View source: R/SBC_fit_rtmpt.R
fit_rtmpt_SBC  R Documentation 
Simulate data from RTMPT models using rtmpt_model
objects. The difference to sim_rtmpt_data
is that here only scalars are allowed. This makes it usable for
simulationbased calibration (SBC; Talts et al., 2018). You can specify the random seed, number of subjects, number of trials, and some
parameters (same as prior_params
from fit_rtmpt
).
fit_rtmpt_SBC( model, seed, n.eff_samples = 99, n.chains = 4, n.iter = 5000, n.burnin = 200, n.thin = 1, Rhat_max = 1.05, Irep = 1000, n.subj = 40, n.trials = 30, prior_params = NULL, sim_list = NULL )
model 
A list of the class 
seed 
Random seed number. 
n.eff_samples 
Number of effective samples. Default is 99, leading to 100 possible ranks (from 0 to 99). 
n.chains 
Number of chains to use. Default is 4. Must be larger than 1 and smaller or equal to 16. 
n.iter 
Number of samples per chain. Default is 5000. Must be larger or equal to 
n.burnin 
Number of warmup samples. Default is 200. 
n.thin 
Thinning factor. Default is 1. 
Rhat_max 
Maximal Potential scale reduction factor: A lower threshold that needs to be reached before the actual sampling starts. Default is 1.05 
Irep 
Every

n.subj 
Number of subjects. Default is 40. 
n.trials 
Number of trials per tree. Default is 30. 
prior_params 
Named list of parameters from which the data will be generated. This must be the same named list as

sim_list 
Object of class 
A list of the class rtmpt_sbc
containing
ranks
: the rank statistic for all parameters,
sim_list
: an object of the class rtmpt_sim
,
fit_list
: an object of the class rtmpt_fit
,
specs
: some specifications like the model, seed number, etc.,
Raphael Hartmann
Talts, S., Betancourt, M., Simpson, D., Vehtari, A., & Gelman, A. (2018). Validating Bayesian inference algorithms with simulationbased calibration. arXiv preprint arXiv:1804.06788.
######################################################################################## # DetectGuess variant of the TwoHigh Threshold model. # The encoding and motor execution times are assumed to be different for each response. ######################################################################################## mdl_2HTM < " # targets d+(1d)*g ; 0 (1d)*(1g) ; 1 # lures (1d)*g ; 0 d+(1d)*(1g) ; 1 # d: detect; g: guess " model < to_rtmpt_model(mdl_file = mdl_2HTM) params < list(mean_of_exp_mu_beta = 10, var_of_exp_mu_beta = 10, mean_of_mu_gamma = 0.5, var_of_mu_gamma = 0.0025, mean_of_omega_sqr = 0.005, var_of_omega_sqr = 0.000025, df_of_sigma_sqr = 10, sf_of_scale_matrix_SIGMA = 0.1, sf_of_scale_matrix_GAMMA = 0.01, prec_epsilon = 10, add_df_to_invWish = 5) R = 2 # typically 2000 with n.eff_samples = 99, but this will run many days rank_mat < matrix(NA, ncol = 393, nrow = 2) for (r in 1:R) { SBC_out < fit_rtmpt_SBC(model, seed = r*123, prior_params = params, n.eff_samples = 99, n.thin = 5, n.iter = 5000, n.burnin = 2000, Irep = 5000) rank_mat[r, ] < SBC_out$ranks }
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