Description Usage Arguments Details Value References See Also Examples
‘gtcorr.matrix’ calculates the efficiencies of matrix group testing procedures for rectangular, diagonal, and random arrangements, allowing for correlation between units and test error.
1 
r 
number of rows in the pooling matrix. 
c 
number of columns in the pooling matrix. 
m 
cluster size. 
p 
probability of a unit testing positive. 
sigma 
pairwise correlation of two units in a cluster. 
se 
sensitivity. The probability that a pool of units tests positive given than at least one unit in that pool is positive 
sp 
specificity. The probability that a pool of units tests negative given that at least one unit in that pool is negative 
r.prime 
for a ‘rectangular’ arrangement, the number of rows in a rectangular cluster. 
c.prime 
for a ‘rectangular’ arrangement, the number of columns in a rectangular cluster. 
arrangement 
how clusters are arranged. Should be ‘rectangular’, ‘diagonal’ or ‘random’. 
model 
probability model for clusters. Should be ‘betabinomial’, ‘Madsen’, or ‘MorelNeerchal’. 
... 

One of m
, p
, sigma
, se
, or sp
can have
more than one value.
For a diagonal arrangement, r
, c
, and m
should be
equal.
For a rectangular arrangement, m
should be
r.prime*c.prime
.
See Lendle et. al. 2011 for more information.
r 

c 

m 
cluster size. 
r.prime 
number of rows in the pooling matrix. 
c.prime 
number of columns in the pooling matrix. 
param.grid 
a data frame containing the values of 
arrangement 
arrangement. 
model 
model. 
efficiency 
a vector of efficiencies, one for each row of 
Samuel D. Lendle, Michael Hudgens, and Bahjat F. Qaqish, "Group Testing for Case Identification with Correlated Responses" Submitted 2011. Biometrics.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ##Plot efficiencies of a 16 by 16 matrix procedure by arrangement
sigma < seq(0,.99, length.out=100)
sig2 < seq(0, .99, length.out=10)
diag < gtcorr.matrix(r=16, c=16, m=16, r.prime=1, c.prime=16,
arr='diag', p=.05, sigma=sigma)$efficiency
rand < gtcorr.matrix(r=16, c=16, m=16, r.prime=1, c.prime=16,
arr='rand', p=.05, sigma=sig2)$efficiency
rect1 < gtcorr.matrix(r=16, c=16, m=16, r.prime=1, c.prime=16, p=.05,
sigma=sigma)$efficiency
rect2 < gtcorr.matrix(r=16, c=16, m=16, r.prime=2, c.prime=8, p=.05,
sigma=sigma)$efficiency
rect3 < gtcorr.matrix(r=16, c=16, m=16, r.prime=4, c.prime=4, p=.05,
sigma=sigma)$efficiency
plot(sigma, diag, ylim=c(0, max(diag)), type='l', ylab="Efficiency", xlab="sigma")
lines(sig2, rand, col=2)
lines(sigma, rect3, col=3)
lines(sigma, rect2, col=4)
lines(sigma, rect1, col=5)
legend("bottomleft", c("Diagonal", "Random", "4x4 rect.", "2x8 rect.",
"1x16 rect."), lty=1, col=1:5)

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