Results 1 to 10 of about 3,822,020 (321)
partR2: partitioning R2 in generalized linear mixed models [PDF]
PeerJ, 2021 The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by ...
Martin A. Stoffel +2 more
doaj +4 more sources
Fitting Linear Mixed-Effects Models Using lme4 [PDF]
Journal of Statistical Software, 2015 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
Douglas Bates +3 more
doaj +2 more sources
, 2022 AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework.
Osval Antonio Montesinos López +2 more
semanticscholar +4 more sources
BLMM: Parallelised computing for big linear mixed models [PDF]
NeuroImage, 2022 Within neuroimaging large-scale, shared datasets are becoming increasingly commonplace, challenging existing tools both in terms of overall scale and complexity of the study designs.
Thomas Maullin-Sapey, Thomas E. Nichols
doaj +2 more sources
lmerTest Package: Tests in Linear Mixed Effects Models
Journal of Statistical Software, 2017 One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer?
Alexandra Kuznetsova +2 more
doaj +2 more sources
Subset selection for linear mixed models. [PDF]
Biometrics, 2023 AbstractLinear mixed models (LMMs) are instrumental for regression analysis with structured dependence, such as grouped, clustered, or multilevel data. However, selection among the covariates—while accounting for this structured dependence—remains a challenge. We introduce a Bayesian decision analysis for subset selection with LMMs. Using a Mahalanobis
Kowal DR.
europepmc +4 more sources
Federated generalized linear mixed models for collaborative genome-wide association studies [PDF]
iScience, 2023 Summary: Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges.
Wentao Li +3 more
doaj +2 more sources
, 2014 Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
semanticscholar +4 more sources
Model Selection in Linear Mixed Models
Statistical Science, 2013 Linear mixed effects models are highly flexible in handling a broad range of data types and are therefore widely used in applications. A key part in the analysis of data is model selection, which often aims to choose a parsimonious model with other ...
Müller, Samuel +2 more
core +4 more sources
Sparse Probit Linear Mixed Model [PDF]
Machine Learning, 2017 Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various ...
Cunningham, John P. +5 more
core +2 more sources