Plot a prior

Takes a prior object and plots the selected implied prior

Usage

# S3 method for class 'emc.prior'
plot(
  x,
  selection = "mu",
  do_plot = TRUE,
  covariates = NULL,
  layout = NA,
  N = 5000,
  ...
)

Arguments

x

An emc_prior element

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 invisible 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_DDMaE, selection = "mu", N = 1e2)

plot(prior_DDMaE, selection = "mu", mapped = FALSE, N = 1e2)

# We can also plot the implied prior on the participant level effects.
plot(prior_DDMaE, selection = "alpha", col = "green", N = 1e2)



















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