This function allows you to calculate A from from a vector hits and a vector a false alarms.
A(data, h, f)
data | A data frame. |
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h | A vector of hits (0 = miss, 1 = hit). |
f | A vector of false alarms (0 = correct rejection, 1 = false alarm). |
#> #>#>#> #>#>#> #>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 A on entire data frame A(axb, hit, fa)#> [1] 0.6703768# Calculate A for each subject # by group, plot it, and run a # linear model axb %>% group_by(subj, group) %>% summarize(A = A(., hit, fa)) %T>% { plot(A ~ as.numeric(group), data = ., main = "A as a function of group", xaxt = "n", xlab = "Group", ylab = "A") axis(1, at = 1:2, labels = c("g1", "g2")) abline(lm(A ~ as.numeric(group), data = .), col = "red") } %>% lm(A ~ group, data = .) %>% summary()#>#> Warning: essentially perfect fit: summary may be unreliable#> #> Call: #> lm(formula = A ~ group, data = .) #> #> Residuals: #> Min 1Q Median 3Q Max #> -7.022e-17 -7.022e-17 0.000e+00 0.000e+00 2.809e-16 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 6.704e-01 4.965e-17 1.35e+16 <2e-16 *** #> groupg2 -7.022e-17 7.022e-17 -1.00e+00 0.347 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Residual standard error: 1.11e-16 on 8 degrees of freedom #> Multiple R-squared: 0.619, Adjusted R-squared: 0.5714 #> F-statistic: 13 on 1 and 8 DF, p-value: 0.006926 #>