EYapprox: Approximates the conditional expectations of individual...
In gregorkb/aenetgt: Adaptive Elastic Net for Group Testing Data
EYapprox R Documentation
Approximates the conditional expectations of individual disease statuses with Gibbs sampling.
Description
Approximates the conditional expectations of individual disease statuses with Gibbs sampling.
Usage
EYapprox(Z, Y, X, b, Se, Sp, GI = 5000)
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.
X
Design matrix with first column a column of 1s.
b
Parameter values at which to compute the conditional expectations.
Se
A vector of testing sensitivities of length max(Z[,3]).
Sp
A vector of testing specificities of length max(Z[,3]).
GI
The length of the Gibbs sampling Markov chain.
Value
The vector of conditional expectations.
This function uses a Gibbs sampler to appriximate the conditional expectation of
each individual's 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 under array testing.
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 array testing
assay.data <- array.assay.gen(Y.true,Se,Sp,cj)
Z <- assay.data$Z
Y <- assay.data$Y
b <- data$b
EY <- EYapprox(Z,Y,X,b,Se,Sp,GI=5000)
gregorkb/aenetgt documentation built on Oct. 12, 2022, 11:51 a.m.
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| EYapprox | R Documentation |
Approximates the conditional expectations of individual disease statuses with Gibbs sampling.
Description
Approximates the conditional expectations of individual disease statuses with Gibbs sampling.
Usage
EYapprox(Z, Y, X, b, Se, Sp, GI = 5000)
Arguments
Z |
Group testing output from one of the functions |
Y |
Group testing output from one of the functions |
X |
Design matrix with first column a column of 1s. |
b |
Parameter values at which to compute the conditional expectations. |
Se |
A vector of testing sensitivities of length |
Sp |
A vector of testing specificities of length |
GI |
The length of the Gibbs sampling Markov chain. |
Value
The vector of conditional expectations.
This function uses a Gibbs sampler to appriximate the conditional expectation of
each individual's disease status, conditional on the observed assay data and the disease
statuses of all other individuals. This function is used in the EM algorithm
performed by the functions mlegt, enetgt, enetgt.grid, and
enetgt.grid.0 under array testing.
Examples
# generate individual covariate values and disease statuses N <- 100 data <- model1(N) X <- data$X Y.true <- data$Yi Se <- c(.95,.92) # set master pool and individual assay sensitivity Sp <- c(.97,.98) # set master pool and individual assay specificity cj <- 4 # set size of master pools # subject individuals to array testing assay.data <- array.assay.gen(Y.true,Se,Sp,cj) Z <- assay.data$Z Y <- assay.data$Y b <- data$b EY <- EYapprox(Z,Y,X,b,Se,Sp,GI=5000)
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