generateNormalData

The designs supported are "oneSample", "paired", "twoSample".

Functions to design and apply tests that are anytime valid. The
functions can be used to design hypothesis tests in the prospective/randomised
control trial setting or in the observational/retrospective setting. The
resulting tests remain valid under both optional stopping and optional
continuation. The current version includes safe t-tests and safe tests of
two proportions. For details on the theory of safe tests, see
Grunwald, de Heide and Koolen (2019) "Safe Testing" <arXiv:1906.07801>,
for details on safe logrank tests see ter Schure, Perez-Ortiz, Ly and Grunwald
(2020) "The Safe Logrank Test: Error Control under Continuous Monitoring with
Unlimited Horizon" <arXiv:2011.06931v3> and Turner, Ly and Grunwald (2021)
"Safe Tests and Always-Valid Confidence Intervals for contingency tables and
beyond" <arXiv:2106.02693> for details on safe contingency table tests.

Alexander Ly

safestats

Safe Anytime-Valid Inference

Rosanne Turner

Muriel Felipe Perez-Ortiz

Judith ter Schure

Peter Grunwald

generateNormalData function

<dl><dt>nPlan</dt>
<dd>vector of max length 2 representing the planned sample sizes.</dd>
<dt>nSim</dt>
<dd>the number of replications, that is, experiments with max samples nPlan.</dd>
<dt>deltaTrue</dt>
<dd>numeric, the value of the true standardised effect size (test-relevant parameter).</dd>
<dt>muGlobal</dt>
<dd>numeric, the true global mean of a paired or two-sample t-test. Its value should not
matter for the test. This parameter is treated as a nuisance.</dd>
<dt>sigmaTrue</dt>
<dd>numeric &gt; 0,the true standard deviation of the data. Its value should not matter
for the test.This parameter treated is treated as a nuisance.</dd>
<dt>paired</dt>
<dd>logical, if <code>TRUE</code> then paired t-test.</dd>
<dt>seed</dt>
<dd>To set the seed for the simulated data.</dd>
<dt>muTrue</dt>
<dd>numeric representing the true mean for simulations with a z-test.
Default <code>NULL</code></dd></dl>

Arguments

Generates Normally Distributed Data Depending on the Design — generateNormalData

<dl>

<dt>nPlan</dt>
<dd>vector of max length 2 representing the planned sample sizes.</dd>


<dt>nSim</dt>
<dd>the number of replications, that is, experiments with max samples nPlan.</dd>


<dt>deltaTrue</dt>
<dd>numeric, the value of the true standardised effect size (test-relevant parameter).</dd>


<dt>muGlobal</dt>
<dd>numeric, the true global mean of a paired or two-sample t-test. Its value should not
matter for the test. This parameter is treated as a nuisance.</dd>


<dt>sigmaTrue</dt>
<dd>numeric &gt; 0,the true standard deviation of the data. Its value should not matter
for the test.This parameter treated is treated as a nuisance.</dd>


<dt>paired</dt>
<dd>logical, if <code>TRUE</code> then paired t-test.</dd>


<dt>seed</dt>
<dd>To set the seed for the simulated data.</dd>


<dt>muTrue</dt>
<dd>numeric representing the true mean for simulations with a z-test.
Default <code>NULL</code></dd>

</dl>

generateNormalData: Generates Normally Distributed Data Depending on the Design

Description

Usage

Value

Arguments

Examples