For the purpose of multivariate analyses (linear Cox regression), phase angle was treated as a continuous variable.
There was no significant improvement when the most deviant waggle run was replaced with the next waggle run from the same side (Regression analysis: Angle: R = 0.06%, p = 0.31; Duration: R = 1.5%, p = 0.10; Fig. 4).
Least Angle Regression is a penalized regression method where predictor variables are included sequentially in the model [ 13].
Two other methods that might be suitable for estimating MEBV are Least Angle Regression (LARS) and Partial Least Square Regression (PLSR).
Three distinct methods were used to calculate MEBV: a Bayesian Method (BM), Least Angle Regression (LARS) and Partial Least Square Regression (PLSR).
Both least absolute shrinkage and selection operator and least angle regression are shrinkage methods for linear regression that minimize the sum of squared errors though with a bound on the sum of the absolute values of the coefficients given by a parameter K.
Least angle regression selects predictors and simultaneously shrinks the regression coefficients by penalization of the likelihood [17].
Followed by SNP-containing motif candidate search, we employed least angle regression via elastic net (LARS-EN) [24] to perform regression and derive a sparse solution simultaneously.
We compare our LP method to the least angle regression method [ 29] for building Lasso models for logistic regression.
Furthermore, efficient variable selection methods, such as least absolute shrinkage and selection operator, least angle regression and forward selection, have been effectively incorporated into the improved model for the determination of regression coefficients.
