compute_marg_PR_nested_reg_array: compute positive rates for nested model with subclass mixing...
In oslerinhealth/baker: Bayesian Analysis Kit for Etiology Research

View source: R/compute_functionals_of_model.R

This is an array-version of compute_marg_PR_nested_reg. This is used in plot_etiology_regression

compute_marg_PR_nested_reg_array(
  ThetaBS_array,
  PsiBS_array,
  pEti_mat_array,
  subwt_mat_array,
  case,
  template
)

`ThetaBS_array`	An array of: True positive rates for JBrS measures (rows) among K subclasses (columns)
`PsiBS_array`	An array of: False positive rates; dimension same as above
`pEti_mat_array`	An array of: a matrix of etiology pies for N subjects (rows) and Jcause causes (columns) rows sum to ones.
`subwt_mat_array`	An array of: a matrix of subclass weights for cases and controls. N by K. Rows sum to ones.
`case`	a N-vector of 1s (cases) and 0s (controls)
`template`	a binary matrix with Jcause+1 rows (Jcause classes of cases and 1 class of controls) and JBrS columns for the Bronze-standard measurement (say, pick one type/slice). The ones in each row indicate the measurements that will show up more frequently in cases given the cause.

An array of: a matrix of values between 0 and 1 (need not to have row sums of ones); of dimension (number of subjects, dimension of the bronze-standard measurement slice).

oslerinhealth/baker documentation built on May 22, 2021, 12:05 p.m.