BMAweight_gamma

Estimate normalized model weights using an Expectation–Maximization (EM) algorithm with a gamma distribution

internal

Lactation curve modeling plays a central role in dairy production, supporting management decisions and the selection of animals with superior productivity and resilience. The package 'EMOTIONS' fits 47 models for lactation curves and creates ensemble models using model averaging based on Akaike information criterion, Bayesian information criterion, root mean square percentage error, and mean squared error, variance of the predictions, cosine similarity for each model's predictions, and Bayesian Model Average. The daily production values predicted through the ensemble models can be used to estimate resilience indicators in the package. Additionally, the package allows the graphical visualization of the model ranks and the predicted lactation curves.

Pablo Fonseca

EMOTIONS

'EMOTIONS: Ensemble Models for Lactation Curves'

BMAweight_gamma function

<dl><dt>converged_models</dt>
<dd>A list containing the fitted models for the individual</dd>
<dt>x</dt>
<dd>A 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></dl>

Arguments

Estimate normalized model weights using an Expectation–Maximization (EM) algorithm with a gamma distribution — BMAweight_gamma

<dl>

<dt>converged_models</dt>
<dd>A list containing the fitted models for the individual</dd>


<dt>x</dt>
<dd>A 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>

</dl>

BMAweight_gamma: Estimate normalized model weights using an Expectation–Maximization (EM) algorithm with a gamma distribution

Description

Usage

Value

Arguments