This set of models was still relatively large (28 different models), since the predictor variable in question could included using a linear or non-linear function, and with species-specific or global parameters (see Eqns (4)–(6)).
This property of lasso makes it a natural approach for genome-wide association analysis, where the marker genotypes are treated as the predictors, the phenotype in question is treated as the response, and the (sparse) set of markers having non-zero regression coefficients are interpreted as the markers truely associated with the phenotype.
In summary, BRT gives two crucial pieces of information, namely the underlying relationship between the response and each predictor, and the strongest statistical predictor (among the simultaneously tested predictors) of the response in question.
Covariates included in the final model were known from the literature to be important predictors of the outcomes in question [e.g., ejaculation abstinence time in analyses of sperm concentration and smoking; body mass index (BMI) for reproductive hormone analyses].
Although several previous evaluations have used a theoretical framework, the further development of behaviour change interventions has rarely been underpinned by empirical data on predictors of the behaviour in question.
For the quadratic models, a single quadratic term was added for the regulator, q, in question: The predictor is linked to the estimated probability p i of eve being ON in the ith nucleus: Models for eve 2 were trained using the 348 nuclei defined as ON in stripe 2, as well as the nuclei defined as OFF excluding the nuclei of other stripes and their immediate neighbors (2588 nuclei).
For a given Ri, should the p-value be greater than 0.05 for a predictor, one can state that the predictor is not statistically significant for the key feature in question, thus the corresponding function f is set to zero.
Similar to the results obtained previously in this study, the combination of the predictor features differed depending on the dominant species in question.
A higher relative importance of a predictor indicates a stronger influence on the response (disease) in question.
To quantify interaction effects between predictors (the collinearity and synergistic effect upon predicting the response in question) we used the routine gbm.interactions [46].
