ResInd

Estimate resilience indicators (log-variance, lag-1 autocorrelation, and skewness) from daily milk production records

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'

ResInd function

<dl><dt>production_df</dt>
<dd>A list containing data frames with the daily production records (actual or predicted) obtained from the LacCurveFit function</dd>
<dt>dim_filter_range</dt>
<dd>A numeric vector with the lower and upper limits used to filter out lactation records at the beginning and end of lactation. If no filtering is needed, set the first two values to the minimum days in milk and the last two to the maximum</dd>
<dt>outlier_sd_threshold</dt>
<dd>A numeric threshold defining the maximum number of standard deviations allowed for resilience indicator values before considering them outliers</dd>
<dt>weight</dt>
<dd>The name of the column containing the selected ensemble prediction. Default is "weight_AIC"</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>
<dt>ID_col</dt>
<dd>The name of the column containing the unique individual IDs</dd></dl>

Arguments

Estimate resilience indicators (log-variance, lag-1 autocorrelation, and skewness) from daily milk production records — ResInd

<dl>

<dt>production_df</dt>
<dd>A list containing data frames with the daily production records (actual or predicted) obtained from the LacCurveFit function</dd>


<dt>dim_filter_range</dt>
<dd>A numeric vector with the lower and upper limits used to filter out lactation records at the beginning and end of lactation. If no filtering is needed, set the first two values to the minimum days in milk and the last two to the maximum</dd>


<dt>outlier_sd_threshold</dt>
<dd>A numeric threshold defining the maximum number of standard deviations allowed for resilience indicator values before considering them outliers</dd>


<dt>weight</dt>
<dd>The name of the column containing the selected ensemble prediction. Default is "weight_AIC"</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>


<dt>ID_col</dt>
<dd>The name of the column containing the unique individual IDs</dd>

</dl>

ResInd: Estimate resilience indicators (log-variance, lag-1 autocorrelation, and skewness) from daily milk production records

Description

Usage

Value

Arguments