EYexact_R: Computes conditional expectations of individual disease...

In gregorkb/aenetgt: Adaptive Elastic Net for Group Testing Data

Computes conditional expectations of individual disease statuses.

Description

Computes conditional expectations of individual disease statuses.

Usage

EYexact_R(Z, Y, eta, Se, Sp)

Arguments

Z	Group testing output from one of the functions individual.assay.gen, masterpool.assay.gen, dorfman.assay.gen, or array.assay.gen.
Y	Group testing output from one of the functions individual.assay.gen, masterpool.assay.gen, dorfman.assay.gen, or array.assay.gen.
eta	the value of the linear predictor
Se	A vector of testing sensitivities of length max(Z[,3]).
Sp	A vector of testing specificities of length max(Z[,3]).

Value

The vector of conditional expectations.

This function computes the conditional expectations of each individual disease status, conditional on the observed assay data and the disease statuses of all other individuals. This function is used in the EM algorithm performed by the functions mlegt, enetgt, enetgt.grid, and enetgt.grid.0.

Examples

# generate individual covariate values and disease statuses
N <- 100
data <- model1(N)
X <- data$X
Y.true <- data$Yi
Se <- c(.95,.92) # set master pool and individual assay sensitivity
Sp <- c(.97,.98) # set master pool and individual assay specificity
cj <- 4 # set size of master pools
# subject individuals to Dorfman testing
assay.data <- dorfman.assay.gen(Y.true,Se,Sp,cj)
Z <- assay.data$Z
Y <- assay.data$Y
b <- data$b
eta <- X %*% b
EY <- EYexact_R(Z,Y,eta,Se,Sp)

gregorkb/aenetgt documentation built on Oct. 12, 2022, 11:51 a.m.