EMC2 (version 3.5.0)

prior: Specify Priors for the Chosen Model

Description

These values are entered manually by default but can be recycled from another prior (given in the update argument).

Usage

prior(
  design,
  type = NULL,
  group_design = NULL,
  update = NULL,
  do_ask = NULL,
  fill_default = TRUE,
  ...
)

Value

A prior list object

Arguments

design

Design list for which a prior is constructed, typically the output of design()

type

Character. What type of group-level model you plan on using i.e. diagonal

group_design

An emc.group_design object created with group_design()

update

Prior list from which to copy values

do_ask

Character. For which parameter types or hyperparameters to ask for prior specification, i.e. Sigma, mu or loadings for factor models, but theta_mu_mean or A also works.

fill_default

Boolean, If TRUE will fill all non-specified parameters, and parameters outside of do_ask, to default values

...

Either values to prefill, i.e. theta_mu_mean = c(1:6), or additional arguments such as n_factors = 2

Details

Where a value is not supplied, the user is prompted to enter numeric values (or functions that evaluate to numbers).

To get the prior help use prior_help(type). With type e.g. 'diagonal'.

Examples

# First define a design for the model
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 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)
# Also add a group-level variance prior:
pscale <- c(
  v_Sleft = .6, v_Sright = .6, a = .3, a_Eneutral = .3, a_Eaccuracy = .3,
  t0 = .2, Z = .5, sv = .4, SZ = .3
)
df <- .4
prior_DDMaE <- prior(design_DDMaE, mu_mean = p_vector, mu_sd = psd, A = pscale, df = df)
# If we specify a new design
design_DDMat0E <- design(
  data = forstmann, model = DDM,
  formula = list(v ~ 0 + S, a ~ E, t0 ~ E, s ~ 1, Z ~ 1, sv ~ 1, SZ ~ 1),
  constants = c(s = log(1))
)
# We can easily update the prior
prior_DDMat0E <- prior(design_DDMat0E, update = prior_DDMaE)