Sentence examples for using the parsimony test from inspiring English sources

We also used Mothur to determine if there was a significant difference in the clustering among samples by using the Parsimony test, the weighted UniFrac method, and the unweighted UniFrac method.

A difference between feeding types was noted using the parsimony test and the Jaccard index.

We conducted clustering analysis with sequence signatures on these samples and used the parsimony test to study how well the grouping information was revealed (Table  2).

Ecological character transformation was traced in Mesquite [ 60] using the parsimony criterion, with test of correlation between fungus farming, gregariousness and inbreeding using Pagel's [ 26] modified test of independence between traits.

For comparison, we also tested whether the different gene partitions were in significant conflict with each other by using the parsimony based incongruence-length differences test (ILD) [ 64], implemented in PAUP* [ 50].

In order to evaluate the stability of the monogenean position in the Neodermata clade, the unconstrained 'best tree' was statistically tested with each of the alternative phylogenetic hypotheses (constrained trees) using the parsimony-based Templeton test [ 39] and likelihood-based Shimodaira-Hasegawa test (SH test; [ 40]) implemented in PAUP* 4.0b10 and TREE-PUZZLE 5.2, respectively.

Large protein datasets are similarly addressed using the parsimony and distance methods.

Alternative hypotheses were compared to the molecular topologies using the parsimony-based nonparametric Templeton test and the likelihood-based Shimodaira-Hasegawa (SH) test [ 74] as implemented in PAUP*.

Considering each group individually, penicillin had no impact on population structure and community membership evaluated by the Parsimony test, but a significant difference was identified using AMOVA.

The parsimony test [ 25] was finally used to compare the derived clusters with the actual groups given in the simulation model.

Next, the Parsimony test, weighted UniFrac method and unweighted UniFrac method were used to determine whether the clustering within the tree was statistically significant.