analyze {adestr} | R Documentation |
Analyze a dataset
Description
The analyze
function can be used calculate the values of a list of
point estimators,
confidence intervals,
and p-values for a given dataset.
Usage
analyze(
data,
statistics = list(),
data_distribution,
use_full_twoarm_sampling_distribution = FALSE,
design,
sigma,
exact = FALSE
)
## S4 method for signature 'data.frame'
analyze(
data,
statistics = list(),
data_distribution,
use_full_twoarm_sampling_distribution = FALSE,
design,
sigma,
exact = FALSE
)
Arguments
data |
a data.frame containing the data to be analyzed. |
statistics |
a list of objects of class |
data_distribution |
object of class |
use_full_twoarm_sampling_distribution |
logical indicating whether this estimator is intended to be used with the full sampling distribution in a two-armed trial. |
design |
object of class |
sigma |
assumed standard deviation. |
exact |
logical indicating usage of exact n2 function. |
Details
Note that in adestr
, statistics are codes as functions of the
stage-wise sample means (and stage-wise sample variances if data_distribution is
Student
). In a first-step, the data is summarized to produce these
parameters. Then, the list of statistics are evaluated at the values of these parameters.
The output of the analyze
function also displays information on the hypothesis
test and the interim decision. If the statistics
list is empty, this will
be the only information displayed.
Value
Results
object containing the values of the statistics
when applied to data.
Examples
set.seed(123)
dat <- data.frame(
endpoint = c(rnorm(28, 0.3)),
stage = rep(1, 28)
)
analyze(data = dat,
statistics = list(),
data_distribution = Normal(FALSE),
design = get_example_design(),
sigma = 1)
# The results suggest recruiting 32 patients for the second stage
dat <- rbind(
dat,
data.frame(
endpoint = rnorm(32, mean = 0.3),
stage = rep(2, 32)))
analyze(data = dat,
statistics = get_example_statistics(),
data_distribution = Normal(FALSE),
design = get_example_design(),
sigma = 1)