This function will calculate d prime from a vector of hits and a vector of false alarms.
dPrime(data, h, f)Arguments
Details
This metric is common in discrimination experiments. Note: If your participants are at ceiling, you may want to consider another analysis.
Examples
# Create some data
set.seed(1); library(dplyr)
axb <- data.frame(subj = sort(rep(1:10, each = 20, times = 10)),
group = gl(2, 1000, labels = c("g1", "g2")),
hit = c(rbinom(1000, size = c(0, 1), prob = .8),
rbinom(1000, size = c(0, 1), prob = .6)),
fa = c(rbinom(1000, size = c(0, 1), prob = .3),
rbinom(1000, size = c(0, 1), prob = .4))
)
# Calculate d prime on entire data frame
dPrime(axb, hit, fa)
#> [1] 0.5557056
# Calculate d prime for each subject by group, plot it,
# and run a linear model
library(dplyr)
axb %>%
group_by(subj, group) %>%
summarize(dp = dPrime(., hit, fa)) %T>%
{
plot(dp ~ as.numeric(group), data = .,
main = "d' as a function of group", xaxt = "n",
xlab = "Group", ylab = "d' prime")
axis(1, at = 1:2, labels = c("g1", "g2"))
abline(lm(dp ~ as.numeric(group), data = .), col = "red")
} %>%
lm(dp ~ group, data = .) %>%
summary()
#> `summarise()` has grouped output by 'subj'. You can override using the
#> `.groups` argument.
#> Warning: essentially perfect fit: summary may be unreliable
#>
#> Call:
#> lm(formula = dp ~ group, data = .)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -7.701e-17 0.000e+00 0.000e+00 1.925e-17 1.925e-17
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 5.557e-01 1.361e-17 4.082e+16 <2e-16 ***
#> groupg2 1.925e-17 1.925e-17 1.000e+00 0.347
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 3.044e-17 on 8 degrees of freedom
#> Multiple R-squared: 0.6245, Adjusted R-squared: 0.5775
#> F-statistic: 13.3 on 1 and 8 DF, p-value: 0.006519
#>