo_cj$category, zlimit=7)