| analyze.simstudy.prop2 {simitation} | R Documentation |
analyze.simstudy.prop2
Description
analyze.simstudy.prop2
Usage
analyze.simstudy.prop2(
test.statistics.prop2,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95,
the.quantiles = c(0.025, 0.1, 0.25, 0.5, 0.75, 0.9, 0.975)
)
Arguments
test.statistics.prop2 |
Summary information for fitting two-sample tests of proportions. Structure is in the form returned by the function simitation::sim.prop2.test(). |
alternative |
See help(prop.test). |
conf.level |
See help(prop.test). |
the.quantiles |
A numeric vector of values between 0 and 1. Summary statistics to analyze the tests will return the specified quantiles. |
Value
A list containing the following elements:
estimate.summary: Summary statistics for the estimate.
stat.summary: Summary statistics for the test statistic.
p.value.summary: Data frame with proportions of tests that rejected and did not reject the null hypothesis.
df.summary: Summary statistics for the degrees of freedom.
ci.range.summary: Summary statistics for the confidence interval range.
ci.proportion.above.null.summary: Summary statistics for the proportion of the upper confidence limit above the null value.
ci.proportion.below.null.summary: Summary statistics for the proportion of the lower confidence limit below the null value.
ci.limit.summary: Summary statistics for either the lower or upper confidence limit, depending on the specified alternative.
Examples
simdat.prop2 <- sim.prop2(nx = 30, ny = 40, px = 0.5,
py = 0.55, num.experiments = 2, experiment.name = "sim",
group.name = "treatment", x.value = "group_1", y.value = "group_2",
value.name = "correct_answer", seed = 3)
test.statistics.prop2 <- sim.prop2.test(simdat.prop2 = simdat.prop2,
p = NULL, alternative = "less", conf.level = 0.95, correct = TRUE,
experiment.name = "sim", group.name = "treatment", x.value = "group_1",
y.value = "group_2", value.name = "correct_answer")
analysis.prop2 <- analyze.simstudy.prop2(test.statistics.prop2 =
test.statistics.prop2, alternative = "less", conf.level = 0.95,
the.quantiles = c(0.025, 0.1, 0.9, 0.975))