ci.rsqr

Computes an approximate confidence interval for a population squared
multiple correlation in a linear model with random predictor variables.
This function uses the scaled central F approximation method. An
approximate standard error is recovered from the confidence interval.
For more details, see Section 2.4 of Bonett (2021, Volume 2)

Implements confidence interval and sample size methods that are especially
useful in psychological research but are also useful in educational, social science,
business, and biological research. This package includes more than 100 confidence
interval functions and more than 80 sample size functions for 1-group, 2-group,
paired-samples, and multiple-group designs and for a variety of parameters including
means, medians, proportions, slopes, standardized mean differences, standardized
linear contrasts of means, and several measures of correlation and association. 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 about these methods 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

ci.rsqr function

<dl><dt>alpha</dt>
<dd>alpha value for 1-alpha confidence</dd>
<dt>r2</dt>
<dd>estimated unadjusted squared multiple correlation</dd>
<dt>s</dt>
<dd>number of predictor variables</dd>
<dt>n</dt>
<dd>sample size</dd></dl>

Arguments

Confidence interval for squared multiple correlation — ci.rsqr

<dl>

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


<dt>r2</dt>
<dd>estimated unadjusted squared multiple correlation</dd>


<dt>s</dt>
<dd>number of predictor variables</dd>


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

</dl>

ci.rsqr: Confidence interval for squared multiple correlation

Description

Usage

Value

Arguments

References

Examples