Posterior output of the random effects in the model.

NIAID Data Ecosystem2026-03-08 收录

生态统计建模

个体效应估计

数据链接：

https://figshare.com/articles/dataset/_Posterior_output_of_the_random_effects_in_the_model_/993383 数据链接链接失效反馈

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Values are shown on a logit scale for the effect of each individual seal.

创建时间：

2014-04-09

Parameter estimates from a Generalized linear mixed-effects model to estimate the differences in bottom depth (m) between a randomly sampled set of bottom depths (see methods for sampling details) and bottom depths at locations of VMT-tagged seals that were or were not associated with other VMT-tag

海洋哺乳动物栖息地分析

生态统计建模

Individual seals were treated as a random effect and bottom depths encountered by each seal were modelled as an AR(1) process (autocorrelation parameter estimate, φ = 0.274). Among-seal SD in bottom d

NIAID Data Ecosystem50

Standard deviation of the random factor.

生态统计建模

随机效应分析

We show the standard deviation of the best model in each set (in all cases this was the model including either individual or breeding pair identities, see Tables 3 and 4), and that of the same model s

Figshare2015-12-02 更新30

ANOVA results for the effect of Fire regimesite (Locality as a random factor) and Sex on logSVL and logweight.

生态统计建模

野生动物形态影响分析

Significant p-values (p (XLSX)

Figshare2025-10-01 更新20

Summary of generalized linear models results.

生态统计建模

蝙蝠多样性评估

Bat diversity parameters (species richness, abundance, functional richness and functional abundance) were tested as dependent variables. In all cases, the fitted model was Y = μ + Forest cover + Seaso

Figshare2016-12-08 更新40

Tested carcass density distribution models with corrected Akaike’s information criterion (AICc) and delta (Δ) AICc, and fitted parameters.

生态统计建模

野生动物种群

Tested carcass density distribution models with corrected Akaike’s information criterion (AICc) and delta (Δ) AICc, and fitted parameters.

Figshare2018-12-12 更新20