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Set prior distributions for the meta-analysis model

Source: R/priors.R
set_priors.Rd

Construct a did_priors object specifying the prior distribution for each population-level parameter. Any parameter not supplied takes a default.

Usage

set_priors(
  treatment_effect_mean = normal(0, 10),
  treatment_effect_sd = cauchy(5),
  time_trend_mean = normal(0, 10),
  time_trend_sd = cauchy(5),
  rho_mean = normal(0, 1),
  rho_sd = normal(0, 0.5),
  nu = gamma(2, 0.1),
  delta_rct = normal(0, 10),
  delta_pp = normal(0, 10),
  sigma = cauchy(5),
  beta_cov = normal(0, 10)
)

Arguments

treatment_effect_mean

Prior on the population treatment effect mean. Default: normal(0, 10).

treatment_effect_sd

Prior on the between-study SD. Default: cauchy(5).

time_trend_mean

Prior on the population time-trend mean. Default: normal(0, 10).

time_trend_sd

Prior on the between-study time-trend SD. Default: cauchy(5).

rho_mean

Prior on the mean of the Fisher-z transformed pre-post correlation (only used when hierarchical_rho = TRUE). Default: normal(0, 1).

rho_sd

Prior on the SD of the Fisher-z transformed pre-post correlation (only used when hierarchical_rho = TRUE). Default: normal(0, 0.5).

nu

Prior on the degrees of freedom for between-study heterogeneity (only used when robust_heterogeneity = TRUE). Default: gamma(2, 0.1).

delta_rct

Prior on the RCT design offset relative to DiD (only used when design_effects = TRUE). Default: normal(0, 10).

delta_pp

Prior on the Pre-Post design offset relative to DiD (only used when design_effects = TRUE). Default: normal(0, 10).

sigma

Prior on the study-level observation standard deviations (shared across all designs). Default: cauchy(5).

beta_cov

Prior on the covariate regression coefficients (only used when covariates is specified in meta_did()). Default: normal(0, 10).

Value

A did_priors object.

Examples

# Use defaults
set_priors()
#> Prior distributions:
#>   treatment_effect_mean ~ normal(mean = 0, sd = 10)
#>   treatment_effect_sd ~ cauchy(scale = 5)
#>   time_trend_mean ~ normal(mean = 0, sd = 10)
#>   time_trend_sd ~ cauchy(scale = 5)
#>   rho_mean ~ normal(mean = 0, sd = 1)
#>   rho_sd ~ normal(mean = 0, sd = 0.5)
#>   nu ~ gamma(shape = 2, rate = 0.1)
#>   delta_rct ~ normal(mean = 0, sd = 10)
#>   delta_pp ~ normal(mean = 0, sd = 10)
#>   sigma ~ cauchy(scale = 5)
#>   beta_cov ~ normal(mean = 0, sd = 10)
#>   lkj_eta ~ lkj(eta = 2)

# Override one prior
set_priors(treatment_effect_sd = cauchy(2))
#> Prior distributions:
#>   treatment_effect_mean ~ normal(mean = 0, sd = 10)
#>   treatment_effect_sd ~ cauchy(scale = 2)
#>   time_trend_mean ~ normal(mean = 0, sd = 10)
#>   time_trend_sd ~ cauchy(scale = 5)
#>   rho_mean ~ normal(mean = 0, sd = 1)
#>   rho_sd ~ normal(mean = 0, sd = 0.5)
#>   nu ~ gamma(shape = 2, rate = 0.1)
#>   delta_rct ~ normal(mean = 0, sd = 10)
#>   delta_pp ~ normal(mean = 0, sd = 10)
#>   sigma ~ cauchy(scale = 5)
#>   beta_cov ~ normal(mean = 0, sd = 10)
#>   lkj_eta ~ lkj(eta = 2)