prior: Specify Priors for the Chosen Model

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

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,
  update = NULL,
  do_ask = NULL,
  fill_default = TRUE,
  ...
)

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
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'.

Value

A prior list object

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)

EMC2 documentation built on April 11, 2025, 5:50 p.m.