brms family for the metad' model

brms family for the metad' model

Usage

metad(K, distribution = "normal", metac_absolute = TRUE, categorical = FALSE)

Arguments

K: The number of confidence levels
distribution: The noise distribution to use for the signal detection model
metac_absolute: If TRUE, fix the type 2 criterion to be equal to the type 1 criterion. Otherwise, equate the criteria relatively such that $$\frac{\textrm{meta-}c}{\textrm{meta-}d'} = \frac{c}{d'}$$
categorical: If FALSE (default), use the multinomial likelihood over aggregated data. If TRUE, use the categorical likelihood over individual trials.

Value

A brms family for the metad' model with $K$ confidence levels

Examples

# create a family using the normal distribution and 3 levels of confidence
metad(3)
#> 
#> Custom family: metad__3__normal__absolute__multinomial 
#> Link function: log 
#> Parameters: mu, dprime, c, metac2zero1diff, metac2zero2diff, metac2one1diff, metac2one2diff 
#> 

# create a family with meta_c = M * c
metad(3, metac_absolute = FALSE)
#> 
#> Custom family: metad__3__normal__relative__multinomial 
#> Link function: log 
#> Parameters: mu, dprime, c, metac2zero1diff, metac2zero2diff, metac2one1diff, metac2one2diff 
#> 

# create a family with an alternative distribution
# note: cumulative distribution functions must be defined
# in R and in Stan using [brms::stanvar()]
metad(4, distribution = "gumbel_min")
#> 
#> Custom family: metad__4__gumbel_min__absolute__multinomial 
#> Link function: log 
#> Parameters: mu, dprime, c, metac2zero1diff, metac2zero2diff, metac2zero3diff, metac2one1diff, metac2one2diff, metac2one3diff 
#>