Fitting Linear Mixed-Effects Models using lme4

Bates, Douglas M.; Mächler, Martin; Bolker, Ben; Walker, Steve

doi:10.48550/arxiv.1406.5823

Cited by 992 publications

(972 citation statements)

References 14 publications

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“…Data was analyzed using R v4.1.2 [ 36 ] with the packages phyloseq [ 38 ], vegan [ 39 ], DESeq2 [ 40 ], and lme4 [ 41 ]. We tested the influence of plant genotype on the diversity and structure of the bacterial microbiome at each compartment.…”

Section: Methodsmentioning

confidence: 99%

Plant Genotype Shapes the Bacterial Microbiome of Fruits, Leaves, and Soil in Olive Plants

Malacrinò

Mosca

Nicosia

et al. 2022

Plants

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The plant microbiome plays an important role in plant biology, ecology, and evolution. While recent technological developments enabled the characterization of plant-associated microbiota, we still know little about the impact of different biotic and abiotic factors on the diversity and structures of these microbial communities. Here, we characterized the structure of bacterial microbiomes of fruits, leaves, and soil collected from two olive genotypes (Sinopolese and Ottobratica), testing the hypothesis that plant genotype would impact each compartment with a different magnitude. Results show that plant genotype differently influenced the diversity, structure, composition, and co-occurence network at each compartment (fruits, leaves, soil), with a stronger effect on fruits compared to leaves and soil. Thus, plant genotype seems to be an important factor in shaping the structure of plant microbiomes in our system, and can be further explored to gain functional insights leading to improvements in plant productivity, nutrition, and defenses.

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Section: Methodsmentioning

confidence: 99%

Plant Genotype Shapes the Bacterial Microbiome of Fruits, Leaves, and Soil in Olive Plants

Malacrinò

Mosca

Nicosia

et al. 2022

Plants

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“…Under the GLMM framework, instead of using the proposed algorithm described in Section 2.2, we used the "glmer" function in the lme4 package in R (Bates et al, 2014).…”

Section: Comparison Methodsmentioning

confidence: 99%

Personalized Policy Learning using Longitudinal Mobile Health Data

Hu¹,

Qian

Cheng

et al. 2020

Preprint

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We address the personalized policy learning problem using longitudinal mobile health application usage data. Personalized policy represents a paradigm shift from developing a single policy that may prescribe personalized decisions by tailoring. Specifically, we aim to develop the best policy, one per user, based on estimating random effects under generalized linear mixed model. With many random effects, we consider new estimation method and penalized objective to circumvent high-dimension integrals for marginal likelihood approximation. We establish consistency and optimality of our method with endogenous app usage. We apply our method to develop personalized push ("prompt") schedules in 294 app users, with a goal to maximize the prompt response rate given past app usage and other contextual factors. We found the best push schedule given the same covariates varied among the users, thus calling for personalized policies. Using the estimated personalized policies would have achieved a mean prompt response rate of 23% in these users at 16 weeks or later: this is a remarkable improvement on the observed rate (11%), while the literature suggests 3%-15% user engagement at 3 months after download. The proposed method compares favorably to existing estimation methods including using the R function "glmer" in a simulation study.

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“…It also included both interactions: sprout and species, to examine if the amount of Pb differential among sprouts is not equal for all species; and zone and species, to determine if the differences in Pb levels between areas are consistent across species. Models were performed using the "lme4" package [50] and, in both cases we removed non-significant interactions sequentially (p-value > 0.05) following a backwards-stepwise selection procedure in both models to avoid risks of over-parameterization. Models' results were presented for all main effects and significant interactions.…”

Section: Statistical Analysesmentioning

confidence: 99%

The Impact of Pb from Ammunition on the Vegetation of a Bird Shooting Range

2022

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Hunting with lead ammunition represents a source of heavy metal pollution to the environment that can be potentially high at the local scale. Intensive hunting of small game species can concentrate high levels of ammunition discharging in small areas. This type of hunting is a relevant economic resource for private landowners in some regions of Spain, and current legislation allows the use of lead ammunition in these scenarios. It becomes, therefore, highly relevant to study whether this activity may pose concerns to the conservation of the environment in the areas where it takes place. Using a red-legged partridge (Alectoris rufa) shooting range as a study area, we examined the effect of intensive hunting on this species on the vegetation present. We found significantly higher lead levels in the sprouts of plants of shooting areas related to control sites of the same property where partridge shooting does not occur. We found differences in the presence of lead between sprouts of different plant species. In addition, old sprouts of existing vegetation in shooting areas also showed higher lead levels than newly emerged sprouts of the same plants. These results demonstrate the impact of lead ammunition on vegetation in terms of persistence over time and differences between species. Further analyses using chemical and ecotoxicological data are necessary to evaluate the extent of environmental pollution risks. Our results provide new support in favor of the use of alternative ammunition, with particular emphasis on scenarios where hunting activity is intensive.

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Fitting Linear Mixed-Effects Models using lme4

Cited by 992 publications

References 14 publications

Plant Genotype Shapes the Bacterial Microbiome of Fruits, Leaves, and Soil in Olive Plants

Plant Genotype Shapes the Bacterial Microbiome of Fruits, Leaves, and Soil in Olive Plants

Personalized Policy Learning using Longitudinal Mobile Health Data

The Impact of Pb from Ammunition on the Vegetation of a Bird Shooting Range

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