ParDef

Define the parameters for the lactation curve models to be fitted

internal

Lactation curves describe temporal changes in milk yield and are key to breeding and managing dairy animals more efficiently. The use of ensemble modeling, which consists of combining predictions from multiple models, has the potential to yields more accurate and robust estimates of lactation patterns than relying solely on single model estimates. The package EMOTIONS fits 47 models for lactation curves and creates ensemble models using model averaging based on Akaike information criterion (AIC), Bayesian information criterion (BIC), root mean square percentage error (RMSPE) and mean squared error (MAE), variance of the predictions, cosine similarity for each model's predictions, and Bayesian Model Average (BMA). The daily production values predicted through the ensemble models can be used to estimate resilience indicators in the package. The package allows the graphical visualization of the model ranks and the predicted lactation curves. Additionally, the packages allows the user to detect milk loss events and estimate residual-based resilience indicators.

Pablo Fonseca

EMOTIONS

Ensemble Models for Lactation Curves

ParDef function

<dl><dt>x</dt>
<dd>data frame containing the daily milking records for the individual</dd>
<dt>trait</dt>
<dd>The name of the column containing daily milking records</dd>
<dt>dim</dt>
<dd>The name of the column containing days in milk records</dd></dl>

Arguments

Define the parameters for the lactation curve models to be fitted — ParDef

<dl>

<dt>x</dt>
<dd>data frame containing the daily milking records for the individual</dd>


<dt>trait</dt>
<dd>The name of the column containing daily milking records</dd>


<dt>dim</dt>
<dd>The name of the column containing days in milk records</dd>

</dl>

ParDef: Define the parameters for the lactation curve models to be fitted

Description

Usage

Value

Arguments