<p>Number of observations: 178. Number of groups (random effect species): 5. AIC = 1444.34, BIC = 1475.69, logLik −712.17. The random intercept was normally distributed (mean 0, SD 5.62), and so was its residual term (mean 0, SD 9.38). Model residuals were normally distributed (Lilliefors D = 0.0403, <i>P</i> = 0.1732).</p>2<p>: variable included as quadratic term.</p
<p>Coefficients of linear mixed effects models of individuals per phenotype (dependent variable). Th...
<p>Models tested the prediction that the advantage of large size in aggressive contests for resource...
<p>Fixed effect size estimates of (i) four distance classes (200, 1000, 2500 and 4000 m), (ii) the i...
<p>Number of observations: 178. Number of groups (random effect species): 5. AIC = 1331.97, BIC = 13...
<p>Degrees of freedom: 88; number of observations: 136; number of groups (random effect ‘sampling lo...
<p>Estimates for each predictor variable and elevation result from separate models and assume a Pois...
<p>Study plot nested in site and sampling months of each year were included as random effects in all...
<p>The models assumed a normally distributed response with grass height log-transformed. Transect wa...
<p>A random intercept model with stand as the only random effect was selected as the best-fitting pa...
<p>The front-ends under consideration are MFCC (intercept), LDA1, LDA2, NMF_CC, H_CC + G_NMF and the...
*<p>Parameters in bold have significant coefficients (<i>p</i><0.05). For the forest biome study, el...
<p>All models had the same fixed effects structure and were fitted with REML estimation. The fit of ...
Due to the large data set, all factors are significant. However, we made an arbitrary cut-off at the...
<p>The intercept represents the restricted maximum likelihood (REML) estimate of the value for invas...
<p>Taxonomic group was included as a random factor to control for possible taxonomic dependence. Whe...
<p>Coefficients of linear mixed effects models of individuals per phenotype (dependent variable). Th...
<p>Models tested the prediction that the advantage of large size in aggressive contests for resource...
<p>Fixed effect size estimates of (i) four distance classes (200, 1000, 2500 and 4000 m), (ii) the i...
<p>Number of observations: 178. Number of groups (random effect species): 5. AIC = 1331.97, BIC = 13...
<p>Degrees of freedom: 88; number of observations: 136; number of groups (random effect ‘sampling lo...
<p>Estimates for each predictor variable and elevation result from separate models and assume a Pois...
<p>Study plot nested in site and sampling months of each year were included as random effects in all...
<p>The models assumed a normally distributed response with grass height log-transformed. Transect wa...
<p>A random intercept model with stand as the only random effect was selected as the best-fitting pa...
<p>The front-ends under consideration are MFCC (intercept), LDA1, LDA2, NMF_CC, H_CC + G_NMF and the...
*<p>Parameters in bold have significant coefficients (<i>p</i><0.05). For the forest biome study, el...
<p>All models had the same fixed effects structure and were fitted with REML estimation. The fit of ...
Due to the large data set, all factors are significant. However, we made an arbitrary cut-off at the...
<p>The intercept represents the restricted maximum likelihood (REML) estimate of the value for invas...
<p>Taxonomic group was included as a random factor to control for possible taxonomic dependence. Whe...
<p>Coefficients of linear mixed effects models of individuals per phenotype (dependent variable). Th...
<p>Models tested the prediction that the advantage of large size in aggressive contests for resource...
<p>Fixed effect size estimates of (i) four distance classes (200, 1000, 2500 and 4000 m), (ii) the i...