miss

A small sample dataset for the tutorial on data imputation

datasets

This is an EM algorithm based method for imputation of missing values in multivariate normal time series. The imputation algorithm accounts for both spatial and temporal correlation structures. Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.

Washington Junger

mtsdi

Multivariate Time Series Data Imputation

miss function

A data frame with 24 observations on the following 5 variables.<dl class="dl-horizontal">
 <dt><code>c31</code></dt><dd>a numeric vector with 1 missing observation</dd>
 <dt><code>c32</code></dt><dd>a numeric vector with 1 missing observation</dd>
 <dt><code>c33</code></dt><dd>a numeric vector with 6 missing observations</dd>
 <dt><code>c34</code></dt><dd>a numeric vector with 3 missing observations</dd>
 <dt><code>c35</code></dt><dd>a numeric vector with 3 missing observations</dd>
 </dl>

Format

Sample Dataset — miss

A data frame with 24 observations on the following 5 variables.<dl class='dl-horizontal'>
 <dt><code>c31</code></dt><dd>a numeric vector with 1 missing observation</dd>
 <dt><code>c32</code></dt><dd>a numeric vector with 1 missing observation</dd>
 <dt><code>c33</code></dt><dd>a numeric vector with 6 missing observations</dd>
 <dt><code>c34</code></dt><dd>a numeric vector with 3 missing observations</dd>
 <dt><code>c35</code></dt><dd>a numeric vector with 3 missing observations</dd>
 </dl>

miss: Sample Dataset

Description

Usage

Arguments

Format

Examples