EMC2 (version 3.5.0)

parameters.emc.prior: Return Data Frame of Parameters

Description

Return Data Frame of Parameters

Usage

# S3 method for emc.prior
parameters(x, selection = "mu", N = 1000, covariates = NULL, ...)
# S3 method for emc
parameters(x, selection = "mu", N = NULL, resample = FALSE, ...)
parameters(x, ...)

Value

A data frame with one row for each sample (with a subjects column if selection = "alpha" and using draws from the posterior)

Arguments

x

An emc or emc.prior object

selection

String designating parameter type (e.g. mu, sigma2, correlation, alpha)

N

Integer. How many samples to take from the posterior/prior. If NULL will return the full posterior

covariates

For priors, possible covariates in the design

...

Optional arguments that can be passed to get_pars

resample

Boolean. If TRUE will sample N samples from the posterior with replacement

Examples

# For prior inference:
# First set up a prior
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))
)
# Then set up a prior using make_prior
p_vector <- 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)
)
psd <- c(
  v_Sleft = 1, v_Sright = 1, a = .3, a_Eneutral = .3, a_Eaccuracy = .3,
  t0 = .4, Z = 1, sv = .4, SZ = 1
)
# Here we left the variance prior at default
prior_DDMaE <- prior(design_DDMaE, mu_mean = p_vector, mu_sd = psd)
# Get our prior samples
parameters(prior_DDMaE, N = 100)
# For posterior inference:
# Get 100 samples of the group-level mean (the default)
parameters(samples_LNR, N = 100)
# or from the individual-level parameters and mapped
parameters(samples_LNR, selection = "alpha", map = TRUE)