Ben Bolker's research while affiliated with McMaster University and other places

Publications (3)

Article
Full-text available
Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The for...

Citations

... The model results at the different test times were analysed using generalised linear mixed-effects regression models ("lme4" package in R, Bates et al., 2014). Models were constructed for null and overt pronouns separately, each time with interpretation (subject or non-subject) as the dependent variable, test time as a fixed factor, and intercepts for simulated participants as a random factor. ...
... For the distal threat response, we predicted that (in)direct resolution and palliation would raise approach-related affect. To test our hypothesis, we conducted a linear mixed model analysis using the R package lme4 (Bates, Mächler, Bolker, & Walker, 2015). We determined P-values using the package lmerTest (Kuznetsova, Brockhoff, & Christensen, 2017) with study modeled as a random effect. ...
... Eight of the haplotypes from Clarke et al. (2015) were shared between the Indo-Pacific clade, the Atlantic clade, and the market-derived samples (Figure 4(a)). Therefore, we used the haplotype frequencies from Clarke et al. (2015) and the frequencies of those haplotypes in the shark fin markets (Figure 4(a)) to conduct a mixed-stock analysis (MSA) with the R-package mixstock (Bolker, 2012). Mixstock was used to estimate the contribution of each source population (i.e., Indo-Pacific and Atlantic) to both shark fin markets using a Markov Chain Monte Carlo (MCMC) estimation with 100,000 iterations following a burn-in of 50,000. ...