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lme4 specifications

Information


Unique identifier OMICS_34213
Name lme4
Alternative name Linear Mixed-Effects
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0, GNU General Public License version 2.0
Computer skills Advanced
Version 1.1-20
Stability Stable
Requirements
Matrix, methods, stats
Source code URL https://cran.r-project.org/src/contrib/lme4_1.1-20.tar.gz
Maintained Yes

Versioning


No version available

Documentation


Maintainers


  • person_outline Douglas Bates
  • person_outline Steven Walker

Publication for Linear Mixed-Effects

lme4 citations

 (2231)
library_books

Circadian and Brain State Modulation of Network Hyperexcitability in Alzheimer’s Disease

2018
eNeuro
PMCID: 5956746
PMID: 29780880
DOI: 10.1523/ENEURO.0426-17.2018

[…] g circular statistics by means of the “circular” package (). Circular outliers were identified using “CircOutlier” package ().For tests entailing random variables, linear mixed models were fit using “lme4” (). Significance was tested using a log-likelihood test comparing the full model to a null model without the factor of interest.For evaluation of the relationship between spike count and θ/δ, we […]

call_split

Common drivers of seasonal movements on the migration – residency behavior continuum in a large herbivore

2018
Sci Rep
PMCID: 5956000
PMID: 29769562
DOI: 10.1038/s41598-018-25777-y
call_split See protocol

[…] We tested the influence of population, season and migratory status (migrant vs resident) on (1) log-transformed seasonal range surface area (estimated using 95% kernel) using linear mixed effects models (LME) with individual identity as a random effect on the intercept to control for repeated observations per individual, in particular for individuals monitored more than on […]

library_books

Slow touch targeting CT fibres does not increase prosocial behaviour in economic laboratory tasks

2018
Sci Rep
PMCID: 5955966
PMID: 29769551
DOI: 10.1038/s41598-018-25601-7

[…] controlling for within-subject dependences of observations. We modelled participants as random intercept and the highest order within-subject interaction as random slope (as recommended by) with the lme4 package in R. For the reported interactions we calculated Type 3 Sum of Squares and used orthogonal contrasts. Reported main effects are based on Type 2 Sum of Squares. P-values are based on Wald […]

library_books

American Gut: an Open Platform for Citizen Science Microbiome Research

2018
mSystems
PMCID: 5954204
DOI: 10.1128/mSystems.00031-18

[…] ich mappings were significantly different between frozen samples and samples left out at ambient temperatures for various periods of time. Using the ‘lme’ package () in R (v3.3.3, R Core Team, 2017), linear mixed-effects models were applied to the abundances, with individual treated as the random effect. Mappings were considered to be significantly associated with temperature if the model was sign […]

library_books

Plasma proteins associated with circulating carotenoids in Nepalese school aged children☆

2018
PMCID: 5937903
PMID: 29605494
DOI: 10.1016/j.abb.2018.03.025

[…] Detailed information on estimation of protein relative abundance from reporter ion intensities within each iTRAQ experiment was published elsewhere []. We applied linear mixed effects models (LME) to determine the association between log2 transformed plasma concentration of each carotenoid and the relative abundance of individual plasma proteins accounting for […]

call_split

Vascular reactivity in small cerebral perforating arteries with 7 T phase contrast MRI – A proof of concept study

2018
Neuroimage
PMCID: 5915583
PMID: 29408324
DOI: 10.1016/j.neuroimage.2018.01.055
call_split See protocol

[…] Velocity reactivity (Rv) in the CSO and BG was calculated with a linear mixed effects (LME) model. We chose this since the velocity data had multiple levels (group and subject), and an ordinary least squares approach would have overestimated the confidence, since w […]

Citations

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lme4 institution(s)
Department of Statistics, University of Wisconsin, Madison, WI, USA; Seminar für Statistik, ETH Zurich, Zurich, Switzerland; Departments of Mathematics & Statistics and Biology, McMaster University, Hamilton, ON, Canada; Department of Mathematics & Statistics, McMaster University, Hamilton, ON, Canada
lme4 funding source(s)
Supported by an NSERC Discovery grant and NSERC postdoctoral fellowship.

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