These methods are called supertree methods, the smaller trees are called source trees and the set of these source trees is called a profile of trees.
Results show that PMOnly methods display the largest fluctuations, while PMLoess methods give the smallest.
There were no cRC value differences between the methods for the smallest 1-cm-diameter insert, cRC being 0.18.
The difference between the methods is the smallest when the latent variable is perfectly normal distributed, and becomes gradually bigger with increasing skewness of the latent variable distribution.
In summary, the eigenvalue method and the orthogonal method identify the smallest set of parameters that needs to be fixed in order to create an identifiable model.
In this method the smaller number is partitioned (broken down into tens and units).
When proteins are compared using this method, the smaller the E-value, the greater the similarity between two compared proteins.
Regardless of the dataset or the imputation method, the smaller the distance between an individual and its closest relative, the higher the R l 2 – –.
By comparison, it can be seen that the cross entropy value of the proposed method is the smallest in the four methods.
Based on the lowest prediction parameters error criteria, the method having the smallest total ranks indicated the best geostatistical method.
