Description Usage Arguments Value
fitBabyMonitor
comprehensively applies the Baby-MONITOR score
Returns a large list. Use inst_mat for institution rankings,
full_subset_mat_baseline and full_subset_mat_nobaseline for subset rankings,
and subset_baseline_mat and subset_nobaseline_mat for subset rankings within institution.
1 2 3 4 | fitBabyMonitor(minimal_data, num_cat, num_cont, var_intercept, var_inst,
var_cat, var_cat_2way, var_cont, var_subset = 1, var_inst_subset_2way = 1,
subset = FALSE, fit_method = "probit", sparse = TRUE, burn_in = 100,
iters = 1000, alpha = 0.05, verbose = TRUE)
|
minimal_data |
Data_frame with a particular format: 1st column: Outcome vector (0-1 encoding) 2nd column: Institution ID 3rd column (if subset == TRUE): Next: num_cat columns of categorical variables (num_cat can equal 0) Next: num_cont columns of continuous variables (num_cont can equal 0) |
num_cat |
Scalar number of categorical variables. |
num_cont |
Number of continuous variables. |
var_intercept |
Prior variance for intercept parameter. |
var_inst |
Prior variance for institution parameters. |
var_cat |
Prior variance for categorical parameters. |
var_cat_2way |
Prior variance for categorical interaction parameters. |
var_cont |
Prior variance for continuous parameters. |
var_subset |
Prior variance for subset parameters. |
var_inst_subset_2way |
Prior variance for subset interaction parameters. |
subset |
If TRUE, perform subset fitting tasks |
fit_method |
Analysis method. Options: 'probit' for Probit Regression, 'bayesianregression' for Bayesian linear regression, and 'probitk' for a Probit Regression implementation written by Daniel Kirsner. |
sparse |
Should design_matrix be stored as Sparse matrix? Requires Matrix package. |
burn_in |
Number of initial iterations to discard for burn in. |
iters |
Number of MCMC iterations to use. |
alpha |
We look at posterior (1-alpha)% posterior intervals. |
verbose |
If TRUE, display status messages while fitting |
Returns a large list with the following components:
inst_mat: Matrix (one row per institution) computing summary statistics, D-G institution ranking, and intervals
full_subset_mat_baseline, full_subset_mat_nobaseline: Matrix with rankings and intervals for the various subset categories.
subset_baseline_mat, subset_nobaseline_mat: Matrix with rankings and intervals for subset categories within institution.
group_labels: A vector of the names of each institution
mcmc_fit: Matrix of MCMC iterations for each coefficient dg_z: Matrix of computed z score for each institution at each MCMC iterations.
coefs: Names of each coefficient (1st is intercept, then institution, then everything else)
prior_var_vec: Vector of prior variances for each coefficient
model_matrix: Design matrix
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