spring1

datasets

A method for detecting outliers with a Kalman filter on impulsed
noised outliers and prediction on cleaned data. 'kfino' is a robust
sequential algorithm allowing to filter data with a large number of outliers.
This algorithm is based on simple latent linear Gaussian processes as in the
Kalman Filter method and is devoted to detect impulse-noised outliers. These
are data points that differ significantly from other observations. 'ML'
(Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm)
algorithms were implemented in 'kfino'. The method is described in full
details in the following arXiv e-Print: <arXiv:2208.00961>.

Isabelle Sanchez

kfino

Kalman Filter for Impulse Noised Outliers

Bertrand Cloez

Benedicte Fontez

spring1 function

a data.frame<dl>
 <dt>Poids</dt>
<dd>weight (in kg)</dd> <dt>Date</dt>
<dd>Date of weighing yyyy-mm-dd</dd> <dt>IDE</dt>
<dd>id of the animal</dd> <dt>Day</dt>
<dd>Date of weighing with day and time yyyy-mm-dd hh:mm:ss</dd> <dt>dateNum</dt>
<dd>a rescaled date - fraction of the whole observational time 
 for one individual. 
 \(dateNum=(Heure - min(Heure))/86400 + (Date - min(Date))/86400\)</dd>
</dl>

Format

a dataset containing the WoW weighing for one animal of 203 observations.
https://doi.org/10.1016/j.compag.2018.08.022 — spring1

a data.frame<dl>
 <dt>Poids</dt>
<dd>weight (in kg)</dd>

 <dt>Date</dt>
<dd>Date of weighing yyyy-mm-dd</dd>

 <dt>IDE</dt>
<dd>id of the animal</dd>

 <dt>Day</dt>
<dd>Date of weighing with day and time yyyy-mm-dd hh:mm:ss</dd>

 <dt>dateNum</dt>
<dd>a rescaled date - fraction of the whole observational time 
 for one individual. 
 \(dateNum=(Heure - min(Heure))/86400 + (Date - min(Date))/86400\)</dd>


</dl>

spring1: a dataset containing the WoW weighing for one animal of 203 observations. https://doi.org/10.1016/j.compag.2018.08.022

Description

Usage

Arguments

Format