## 4 Discussion  
The present study aimed to compare, validate and optimize the linear appraisal methodological approaches proposed by [PERSON] et al. (2010) for does and bucks in the Murciano-Granadina goat breed to determine the quality of the LAS, which is routinely applied in opposition to traditional zoometric analysis, for dairy-related morphological traits. The combination of principal components and regression analyses has been reported to yield good estimates for the coefficients of explanatory  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sey/Lactation Phase** & \multicolumn{2}{c}{**Primipara Does**} & \multicolumn{2}{c}{**Multipara Does**} & \multicolumn{2}{c}{**Bucks**} \\ \hline
**Parameters** & **LAS Scores** & **Measurements** & **LAS Scores** & **Measurements** & **LAS Scores** & **Measurements** \\ \hline RSS & 5012.833 & 13,665.474 & 5804.558 & 10,318.972 & 389.064 & 523.915 \\ MSE & 0.221 & 0.602 & 0.340 & 0.603 & 0.267 & 0.354 \\ MMSE & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.0  
variables aiming to measure the same DV. [PERSON] and [PERSON] [25] suggested that regression analysis and principal component analysis (PCA) can be used to determine reduced numbers of explanatory variables to explain the variability described by a certain DV. With regard to the optimization of CAPRIGRAN LAS, principal components analysis reported quite conservative results, as only the rump angle was omitted from the variables considered for primipara and multipara does and bucks. Additionally, the optimization of the models reported a substantial internal consistency [26]; i.e., the optimized linear appraisal scales used showed good reliability (with values over 0.9). Thus, there was a significant validated ability to explain internal variability, as suggested by the Cronbach's alpha values. The Cronbach's alpha values were slightly higher for primipara and multipara does than for bucks, with the set of variables evaluated for does reporting a Cronbach's alpha value of 0.956 and 0.949 as opposed to the value of 0.936 reported for bucks, which suggested a slightly greater internal consistency in the case of females, which could be attributed to the dairy-related nature of the zoometric traits used in this study. However, the variability that each of the models was able to explain was moderately higher in males than in females, with values of 60.932% for bucks as opposed to values of 57.558 and 54.445% for primipara and multipara does. This could be ascribed to the slightly greater variability found in bucks, which was suggested by the descriptive statistics shown in Tables S3-S5, respectively. The computation of Cronbach's alpha was used as it is based on the comparison of the reliability of a test relative to other tests with the same number of items and measuring the same construct of interest [27]; thus, its application in the validation of linear appraisal system scales allowed us to report solid and objective results regarding the reliability of the translation from zoometric measurements to LAS scores. PCA identifies the variables that explain the highest fraction of variability in a dataset, and then it uses this information to create a dataset with a reduced number of variables with minimal loss of descriptive power. Bearing this in mind, the greater percentage of variability explained in the case of bucks may be ascribed to the greater variability found in the population of males, as suggested by the descriptive statistics in Tables S3-S5. One of the advantages that datasets with a reduced number of variables present compared to more complex datasets is that these should have less noise in the data, thus requiring less processing power, which in turn results in optimized variable sets that can be considered to explain or estimate a joint outcome; for example, in the present paper, the dairy-related morphological value of a certain animal. Still, the features of the most highly variable predictors may not necessarily be the best predictors of the variability in the whole dataset.