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These values are entered manually by default but can be recycled from another prior (given in the update argument).
prior(
design,
type = NULL,
update = NULL,
do_ask = NULL,
fill_default = TRUE,
...
)A prior list object
Design list for which a prior is constructed, typically the output of design()
Character. What type of group-level model you plan on using i.e. diagonal
Prior list from which to copy values
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.
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
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'.
# 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)
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