.quantilePercentiles

Function calculates smoothing spline quantiles
or linear quantiles as a fall back. Not intended for general use.
Expected predicted and residual data.
Exported to support related packages.

Miscellaneous tools and functions,
including: generate descriptive statistics tables,
format output, visualize relations among variables or check
distributions, and generic functions for residual and
model diagnostics.

Joshua F Wiley

JWileymisc

Miscellaneous Utilities and Functions

Joshua F. Wiley

.quantilePercentiles function

<dl><dt>data</dt>
<dd>A dataset of predicted and residual values.
Assumed from some sort of (probably parametric) model.</dd>
<dt>LL</dt>
<dd>The lower limit for prediction. Defaults to
<code>.1</code> to give the 10th percentile.</dd>
<dt>UL</dt>
<dd>The upper limit for prediction. Defaults to
<code>.9</code> to give the 90th percentile.</dd>
<dt>na.rm</dt>
<dd>A logical whether to remove missing values.
Defaults to <code>TRUE</code></dd>
<dt>cut</dt>
<dd>An integer, how many unique predicted values
there have to be at least for it to use quantile regression
or treat the predicted values as discrete.
Defaults to 4.</dd></dl>

Arguments

Internal Function to Calculate Quantiles — .quantilePercentiles

<dl>

<dt>data</dt>
<dd>A dataset of predicted and residual values.
Assumed from some sort of (probably parametric) model.</dd>


<dt>LL</dt>
<dd>The lower limit for prediction. Defaults to
<code>.1</code> to give the 10th percentile.</dd>


<dt>UL</dt>
<dd>The upper limit for prediction. Defaults to
<code>.9</code> to give the 90th percentile.</dd>


<dt>na.rm</dt>
<dd>A logical whether to remove missing values.
Defaults to <code>TRUE</code></dd>


<dt>cut</dt>
<dd>An integer, how many unique predicted values
there have to be at least for it to use quantile regression
or treat the predicted values as discrete.
Defaults to 4.</dd>

</dl>

.quantilePercentiles: Internal Function to Calculate Quantiles

Description

Usage

Value

Arguments