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Fixing the number of categories issue
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,164 @@ | ||
| ### Functions to fit the WEV model | ||
| ### Model based on Peters et al. (2017) | ||
| ### the original model is extended to include a choice bias parameter so the | ||
| ### the cognitive architecture underlying the decision is equivalent to SDT. | ||
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| fitHeuris <- | ||
| function(N_SA_RA, N_SA_RB, N_SB_RA, N_SB_RB, | ||
| nInits, nRestart, nRatings, nCond){ | ||
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| # coarse grid search to find promising initial values | ||
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| temp <- expand.grid(minD = seq(-.3, .8, length.out=4), | ||
| maxD = c(.6, 1.2, 1.4, 1.9), | ||
| b = c(-.6, -.2, .2, .6), # bias in favor of option "B | ||
| tauMin = seq(-1.1, 2.4, length.out=4), # position of the most conservative confidence criterion related to stimulus A | ||
| tauRange = seq(.1,3.8, length.out=4)) # range of rating criteria stimulus B | ||
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| inits <- data.frame(matrix(data=NA, nrow= nrow(temp), | ||
| ncol = nCond + nRatings*2 -1)) | ||
| if(nCond==1) { | ||
| inits[,1] <- log(temp$maxD) } | ||
| else{ | ||
| inits[,1:(nCond)] <- log(t(mapply(function(maxD) diff(seq(0, maxD, length.out = nCond+1)), temp$maxD))) | ||
| } | ||
| inits[,nCond +1] <- temp$tauMin | ||
| inits[,(nCond+2):(nCond+nRatings-1)] <- | ||
| log(t(mapply(function(tauRange) rep(tauRange/(nRatings-1), nRatings-2), | ||
| temp$tauRange))) | ||
| # inits[,nCond+(nRatings-1)] <- temp$tauMin | ||
| inits[,nCond+nRatings] <- temp$b # theta | ||
| inits[,nCond+(nRatings+1)] <- temp$tauMin | ||
| inits[,(nCond+nRatings+2):(nCond + nRatings*2-1)] <- | ||
| log(t(mapply(function(tauRange) rep(tauRange/(nRatings-1), nRatings-2), | ||
| temp$tauRange))) | ||
|
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||
| logL <- apply(inits, MARGIN = 1, | ||
| function(p) try(llHeuris(p, N_SA_RA, N_SA_RB, N_SB_RA,N_SB_RB, nRatings, nCond), silent = TRUE)) | ||
| logL <- as.numeric(logL) | ||
| nAttempts <- 5 | ||
| nRestart <- 4 | ||
| inits <- inits[order(logL),][1:nAttempts,] | ||
| nIter <- 10^6 | ||
| noFitYet <- TRUE | ||
| #print(paste("Initial grid search took...",as.character(round(as.double(difftime(Sys.time(),t00,units = "mins")), 2))," mins")) | ||
| #print("Start fitting ... ") | ||
| start <- inits[1,] | ||
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| try(fit <- optim(par = start, | ||
| f = llHeuris(), gr = NULL, | ||
| N_SA_RA = N_SA_RA,N_SA_RB = N_SA_RB, | ||
| N_SB_RA = N_SB_RA,N_SB_RB = N_SB_RB, nRatings = nRatings, nCond = nCond, | ||
| control = list(maxit = 10^6, reltol = 10^-8))) | ||
| if (!exists("fit") || class(fit) == "try-error"){ | ||
| i <- 2 | ||
| while(noFitYet && (i <= nAttempts)){ | ||
| start <- inits[i,] | ||
| try(fit <- optim(par = start, | ||
| f = llHeuris, gr = NULL, | ||
| N_SA_RA = N_SA_RA,N_SA_RB = N_SA_RB, | ||
| N_SB_RA = N_SB_RA,N_SB_RB = N_SB_RB, nRatings = nRatings, nCond = nCond, | ||
| control = list(maxit = 10^6, reltol = 10^-8))) | ||
| if (exists("fit") && class(fit) == "list") noFitYet <- FALSE | ||
| i <- i+1 | ||
| } | ||
| } | ||
|
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||
| if (exists("fit") && class(fit) == "list"){ | ||
| for (i in 1:nRestart){ | ||
| try(fit <- optim(par = fit$par, | ||
| f = llHeuris, gr = NULL, | ||
| N_SA_RA = N_SA_RA,N_SA_RB = N_SA_RB, | ||
| N_SB_RA = N_SB_RA,N_SB_RB = N_SB_RB, nRatings = nRatings, nCond = nCond, | ||
| control = list(maxit = 10^6, reltol = 10^-8))) | ||
| } | ||
| } | ||
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| res <- data.frame(matrix(nrow=1, ncol=0)) | ||
| if(class(fit) != "try-error"){ | ||
| k <- length(fit$par) | ||
| N <- length(ratings) | ||
| for (i in 1:nCond){ | ||
| res[paste("d",i, sep="")] <- as.vector(fit$par[i]) | ||
| } | ||
| res$b <- as.vector(fit$par[nCond+nRatings]) | ||
| res[,paste("cA",1:(nRatings-1), sep="")] <- | ||
| c(as.vector(fit$par[nCond+1]), | ||
| as.vector(fit$par[nCond+1]) + | ||
| as.vector(cumsum(c(exp(fit$par[(nCond+2):(nCond + nRatings-1)]))))) | ||
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| res[,paste("cB",1:(nRatings-1), sep="")] <- | ||
| c(as.vector(fit$par[nCond+nRatings+1]), | ||
| as.vector(fit$par[nCond+nRatings+1]) + | ||
| as.vector(cumsum(c(exp(fit$par[(nCond+nRatings+2):(nCond + nRatings*2-1)]))))) | ||
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| res$negLogLik <- fit$value | ||
| res$N <- N | ||
| res$k <- k | ||
| res$BIC <- 2 * fit$value + k * log(N) | ||
| res$AICc <- 2 * fit$value + k * 2 + 2*k*(k-1)/(N-k-1) | ||
| res$AIC <- 2 * fit$value + k * 2 | ||
| } | ||
| res | ||
| } | ||
|
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| llHeuris <- | ||
| function(p, N_SA_RA,N_SA_RB, N_SB_RA, N_SB_RB, nRatings, nCond){ | ||
| p <- c(t(p)) | ||
|
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| ds <- p[1:nCond] | ||
| b <- p[nCond+nRatings] | ||
| c_RA <- c(-Inf, p[nCond+1], p[nCond+1] + | ||
| cumsum(c(exp(p[(nCond+2):(nCond+nRatings-1)]))), Inf) | ||
| # c_RA <- c(-Inf, p[nCond+nRatings-1], rev(cumsum(c(exp(p[(nCond+1):(nCond+nRatings-2)])))), p[nCond+nRatings-1], Inf) | ||
| c_RB <- c(-Inf, p[nCond+nRatings+1], p[nCond+nRatings+1] + | ||
| cumsum(c(exp(p[(nCond+nRatings+2):(nCond + nRatings*2-1)]))), Inf) | ||
|
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| p_SA_RA <- matrix(NA, nrow=nCond, ncol = nRatings) | ||
| p_SA_RB <- matrix(NA, nrow=nCond, ncol = nRatings) | ||
| p_SB_RA <- matrix(NA, nrow=nCond, ncol = nRatings) | ||
| p_SB_RB <- matrix(NA, nrow=nCond, ncol = nRatings) | ||
|
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| P_SBRB <- Vectorize(function(j,i){ | ||
| integrate(function(x) dnorm(x, mean=ds[j]+b) * pnorm(x, mean=-b), | ||
| lower = c_RB[i], | ||
| upper = c_RB[i+1], | ||
| rel.tol = 10^-8)$value | ||
| }) | ||
| P_SARB <- Vectorize(function(j,i){ | ||
| integrate(function(x) dnorm(x, mean = b) * pnorm(x, mean=ds[j]-b), # dnorm(x, mean=) * (1-pnorm(x, mean=b)) | ||
| lower = c_RB[i], | ||
| upper = c_RB[i+1], | ||
| rel.tol = 10^-8)$value | ||
| }) | ||
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|
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| P_SBRA <- Vectorize(function(j,i){ | ||
| integrate(function(x) dnorm(x, mean=-b) * pnorm(x, mean=ds[j]+b) , # dnorm(x, mean=ds[j]+b) * (1 - pnorm(x, mean=-b)) | ||
| lower = c_RA[i], | ||
| upper = c_RA[i+1], | ||
| rel.tol = 10^-8)$value | ||
| }) | ||
| P_SARA <- Vectorize(function(j,i){ | ||
| integrate(function(x) dnorm(x, mean= ds[j]-b) * pnorm(x, mean=b), | ||
| lower = c_RA[i], | ||
| upper = c_RA[i+1], | ||
| rel.tol = 10^-8)$value | ||
| }) | ||
|
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| p_SB_RB <- outer(1:nCond, 1:nRatings, P_SBRB) | ||
| p_SB_RA <- outer(1:nCond, 1:nRatings, P_SBRA) | ||
| p_SA_RA <- outer(1:nCond, 1:nRatings, P_SARA) | ||
| p_SA_RB <- outer(1:nCond, 1:nRatings, P_SARB) | ||
|
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| p_SB_RB[(is.na(p_SB_RB))| is.nan(p_SB_RB)| p_SB_RB < 1e-20] <- 1e-20 | ||
| p_SB_RA[(is.na(p_SB_RA))| is.nan(p_SB_RA)| p_SB_RA < 1e-20] <- 1e-20 | ||
| p_SA_RB[(is.na(p_SA_RB))| is.nan(p_SA_RB)| p_SA_RB < 1e-20] <- 1e-20 | ||
| p_SA_RA[(is.na(p_SA_RA))| is.nan(p_SA_RA)| p_SA_RA < 1e-20] <- 1e-20 | ||
| 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)) | ||
|
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||
| negLogL | ||
| } |
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