Set up exemplar matrix
- columns correspond to cues, last column is criterion value
- rows correspond to exemplars
exemplars <- matrix(
ncol=5,byrow=TRUE,dimnames = list(NULL,c("c1","c2","c3","c4","crit")),
data = c(
0,0,0,1,23,
0,0,1,0,25,
0,1,0,0,30,
0,1,0,1,43,
1,0,0,0,35,
1,0,1,1,70,
1,1,0,1,68,
1,1,1,0,63))
Set simulation parameters and simulate data
nsim <- 100000
cues <- exemplars[,1:4]
sim_data_alpha_0.0<-simulate_rulexj(n=nsim,cues=cues,exemplars=exemplars,alpha_fix=0.0,w_sum_max=100)
sim_data_alpha_0.5<-simulate_rulexj(n=nsim,cues=cues,exemplars=exemplars,alpha_fix=0.5,w_sum_max=100)
sim_data_alpha_1.0<-simulate_rulexj(n=nsim,cues=cues,exemplars=exemplars,alpha_fix=1.0,w_sum_max=100)
sim_data_alpha_0.0$judgments <- cbind(sim_data_alpha_0.0$judgments,cue=rep(1:8,nsim))
sim_data_alpha_0.5$judgments <- cbind(sim_data_alpha_0.5$judgments,cue=rep(1:8,nsim))
sim_data_alpha_1.0$judgments <- cbind(sim_data_alpha_1.0$judgments,cue=rep(1:8,nsim))