Adds a set of start points to each chain. These start points are sampled from a user-defined multivariate normal across subjects.
Usage
init_chains(
emc,
start_mu = NULL,
start_var = NULL,
particles = 1000,
cores_per_chain = 1,
cores_for_chains = length(emc)
)Arguments
- emc
An emc object made by
make_emc()- start_mu
A vector. Mean of multivariate normal used in proposal distribution
- start_var
A matrix. Variance covariance matrix of multivariate normal used in proposal distribution. Smaller values will lead to less deviation around the mean.
- particles
An integer. Number of starting values
- cores_per_chain
An integer. How many cores to use per chain. Parallelizes across participant calculations.
- cores_for_chains
An integer. How many cores to use to parallelize across chains. Default is the number of chains.
Examples
if (FALSE) { # \dontrun{
# Make a design and an emc object
design_DDMaE <- design(data = forstmann,model=DDM,
formula =list(v~0+S,a~E, t0~1, s~1, Z~1, sv~1, SZ~1),
constants=c(s=log(1)))
DDMaE <- make_emc(forstmann, design_DDMaE)
# set up our mean starting points (same used across subjects).
mu <- c(v_Sleft=-2,v_Sright=2,a=log(1),a_Eneutral=log(1.5),a_Eaccuracy=log(2),
t0=log(.2),Z=qnorm(.5),sv=log(.5),SZ=qnorm(.5))
# Small variances to simulate start points from a tight range
var <- diag(0.05, length(mu))
# Initialize chains, 4 cores per chain, and parallelizing across our 3 chains as well
# so 4*3 cores used.
DDMaE <- init_chains(DDMaE, start_mu = p_vector, start_var = var, cores_per_chain = 4)
# Afterwards we can just use fit
DDMaE <- fit(DDMaE, cores_per_chain = 4)
} # }