Description Usage Arguments Details Value Author(s) References See Also Examples
ndlClassify
uses the equilibrium equations of Danks (2003)
for the Rescorla-Wagner model (1972) to estimate association
strengths (weights) for cues (typically levels of factorial
predictors) to outcomes (typically a binary or polytomous response
variable). Given the association strengths, the probability of a
response level is obtained by summation over the weights on active
incoming links.
1 2 3 4 |
formula |
An object of class |
data |
A data frame containing the variables in the model. |
frequency |
A numeric vector (or the name of a column in the input data frame) with the frequencies of the exemplars. If absent, each exemplar is assigned a frequency equal to 1. |
x |
An object of the class |
max.print |
The maximum number of rows of the |
variable.value.separator |
A character string which will separate variable names from
variable values in their combination as cue values; by default an
empty character string ( |
... |
Control arguments to be passed along to
|
Classification by naive discriminative learning.
A list of the class "ndlClassify"
with the following components:
activationMatrix
A matrix specifying for each row of the
input data frame the activations (probabilities) of the levels of the
response variable (nrow
observations by nlevels
of response variable).
weightMatrix
A matrix specifying for each cue (predictor value) the association strength (weight) to each outcome (level of the response variable) (number of distinct predictor values by number of response levels).
cuesOutcomes
The input data structure for naive
discriminative learning created by ndlCuesOutcomes
based on
the data
argument (number of observations by 3: Frequency,
Cues, Outcomes
).
call
The call matched to fit the resulting "ndlClassify"
object.
formula
The formula specified for fitting the
resulting "ndlClassify"
object.
data
The supplied data
argument, excluding all
elements not specified for the modeling task in formula
and
frequency
.
R. H. Baayen and Antti Arppe
Baayen, R. H. and Milin, P. and Filipovic Durdevic, D. and Hendrix, P. and Marelli, M., An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118, 438-482.
Danks, D. (2003). Equilibria of the Rescorla-Wagner model. Journal of Mathematical Psychology, 47 (2), 109-121.
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In Black, A. H., & Prokasy, W. F. (Eds.), Classical conditioning II: Current research and theory (pp. 64-99). New York: Appleton-Century-Crofts.
Arppe, A. and Baayen, R. H. (in prep.) Statistical classification and principles of human learning.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | data(think)
set.seed(314)
think <- think[sample(1:nrow(think),500),]
think.ndl <- ndlClassify(Lexeme ~ (Person * Number * Agent) + Register,
data=think)
summary(think.ndl)
## Not run:
think.ndl.SA <- ndlClassify(Lexeme ~ (Polarity + Voice + Mood + Person +
Number + Covert + ClauseEquivalent + Agent + Patient + Manner + Time +
Modality1 + Modality2 + Source + Goal + Quantity + Location +
Duration + Frequency + MetaComment + ReasonPurpose + Condition +
CoordinatedVerb)^2 + Author + Section, data=think)
summary(think.ndl.SA)
## End(Not run)
## Not run:
data(dative)
out <- which(is.element(colnames(dative), c("Speaker","Verb")))
dative <- dative[-out]
dative.ndl <- ndlClassify(RealizationOfRecipient ~ ., data=dative)
summary(dative.ndl)
## End(Not run)
|
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