compute positive rates for nested model with subclass mixing weights that are the same across Jcause classes for each person (people may have different weights.)
Source: R/compute_functionals_of_model.R
compute_marg_PR_nested_reg_array.RdThis is an array-version of compute_marg_PR_nested_reg. This is used in plot_etiology_regression
Usage
compute_marg_PR_nested_reg_array(
ThetaBS_array,
PsiBS_array,
pEti_mat_array,
subwt_mat_array,
case,
template
)Arguments
- 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.