polio

<p>Time series of Polio incidences in U.S.A. from 1970 to 1983.</p>

datasets

Likelihood inference in Gaussian copula marginal
regression models.

Cristiano Varin

gcmr

Gaussian Copula Marginal Regression

polio function

<p>A data frame with the 168 monthly observations (from January 1970 to December 1983) with the following variables</p><table><tr><td><p></p><p><code>y</code> </p></td>
<td> time series of polio incidences. </td>
</tr><tr><td>
<code>t*10^( -3 )</code> </td>
<td> linear trend multiplied by factor \(10^{(-3)}\). </td>
</tr><tr><td>
<code>cos( 2*pi*t/12 )</code> </td>
<td> cosine annual seasonal component. </td>
</tr><tr><td>
<code>sin( 2*pi*t/12 )</code> </td>
<td> sine annual seasonal component. </td>
</tr><tr><td>
<code>cos( 2*pi*t/6 )</code> </td>
<td> cosine semi-annual seasonal component. </td>
</tr><tr><td>
<code>sin( 2*pi*t/6 )</code> </td>
<td> sine semi-annual seasonal component. </td>
</tr></table>

polio: Polio Time Series

Description

Usage

Arguments

Format

Examples

`y`	time series of polio incidences.
`t*10^( -3 )`	linear trend multiplied by factor \(10^{(-3)}\).
`cos( 2pit/12 )`	cosine annual seasonal component.
`sin( 2pit/12 )`	sine annual seasonal component.
`cos( 2pit/6 )`	cosine semi-annual seasonal component.
`sin( 2pit/6 )`	sine semi-annual seasonal component.