dpsdPosSim: Function dpsdPosSim

In hbmem: Hierarchical Bayesian Analysis of Recognition Memory

Function dpsdPosSim

Description

Function to simulate data from DPSD model with positive d'

Usage

dpsdPosSim(NN = 1, NS = 2, I = 30, J = 200, K = 6, muN = -0.7,
                 s2aN = 0.2, s2bN = 0.2, muD = c(0, 0.5), s2aD = 0.2,
                 s2bD = 0.2, muR = qnorm(c(0.2, 0.4)), s2aR = 0.2, s2bR
                 = 0.2, crit = matrix(rep(c(-1.6, -0.5, 0, 0.5, 1.6),
                 each = I), ncol = (K - 1)))

Arguments

NN	Number of new-item conditions.
NS	Number of studied-item conditions.
I	Number of participants.
J	Number of items.
K	Number of response options.
muN	Mean of new-item distribution. If there are more than one new-item conditions this is a vector of means with length equal to NN.
s2aN	Variance of participant effects on mean of new-item distribution.
s2bN	Variance of item effects on mean of new-item distribution.
muD	Mean of dprime distribution. If there are more than new-item conditions this is a vector of means with length equal to NNone studied-item conditions this is a vector of means with length equal to NS.
s2aD	Variance of participant effects on mean of dprime distribution.
s2bD	Variance of item effects on mean of dprime distribution.
muR	Mean recollection, on probit space.
s2aR	Variance of participant effects recollection.
s2bR	Variance of item effects on recollection.
crit	Matrix of criteria (not including -Inf or Inf). Columns correspond to criteria, rows correspond to participants.

hbmem documentation built on Aug. 23, 2023, 1:10 a.m.