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We used a linear mixed model (West et al. 2014) with year as a random effect to test for differences among ground cover type, habitat type, and year, specifically testing the ground cover type × habitat type × year interaction.
We used a linear mixed model (West et al. 2014) with year as a random effect to test for differences among browse species, habitat types, and year, specifically testing the species × habitat type × year interaction.
We used a linear mixed model (West et al. 2014) with year as a random effect to test for differences in tree and shrub total density among habitat type and year, specifically testing the habitat type × year interaction.
We used a linear mixed model (West et al. 2014) with year as a random effect to test for differences among species, habitat type, and year, specifically testing the species × habitat type × year interaction.
Therefore, we included range*family as a random effect to test for phylogenetic correlations.
We used generalized linear mixed models (GLMMs) with dog identity included as a random effect to test for differences between conditions or sessions and for learning across trials (using R package lme4, Bates et al. 2012).
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The basic models are as described above, with age also entered as a random effect when testing age-related changes.
Participant was treated as a random effect and Time (pre-test, post-test) was treated as a fixed effect.
We used Kruskal Wallis equality of proportions test, Somers' D test, and separate random intercept logistic regressions with clinical site as the random effect to evaluate measures of association and statistical significance in the data.
We used Somers' D test and separate random intercept logistic regressions with clinical site as the random effect to evaluate bivariate measures of association.
In the ANOVA model subject was tested as a random effect.
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