Note that "transcriptional drift" is different from "transcriptional noise" in that the former analyzes variance among genes within the same biological replicates, whereas the latter analyzes variance of the same genes among biological replicates.
17 The former three variance estimators were derived under the independent assumption of the standard regression coefficients βk.
There are other variances.
The response of lizard abundance to August precipitation of the preceding year was however better adjusted to a hyperbolic rather than to a linear function, since the former explained more variance (71.0%; F1,8 = 44.21, P<0.001, Fig. 6) than a linear function (52.7%).
If an accurate model is used the latter terms should be much smaller than the former, and thus the variance of the model-assisted estimator should be much smaller than the variance of the ordinary Horvitz-Thompson estimator, although this is not immediately clear when comparing Eq. 2 and Eq. 4.
Another reason for the lower h2 from repeated measures animal models compared to parent-offspring regressions is the former's lower additive variance (VA).
Results of the recent zooplankton revisions are at variance with former assessments based on insufficient biodiversity knowledge [58] [61] and, consequently, with the outdated affirmation that "the number of species in the Baltic is low" [62].
The former measure primarily reflects variance in fertility across lines, while the latter measure will be affected by the co-expression of the two parental haploid genomes.
We therefore elected for the more parsimonious GLM rather than REML approach, since the results were almost identical and the former allows estimation of variance explained.
We computed robustness to both factors using the 2 types of variances, V ip and V g, where the former was the noise-induced variance in the distribution of the fitness (i.e., the number of "on" target genes), in a population of isogenic individuals and V g is the variance in the distribution of fitness in a heterogenic population.
For linear mixed-effects models with a hierarchical (i.e. nested) structure, Nakagawa and Schielzeth (2013) derived two R 2 variates: marginal R 2 and conditional R 2. The former describes the proportion of variance explained by fixed factors alone, while the latter describes the proportion of variance explained by both fixed and random factors.
