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.)

The array version of this function (compute_marg_PR_nested_reg_array) is used in plot_etiology_regression

Usage

compute_marg_PR_nested_reg(ThetaBS, PsiBS, pEti_mat, subwt_mat, case, template)

Arguments

ThetaBS

True positive rates for JBrS measures (rows) among K subclasses (columns)

PsiBS

False positive rates; dimension same as above

pEti_mat

a matrix of etiology pies for N subjects (rows) and Jcause causes (columns) rows sum to ones.

subwt_mat

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.

Value

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).