Compute joint response probabilities from aggregated counts

Usage

response_probabilities(counts)

Arguments

counts: A vector (or matrix) of counts of joint type 1/type 2 responses as provided by aggregate_metad

Value

A vector (or matrix) of response probabilities $P(R, C \;\vert\; S)$

Details

For response $R$, confidence $C$, stimulus $S$, and number of confidence levels $K$, counts should be a vector (or matrix with rows) of the form: $$ [N_{S=0, R=0, C=K}, \ldots, N_{S=0, R=0, C=1}, \\ N_{S=0, R=1, C=1}, \ldots, N_{S=0, R=1, C=K}, \\ N_{S=1, R=0, C=K}, \ldots, N_{S=1, R=0, C=1}, \\ N_{S=1, R=1, C=1}, \ldots, N_{S=1, R=1, C=K}] \\ $$

Returns a vector (or matrix with rows) of the form: $$ [P(R=0, C=K \;\vert\; S=0), ..., P(R=0, C=1 \;\vert\; S=0), \\ P(R=1, C=1 \;\vert\; S=0), ..., P(R=1, C=K \;\vert\; S=0), \\ P(R=0, C=K \;\vert\; S=1), ..., P(R=0, C=1 \;\vert\; S=1), \\ P(R=1, C=1 \;\vert\; S=1), ..., P(R=1, C=K \;\vert\; S=1)] $$

Examples

# Aggregate responses from simulated data
d <- sim_metad() |> aggregate_metad()

# Compute conditional response probabilities
response_probabilities(d$N)
#>      N_0_1 N_0_2 N_0_3 N_0_4 N_0_5 N_0_6 N_0_7 N_0_8 N_1_1 N_1_2 N_1_3 N_1_4
#> [1,]  0.12  0.12   0.1   0.2  0.24  0.16  0.06     0  0.02  0.02   0.1  0.26
#>      N_1_5 N_1_6 N_1_7 N_1_8
#> [1,]  0.12  0.18  0.16  0.14

# Also works on matrices
matrix(rep(1, 16), nrow = 2) |> response_probabilities()
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,] 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
#> [2,] 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25