Prior specification

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

Usage

prior(
  design,
  type = "standard",
  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

Value

A prior list object

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)))
#> Parameter(s) st0 not specified in formula and assumed constant.
#> 
#>  Sampled Parameters: 
#> [1] "v_Sleft"     "v_Sright"    "a"           "a_Eneutral"  "a_Eaccuracy"
#> [6] "t0"          "Z"           "sv"          "SZ"         
#> 
#>  Design Matrices: 
#> $v
#>      S v_Sleft v_Sright
#>   left       1        0
#>  right       0        1
#> 
#> $a
#>         E a a_Eneutral a_Eaccuracy
#>     speed 1          0           0
#>   neutral 1          1           0
#>  accuracy 1          0           1
#> 
#> $t0
#>  t0
#>   1
#> 
#> $s
#>  s
#>  1
#> 
#> $Z
#>  Z
#>  1
#> 
#> $sv
#>  sv
#>   1
#> 
#> $SZ
#>  SZ
#>   1
#> 
#> $st0
#>  st0
#>    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)))
#> Parameter(s) st0 not specified in formula and assumed constant.
#> 
#>  Sampled Parameters: 
#>  [1] "v_Sleft"      "v_Sright"     "a"            "a_Eneutral"   "a_Eaccuracy" 
#>  [6] "t0"           "t0_Eneutral"  "t0_Eaccuracy" "Z"            "sv"          
#> [11] "SZ"          
#> 
#>  Design Matrices: 
#> $v
#>      S v_Sleft v_Sright
#>   left       1        0
#>  right       0        1
#> 
#> $a
#>         E a a_Eneutral a_Eaccuracy
#>     speed 1          0           0
#>   neutral 1          1           0
#>  accuracy 1          0           1
#> 
#> $t0
#>         E t0 t0_Eneutral t0_Eaccuracy
#>     speed  1           0            0
#>   neutral  1           1            0
#>  accuracy  1           0            1
#> 
#> $s
#>  s
#>  1
#> 
#> $Z
#>  Z
#>  1
#> 
#> $sv
#>  sv
#>   1
#> 
#> $SZ
#>  SZ
#>   1
#> 
#> $st0
#>  st0
#>    1
#> 
# We can easily update the prior
prior_DDMat0E <- prior(design_DDMat0E, update = prior_DDMaE)