R/int_llSDT.R

In statConfR: Models of Decision Confidence and Measures of Metacognition

llSDT <-
  function(p, N_SA_RA,N_SA_RB, N_SB_RA, N_SB_RB, nRatings, nCond){
    p <- c(t(p))
    ds <- cumsum(exp(p[1:(nCond)])) # enforce that sensitivity is ordered
    locA <- -ds/2
    locB <- ds/2

    c_RA <- c(-Inf, p[nCond+nRatings] -
                rev(cumsum(c(exp(p[(nCond+1):(nCond+nRatings-1)])))),
              p[nCond+nRatings])

    c_RB <- c(p[nCond+nRatings], p[nCond+nRatings] +
                cumsum(c(exp(p[(nCond+nRatings+1):(length(p))]))), Inf)

    p_SA_RA <- matrix(NA, nrow=nCond, ncol = nRatings)
    p_SA_RB <- matrix(NA, nrow=nCond, ncol = nRatings)
    p_SB_RA <- matrix(NA, nrow=nCond, ncol = nRatings)
    p_SB_RB <- matrix(NA, nrow=nCond, ncol = nRatings)

    P_SBRB_SDT <- Vectorize(function(j,i) pnorm(q=c_RB[i+1], locB[j]) - pnorm(q=c_RB[i], locB[j]))
    P_SBRA_SDT <- Vectorize(function(j,i) pnorm(q=c_RA[i+1], locB[j]) - pnorm(q=c_RA[i], locB[j]))
    P_SARA_SDT <- Vectorize(function(j,i) pnorm(q=c_RA[i+1], locA[j]) - pnorm(q=c_RA[i], locA[j]))
    P_SARB_SDT <- Vectorize(function(j,i) pnorm(q=c_RB[i+1], locA[j]) - pnorm(q=c_RB[i], locA[j]))

    p_SB_RB <- outer(1:nCond, 1:nRatings, P_SBRB_SDT)
    p_SB_RA <- outer(1:nCond, 1:nRatings, P_SBRA_SDT)
    p_SA_RA <- outer(1:nCond, 1:nRatings, P_SARA_SDT)
    p_SA_RB <- outer(1:nCond, 1:nRatings, P_SARB_SDT)

    p_SB_RB[(is.na(p_SB_RB))| is.nan(p_SB_RB)| p_SB_RB < 10^-64] <- 10^-64
    p_SB_RA[(is.na(p_SB_RA))| is.nan(p_SB_RA)| p_SB_RA < 10^-64] <- 10^-64
    p_SA_RB[(is.na(p_SA_RB))| is.nan(p_SA_RB)| p_SA_RB < 10^-64] <- 10^-64
    p_SA_RA[(is.na(p_SA_RA))| is.nan(p_SA_RA)| p_SA_RA < 10^-64] <- 10^-64

    negLogL <- - sum (c(log(p_SB_RB) * N_SB_RB, log(p_SB_RA) * N_SB_RA,
                        log(p_SA_RB) * N_SA_RB, log(p_SA_RA) * N_SA_RA))

    negLogL
  }