Sentence examples for models when testing from inspiring English sources

Second, we used the top explanatory variables identified in these regression models when testing the amova analyses comparing the influence of ecological factors in determining the genetic diversity of haemosporidia lineages across sites (see above).

These results offer a roadmap for further functional characterizations of the protein factors, and for building models when testing the genetic contributions of these genes in plant growth and development.

If both sex and Centroid_Size were found to be significant contributors to the variation in a relative warp, both were added as cofactors to models when testing for life history or QTL effects.

The GEE approach is also preferable rather than the random-effect regression models when testing the effects of cluster-level covariates (like the multifaceted intervention which is our only cluster-level exposure variable) [ 23], and when studying small number of clusters per arm.

But so far, the results of the "fundamentals" models when tested on real data have been consistent with a hypothesis of no forecasting skill but instead some random variance centered around a poorly performing mean.

Barnett says getting forcing data is "a must" because many climate models, when tested against history, produced results close to observed temperatures, despite making different assumptions about "forcing" (probably radiative forcing, the net difference between heat radiation entering the earth's atmosphere and leaving it).

However, because these models performed less well than the PLS regression kriging models when tested against the validation dataset, this approach was not taken any further in this study.

The cumulative arsenic exposure influenced the associations slightly more than did the average arsenic exposure, and adjusting for the urinary arsenic concentrations did not make any difference in the models when tested (data not shown).

Although this does not qualify as an independent evaluation of these potential novel biomarkers, these results at least suggest that it is viable to obtain adequate predictive concordance between prognostic models when tested on different data generated by independent expression measurement platforms.

Various coculture systems also add important information to the problem of a "relevant metabolic activation model" when testing genotoxic effects of PM in vitro.

We could include patient as a fixed effect in our linear model, when testing for differential expression between the cell populations.