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Data from: Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses
负责人:
关键词:
Logistic Regression on Distance Matrices;Commonality analysis;Multiple Regression on Distance Matrices
DOI:
doi:10.5061/dryad.86gm0
摘要:
fe species and are the object of many methodological developments. However, multicollinearity among explanatory variables are a systemic issue
Data from: A note on measuring natural selection on principal component scores
负责人:
关键词:
quantitative genetics;Life History Evolution;natural selection
DOI:
doi:10.5061/dryad.d28080r
摘要:
sensitive to the effects of multicollinearity due to highly correlated traits. While measuring selection on principal component scores is an apparent soluti
Data from: Regression commonality analyses on hierarchical genetic distances.
负责人:
关键词:
Landscape Genetics;variance partitioning;Cervus elaphus;landscape connectivity
DOI:
doi:10.5061/dryad.t5m22
摘要:
tors on spatial patterns of genetic differentiation. However, multicollinearity may obscure the interpretation of multivariate regressions. We illustrate how regression
Data from: Evidence of opposing fitness effects of parental heterozygosity and relatedness in a critically endangered marine turtle?
负责人:
关键词:
heterozygosity-fitness correlations;microsatellites;Eretmochelys imbricata;hawksbill turtle;Outbreeding depression;inbreeding depression;negative heterozygosity-fitness correlations
DOI:
doi:10.5061/dryad.6697t
摘要:
h positive and negative effects of genetic variability. Multicollinearity in these tests was within safe limits, and null simulations suggested the effect was not an ar
Data from: How well can body size represent effects of the environment on demographic rates? Disentangling correlated explanatory variables
负责人:
关键词:
multicollinearity adaptation demographic rates environmental effects multiple regression trait-based demography area-proportional Venn diagram
DOI:
doi:10.5061/dryad.pq161
摘要:
of multiple regression that is useful for understanding nonlinear relationships between responses and multicollinear explanatory variables. We graphically present the res

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