gtTest {binGroup2}  R Documentation 
Calculates pvalues for hypothesis tests of single proportions estimated from group testing experiments against a threshold proportion in the hypotheses. Available methods include the exact test, score test, and Wald test.
gtTest(n, y, s, p.hyp, alternative = "two.sided", method = "exact")
n 
integer specifying the number of groups. 
y 
integer specifying the number of positive groups. 
s 
integer specifying the common size of groups. 
p.hyp 
the hypothetical threshold proportion against which to test, specified as a number between 0 and 1. 
alternative 
character string defining the alternative hypothesis, either "two.sided", "less", or "greater". 
method 
character string defining the test method to be
used. Options include "exact" for an exact test corresponding
to the ClopperPearson confidence interval, "score" for a score
test corresponding to the Wilson confidence interval, and "Wald"
for a Wald test corresponding to the Wald confidence interval.
The Wald method is not recommended. The "exact" method uses

This function assumes equal group sizes, no testing error (i.e., 100 percent sensitivity and specificity) to test the groups, and individual units randomly assigned to the groups with identical true probability of success.
A list containing:
p.value 
the pvalue of the test 
estimate 
the estimated proportion 
p.hyp 
the threshold proportion provided by the user. 
alternative 
the alternative provided by the user. 
method 
the test method provided by the user. 
This function was originally written as bgtTest
by Frank
Schaarschmidt for the binGroup
package. Minor modifications have
been made for inclusion of the function in the binGroup2
package.
propCI
for confidence intervals in
group testing and binom.test(stats)
for the
exact test and corresponding confidence interval.
Other estimation functions:
designEst()
,
designPower()
,
gtPower()
,
gtWidth()
,
propCI()
,
propDiffCI()
# Consider the following the experiment: Tests are # performed on n=10 groups, each group has a size # of s=100 individuals. The aim is to show that # less than 0.5 percent (\eqn{p < 0.005}) of the units # in the population show a detrimental trait (positive test). # y=1 positive test and 9 negative tests are observed. gtTest(n = 10, y = 1, s = 100, p.hyp = 0.005, alternative = "less", method = "exact") # The exact test corresponds to the # limits of the ClopperPearson confidence interval # in the example of Tebbs & Bilder (2004): gtTest(n = 24, y = 3, s = 7, alternative = "two.sided", method = "exact", p.hyp = 0.0543) gtTest(n = 24, y = 3, s = 7, alternative = "two.sided", method = "exact", p.hyp = 0.0038) # Hypothesis test with a group size of 1. gtTest(n = 24, y = 3, s = 1, alternative = "two.sided", method = "exact", p.hyp = 0.1) # Further methods: gtTest(n = 24, y = 3, s = 7, alternative = "two.sided", method = "score", p.hyp = 0.0516) gtTest(n = 24, y = 3, s = 7, alternative = "two.sided", method = "Wald", p.hyp = 0.0401)