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.

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

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