Description Usage Arguments Value Author(s) Examples
Computes Signal Detection Theory indices, including d', beta, A', B”D and c.
1 2 3 4 5 6 7 8 9 |
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. |
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).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | 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
)
|
Note: Many functions of the 'psycho' package have been (improved and) moved to other packages of the new 'easystats' collection (https://github.com/easystats). If you don't find where a function is gone, please open an issue at: https://github.com/easystats/easystats/issues
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