sdm {bmm} | R Documentation |
Signal Discrimination Model (SDM) by Oberauer (2023)
Description
Signal Discrimination Model (SDM) by Oberauer (2023)
Usage
sdm(resp_error, version = "simple", ...)
sdmSimple(resp_error, version = "simple", ...)
Arguments
resp_error |
The name of the variable in the dataset containing the
response error. The response error should code the response relative to the
to-be-recalled target in radians. You can transform the response error in
degrees to radians using the |
version |
Character. The version of the model to use. Currently only "simple" is supported. |
... |
used internally for testing, ignore it |
Details
see the online article for a detailed description of the model and how to use it. * Domain: Visual working memory
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Task: Continuous reproduction
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Name: Signal Discrimination Model (SDM) by Oberauer (2023)
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Citation:
Oberauer, K. (2023). Measurement models for visual working memory - A factorial model comparison. Psychological Review, 130(3), 841-852
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Version: simple
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Requirements:
The response variable should be in radians and represent the angular error relative to the target
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Parameters:
-
mu
: Location parameter of the SDM distribution (in radians; by default fixed internally to 0) -
c
: Memory strength parameter of the SDM distribution -
kappa
: Precision parameter of the SDM distribution
-
-
Fixed parameters:
-
mu
= 0
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Default parameter links:
mu = tan_half; c = log; kappa = log
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Default priors:
-
mu
:-
main
: student_t(1, 0, 1)
-
-
kappa
:-
main
: student_t(5, 1.75, 0.75) -
effects
: normal(0, 1)
-
-
c
:-
main
: student_t(5, 2, 0.75) -
effects
: normal(0, 1)
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-
Value
An object of class bmmodel
Examples
# simulate data from the model
dat <- data.frame(y = rsdm(n = 1000, c = 4, kappa = 3))
# specify formula
ff <- bmf(c ~ 1,
kappa ~ 1)
# specify the model
fit <- bmm(formula = ff,
data = dat,
model = sdm(resp_error = 'y'),
cores = 4,
backend = 'cmdstanr')