# Function where x is Target (1 present 0 absent) and y is accuracy (1 correct 0 incorrect trial)

SDT_Classifier <- function(x, y) {

   performance <-rep(NA, length(x))    #define empty performance vector

   performance[x ==1 & y == 1] <- "Hit"

   performance[x==0 & y == 1] <- "Cr"

   performance[x== 1 & y == 0 ] <- "Miss"

   performance[x== 0 & y == 0] <- "FA"

   return(performance)

}

Performance_vector <- SDT_Classifier(df$Target, df$ACC) #this applies the function and #generates a vector that you can bind on the df

newdf <- cbind(df, Performance_vector)

#Now you can use the awesome dplyr to compute the performance rate per participant/per #condition.

library(dplyr)

Perf_Rate <- newdf %>% 

             group_by(SubjID, Condition) %>%

             summarise(no_Hit = sum(Performance_vector == "Hit"), 

                       no_Fa = sum(Performance_vector == "FA"), 

                       no_Miss= sum(Performance_vector == "Miss"), 

                       no_CR = sum(Performance_vector == "Cr"))   

#here an example, his function dprime computes parametric and non parametric dprime and #Criterion

library(psycho)

dprime <- dprime(Perf_Rate$no_Hit,Perf_Rate$no_Fa,Perf_Rate$no_Miss,Perf_Rate$no_CR) 

Perf_Rate <- cbind(Perf_Rate, dprime) #bind this to your dataframe.