| dprime {psycho} | R Documentation |
Dprime (d') and Other Signal Detection Theory indices.
Description
Computes Signal Detection Theory indices, including d', beta, A', B”D and c.
Usage
dprime(
n_hit,
n_fa,
n_miss = NULL,
n_cr = NULL,
n_targets = NULL,
n_distractors = NULL,
adjusted = TRUE
)
Arguments
n_hit |
Number of hits. |
n_fa |
Number of false alarms. |
n_miss |
Number of misses. |
n_cr |
Number of correct rejections. |
n_targets |
Number of targets (n_hit + n_miss). |
n_distractors |
Number of distractors (n_fa + n_cr). |
adjusted |
Should it use the Hautus (1995) adjustments for extreme values. |
Value
Calculates the d', the beta, the A' and the B”D based on the signal detection theory (SRT). See Pallier (2002) for the algorithms.
Returns a list containing the following indices:
dprime (d'): The sensitivity. Reflects the distance between the two distributions: signal, and signal+noise and corresponds to the Z value of the hit-rate minus that of the false-alarm rate.
beta: The bias (criterion). The value for beta is the ratio of the normal density functions at the criterion of the Z values used in the computation of d'. This reflects an observer's bias to say 'yes' or 'no' with the unbiased observer having a value around 1.0. As the bias to say 'yes' increases (liberal), resulting in a higher hit-rate and false-alarm-rate, beta approaches 0.0. As the bias to say 'no' increases (conservative), resulting in a lower hit-rate and false-alarm rate, beta increases over 1.0 on an open-ended scale.
c: Another index of bias. the number of standard deviations from the midpoint between these two distributions, i.e., a measure on a continuum from "conservative" to "liberal".
aprime (A'): Non-parametric estimate of discriminability. An A' near 1.0 indicates good discriminability, while a value near 0.5 means chance performance.
bppd (B”D): Non-parametric estimate of bias. A B”D equal to 0.0 indicates no bias, positive numbers represent conservative bias (i.e., a tendency to answer 'no'), negative numbers represent liberal bias (i.e. a tendency to answer 'yes'). The maximum absolute value is 1.0.
Note that for d' and beta, adjustement for extreme values are made following the recommandations of Hautus (1995).
Author(s)
Examples
library(psycho)
n_hit <- 9
n_fa <- 2
n_miss <- 1
n_cr <- 7
indices <- psycho::dprime(n_hit, n_fa, n_miss, n_cr)
df <- data.frame(
Participant = c("A", "B", "C"),
n_hit = c(1, 2, 5),
n_fa = c(6, 8, 1)
)
indices <- psycho::dprime(
n_hit = df$n_hit,
n_fa = df$n_fa,
n_targets = 10,
n_distractors = 10,
adjusted = FALSE
)