IMMdist | R Documentation |
Density, distribution, and random generation functions for the
interference measurement model with the location of mu
, strength of cue-
dependent activation c
, strength of cue-independent activation a
, the
generalization gradient s
, and the precision of memory representations
kappa
.
dimm(
x,
mu = c(0, 2, -1.5),
dist = c(0, 0.5, 2),
c = 5,
a = 2,
b = 1,
s = 2,
kappa = 5,
log = FALSE
)
pimm(
q,
mu = c(0, 2, -1.5),
dist = c(0, 0.5, 2),
c = 1,
a = 0.2,
b = 0,
s = 2,
kappa = 5
)
qimm(
p,
mu = c(0, 2, -1.5),
dist = c(0, 0.5, 2),
c = 1,
a = 0.2,
b = 0,
s = 2,
kappa = 5
)
rimm(
n,
mu = c(0, 2, -1.5),
dist = c(0, 0.5, 2),
c = 1,
a = 0.2,
b = 1,
s = 2,
kappa = 5
)
x |
Vector of observed responses |
mu |
Vector of locations |
dist |
Vector of distances of the item locations to the cued location |
c |
Vector of strengths for cue-dependent activation |
a |
Vector of strengths for cue-independent activation |
b |
Vector of baseline activation |
s |
Vector of generalization gradients |
kappa |
Vector of precision values |
log |
Logical; if |
q |
Vector of quantiles |
p |
Vector of probability |
n |
Number of observations to generate data for |
dimm
gives the density of the interference measurement model,
pimm
gives the cumulative distribution function of the interference
measurement model, qimm
gives the quantile function of the interference
measurement model, and rimm
gives the random generation function for the
interference measurement model.
Oberauer, K., Stoneking, C., Wabersich, D., & Lin, H.-Y. (2017). Hierarchical Bayesian measurement models for continuous reproduction of visual features from working memory. Journal of Vision, 17(5), 11.
# generate random samples from the imm and overlay the density
r <- rimm(10000, mu = c(0, 2, -1.5), dist = c(0, 0.5, 2),
c = 5, a = 2, s = 2, b = 1, kappa = 4)
x <- seq(-pi,pi,length.out=10000)
d <- dimm(x, mu = c(0, 2, -1.5), dist = c(0, 0.5, 2),
c = 5, a = 2, s = 2, b = 1, kappa = 4)
hist(r, breaks=60, freq=FALSE)
lines(x,d,type="l", col="red")
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