ci.mape

Computes a confidence interval for a population mean absolute prediction
error (MAPE) in a general linear model. The MAPE is a more robust
alternative to the residual standard deviation. This function requires a
vector of estimated residuals from a general linear model. This confidence
interval does not assume zero excess kurtosis but does assume symmetry of
the population prediction errors.

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.mape function

<dl><dt>alpha</dt>
<dd>alpha level for 1-alpha confidence</dd>
<dt>res</dt>
<dd>vector of residuals</dd>
<dt>s</dt>
<dd>number of predictor variables in model</dd></dl>

Arguments

Confidence interval for a mean absolute prediction error — ci.mape

<dl>

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


<dt>res</dt>
<dd>vector of residuals</dd>


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

</dl>

ci.mape: Confidence interval for a mean absolute prediction error

Description

Usage

Value

Arguments

Examples