Generate (log) probability simplex over the joint type 1/type 2 responses

Usage

metad_pmf(
  stimulus,
  dprime,
  c,
  meta_dprime,
  meta_c,
  meta_c2_0,
  meta_c2_1,
  lcdf = normal_lcdf,
  lccdf = normal_lccdf,
  log = FALSE
)

Arguments

stimulus: the stimulus (0 or 1)
dprime: the type 1 sensitivity
c: the type 1 response criterion
meta_dprime: the type 2 sensitivity
meta_c: the type 1 criteriom for generating confidence ratings
meta_c2_0: the type 2 response criteria for "0" responses, indexed by increasing confidence levels
meta_c2_1: the type 2 response criteria for "1" responses, indexed by increasing confidence levels
lcdf: The log cumulative distribution function for the underlying distribution in the metad' model. By default, uses the normal distribution with a standard deviation of 1.
lccdf: The log complement cumulative distribution function for the underlying distribution in the metad' model. By default, uses the normal distribution with a standard deviation of 1.
log: if TRUE, return log probabilities instead of probabilities

Value

A probability simplex $$\begin{bmatrix} P(R=0, C=K \vert S=0), \ldots, P(R=0, C=1 \vert S=0), P(R=0, C=1 \vert S=1), \ldots, P(R=1, C=1 \vert S=1)\end{bmatrix}$$ for response $R$ and confidence $C$ given stimulus $S$, as defined by the meta-d' model.

Examples

metad_pmf(
  stimulus = 0, dprime = 2, c = .5, meta_dprime = 1, meta_c = .5,
  meta_c2_0 = c(0, -.5), meta_c2_1 = c(1, 1.5)
)
#> [1] 0.554584077 0.212364065 0.166244657 0.038675753 0.018551730 0.009579718