Extracts a summary of the prior in a structured data format.
Arguments
- object
blrmfit(blrm_trial) object as returned fromblrm_exnex()(blrm_trial()) analysis- digits
number of digits to show
- ...
ignored by the function
Value
Returns an analysis specific list, which has it's own
print function. The returned list contains arrays which
represent the prior in a structured format.
Details
The summary of the prior creates a structured
representation of the specified prior from a
blrm_exnex() (blrm_trial()) analysis.
Examples
.user_mc_options <- options()
## run combo2 analysis which defines blrmfit model object
example_model("combo2", silent = TRUE)
prior_summary(blrmfit)
#> Bayesian Logistic Regression Model with EXchangeability-NonEXchangeability
#>
#> Mixture configuration
#> ---------------------
#> EXNEX components : 0
#> component
#> I(log(drug_A/dref[1])) I(log(drug_B/dref[2]))
#> 0 0
#>
#> EXNEX interactions: 0
#> interaction
#> I(drug_A/dref[1] * drug_B/dref[2])
#> 0
#>
#> Prior probability for exchangeability per group
#> component
#> group I(log(drug_A/dref[1])) I(log(drug_B/dref[2]))
#> trial_A 1 1
#> trial_B 1 1
#> IIT 1 1
#> trial_AB 1 1
#>
#> interaction
#> group I(drug_A/dref[1] * drug_B/dref[2])
#> trial_A 1
#> trial_B 1
#> IIT 1
#> trial_AB 1
#>
#> EXchangable hyperparameter priors
#> ---------------------------------
#> Component parameters
#> Mean mu_log_beta
#> prior weight m_intercept m_log_slope s_intercept s_log_slope rho
#> component mix
#> I(log(drug_A/dref[1])) comp_1 1.0 -1.4 0.0 2.0 1.0 0.0
#> I(log(drug_B/dref[2])) comp_1 1.0 -1.4 0.0 2.0 1.0 0.0
#>
#> Heterogeneity tau_log_beta (log-normal)
#> prior weight m_tau_intercept m_tau_log_slope s_tau_intercept s_tau_log_slope rho
#> stratum component mix
#> stratum_1 I(log(drug_A/dref[1])) comp_1 1.00 -1.39 -2.08 0.71 0.71 0.00
#> I(log(drug_B/dref[2])) comp_1 1.00 -1.39 -2.08 0.71 0.71 0.00
#>
#> Correlation LKJ
#> component
#> I(log(drug_A/dref[1])) I(log(drug_B/dref[2]))
#> 1 1
#>
#> Interaction parameters
#> Mean mu_eta
#> prior w m[1] s[1]
#> mix
#> comp1 1.0 0.0 1.1
#>
#> Heterogeneity tau_eta (log-normal)
#> prior w m[1] s[1]
#> stratum mix
#> stratum_1 comp1 1.00 -2.08 0.71
#>
#> Correlation LKJ
#> interaction
#> 1
#>
#> NonEXchangable priors
#> ---------------------
#> Component parameters
#> Mean mu_log_beta
#> prior weight m_intercept m_log_slope s_intercept s_log_slope rho
#> component mix
#> I(log(drug_A/dref[1])) comp_1 1.0 -1.4 0.0 2.0 1.0 0.0
#> I(log(drug_B/dref[2])) comp_1 1.0 -1.4 0.0 2.0 1.0 0.0
#>
#> Interaction parameters
#> Mean mu_eta
#> prior w m[1] s[1]
#> mix
#> comp1 1.0 0.0 1.1
prior_sum <- prior_summary(blrmfit)
names(prior_sum)
#> [1] "has_inter" "is_EXNEX_comp" "EX_prob_comp"
#> [4] "EX_mu_log_beta" "NEX_mu_log_beta" "EX_tau_log_beta"
#> [7] "EX_corr_eta_comp" "tau_dist" "is_EXNEX_inter"
#> [10] "EX_prob_inter" "EX_mu_eta" "NEX_mu_eta"
#> [13] "EX_tau_eta" "EX_corr_eta_inter" "num_strata"
#> [16] "num_groups"
## the entries of the prior list are labelled arrays
dimnames(prior_sum$EX_mu_log_beta)
#> $prior
#> [1] "weight" "m_intercept" "m_log_slope" "s_intercept" "s_log_slope"
#> [6] "rho"
#>
#> $component
#> [1] "I(log(drug_A/dref[1]))" "I(log(drug_B/dref[2]))"
#>
#> $mix
#> [1] "comp_1"
#>
## Recover user set sampling defaults
options(.user_mc_options)