sim.ci.cor

Performs a computer simulation of confidence interval performance for a
Pearson correlation. A bias adjustment is used to reduce the bias of the
Fisher transformed Pearson correlation. Sample data can be generated
from bivariate population distributions with five different marginal
distributions. All distributions are scaled to have standard deviations
of 1.0. Bivariate random data with specified marginal skewness and
kurtosis are generated using the unonr function in the mnonr package.

Implements confidence interval and sample size methods that
are especially useful in psychological research. The methods can be
applied in 1-group, 2-group, paired-samples, and multiple-group designs
and to a variety of parameters including means, medians, proportions,
slopes, standardized mean differences, standardized linear contrasts
of means, plus several measures of correlation and association.
Confidence interval and sample size functions are given for single
parameters as well as differences, ratios, and linear contrasts of
parameters. The sample size functions can be used to approximate the
sample size needed to estimate a parameter or function of parameters
with desired confidence interval precision or to perform a variety of
hypothesis tests (directional two-sided, equivalence, superiority,
noninferiority) with desired power. For details see: Statistical Methods
for Psychologists, Volumes 1 – 4, <https://dgbonett.sites.ucsc.edu/>.

Douglas G. Bonett

statpsych

Statistical Methods for Psychologists

Robert J. Calin-Jageman

sim.ci.cor function

<dl><dt>alpha</dt>
<dd>alpha level for 1-alpha confidence</dd>
<dt>n</dt>
<dd>sample size</dd>
<dt>cor</dt>
<dd>population Pearson correlation</dd>
<dt>dist1</dt>
<dd>type of distribution for variable 1 (1, 2, 3, 4, or 5)</dd>
<dt>dist2</dt>
<dd>type of distribution for variable 2 (1, 2, 3, 4, or 5)<ul>
<li>1 = Gaussian (skewness = 0 and excess kurtosis = 0)</li>
<li>2 = platykurtic (skewness = 0 and excess kurtosis = -1.2)</li>
<li>3 = leptokurtic (skewness = 0 and excess kurtosis = 6)</li>
<li>4 = moderate skew (skewness = 1 and excess kurtosis = 1.5)</li>
<li>5 = large skew (skewness = 2 and excess kurtosis = 6)</li>
</ul></dd>
<dt>rep</dt>
<dd>number of Monte Carlo samples</dd></dl>

Arguments

Simulates confidence interval coverage probability for a Pearson
correlation — sim.ci.cor

<dl>

<dt>alpha</dt>
<dd>alpha level for 1-alpha confidence</dd>


<dt>n</dt>
<dd>sample size</dd>


<dt>cor</dt>
<dd>population Pearson correlation</dd>


<dt>dist1</dt>
<dd>type of distribution for variable 1 (1, 2, 3, 4, or 5)</dd>


<dt>dist2</dt>
<dd>type of distribution for variable 2 (1, 2, 3, 4, or 5)<ul>
<li>1 = Gaussian (skewness = 0 and excess kurtosis = 0)</li>
<li>2 = platykurtic (skewness = 0 and excess kurtosis = -1.2)</li>
<li>3 = leptokurtic (skewness = 0 and excess kurtosis = 6)</li>
<li>4 = moderate skew (skewness = 1 and excess kurtosis = 1.5)</li>
<li>5 = large skew (skewness = 2 and excess kurtosis = 6)</li>
</ul></dd>


<dt>rep</dt>
<dd>number of Monte Carlo samples</dd>

</dl>

sim.ci.cor: Simulates confidence interval coverage probability for a Pearson correlation

Description

Usage

Value

Arguments

Examples