Initialise the random effects for each subject using MCMC.
init(
pmwgs,
start_mu = NULL,
start_sig = NULL,
display_progress = TRUE,
particles = 100
)The sampler object that provides the parameters.
An array of starting values for the group means
An array of starting values for the group covariance matrix
Display a progress bar during sampling
The number of particles to generate in initialisation
The sampler object but with initial values set for theta_mu,
theta_sig, alpha and other values for the first sample.
Before sampling can start the Particle Metropolis within Gibbs sampler needs
initial values for the random effects. The init function generates
these values using a Monte Carlo algorithm. One alternative methods would be
setting the initial values randomly.
Optionally takes starting values for the model parameters and the variance / covariance matrix. All arrays must match the appropriate shape.
For example, with 5 parameters and 10 subjects, the model parameter start means must be a vector of length 5 and the covariance matrix must be an array of 5 x 5.
If the start_mu and start_sig arguments are left at the default (NULL) then start_mu will be sampled from a normal distribution with mean as the prior mean for eac variable and sd as the square of the variance from the prior covariance matrix. start_sig by default is sampled from an inverse wishart (IW) distribution. For a model with the number of parameters N the degrees of freedom of the IW distribution is set to N*3 and the scale matrix is the identity matrix of size NxN.
lba_ll <- function(x, data) {
x <- exp(x)
if (any(data$rt < x["t0"])) {
return(-1e10)
}
sum(
log(
rtdists::dLBA(
rt = data$rt,
response = data$correct,
A = x["A"],
b = x["A"] + x[c("b1", "b2", "b3")][data$condition],
t0 = x["t0"],
mean_v = x[c("v1", "v2")],
sd_v = c(1, 1),
silent = TRUE
)
)
)
}
sampler <- pmwgs(
forstmann,
c("b1", "b2", "b3", "A", "v1", "v2", "t0"),
lba_ll
)
sampler <- init(sampler, particles=10)
#> MESSAGE: Sampling Initial values for random effects
#>
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