View source: R/simulate_rtmpt_SBC.R
sim_rtmpt_data_SBC | R Documentation |
Simulate data from RT-MPT models using rtmpt_model
objects. The difference to sim_rtmpt_data
is that here only scalars are allowed. This makes it usable for
simulation-based 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
).
sim_rtmpt_data_SBC(model, seed, n.subj, n.trials, params = NULL)
model |
A list of the class |
seed |
Random seed number. |
n.subj |
<- Number of subjects. |
n.trials |
<- Number of trials per tree. |
params |
Named list of parameters from which the data will be generated. This must be the same named list as
|
A list of the class rtmpt_sim
containing
data
: the data.frame with the simulated data,
gen_list
: a list containing lists of the group-level and subject-specific parameters for the process-related parameters and the motor-related
parameters, and the trial-specific probabilities, process-times, and motor-times,
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 simulation-based calibration. arXiv preprint arXiv:1804.06788.
######################################################################################## # Detect-Guess variant of the Two-High Threshold model. # The encoding and motor execution times are assumed to be different for each response. ######################################################################################## mdl_2HTM <- " # targets do+(1-do)*g ; 0 (1-do)*(1-g) ; 1 # lures (1-dn)*g ; 0 dn+(1-dn)*(1-g) ; 1 # do: detect old; dn: detect new; 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) sim_dat <- rtmpt:::sim_rtmpt_data_SBC(model, seed = 123, n.subj = 40, n.trials = 30, params = params)
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