Title

Title

Usage

plot_prior(
  prior,
  design,
  selection = "mu",
  do_plot = TRUE,
  covariates = NULL,
  layout = NA,
  N = 50000,
  ...
)

Arguments

prior

A prior list created with prior

design

A design list created with design

selection

A Character string. Indicates which parameter type to use (e.g., alpha, mu, sigma2, correlation).

do_plot

Boolean. If FALSE will only return prior samples and omit plotting.

covariates

dataframe/functions as specified by the design

layout

A vector indicating which layout to use as in par(mfrow = layout). If NA, will automatically generate an appropriate layout.

N

Integer. How many prior samples to draw

...

Optional arguments that can be passed to get_pars, histogram, plot.default (see par()), or arguments required for the types of models e.g. n_factors for type = "factor"

Value

An mcmc.list object with prior samples of the selected type

Examples

# \donttest{
# 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 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)
# Now we can plot all sorts of (implied) priors
plot_prior(prior_DDMaE, design_DDMaE, selection = "mu", N = 1e3)

plot_prior(prior_DDMaE, design_DDMaE, selection = "mu", mapped = FALSE, N=1e3)

# We can also plot the implied prior on the participant level effects.
plot_prior(prior_DDMaE, design_DDMaE, selection = "alpha", col = "green", N = 1e3)

# }