scholarly journals Testing multiple variance components in linear mixed-effects models

Biostatistics ◽  
2012 ◽  
Vol 14 (1) ◽  
pp. 144-159 ◽  
Author(s):  
R. Drikvandi ◽  
G. Verbeke ◽  
A. Khodadadi ◽  
V. Partovi Nia
Keyword(s):  
Variance Components ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects

scholarly journals Testing heterogeneity in inter-rater reliability

2020 ◽  
Author(s):  
František Bartoš ◽  
Patrícia Martinková ◽  
Marek Brabec
Keyword(s):  
Variance Components ◽  
Generalized Additive Models ◽  
Mixed Effects ◽  
Additive Models ◽  
Mixed Effects Models ◽  
Rater Reliability ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Wide Range

Estimating the inter-rater reliability (IRR) is important for assessing and improving the quality of ratings. In some cases, the IRR may differ between groups due to their features. To test heterogeneity in IRR, the second-order generalized estimating equations (GEE2) and linear mixed-effects models (LME) were already used. Another method capable of estimating the components for IRR is generalized additive models (GAM). This paper presents a simulation study evaluating the performance of these methods in estimating variance components and in testing heterogeneity in IRR. We consider a wide range of sample sizes and various scenarios leading to heterogenous IRR. The results show, that while the LME and GAM models perform similarly and yield reliable estimates, the GEE2 models may lead to incorrect results.

scholarly journals Stochastic Lanczos estimation of genomic variance components for linear mixed-effects models

2019 ◽  
Author(s):  
Richard Border ◽  
Stephen Becker
Keyword(s):  
Variance Components ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Stochastic Methods ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Matrix Operations ◽  
Novel Algorithms ◽  
Reml Estimation ◽  
Estimation Of Variance

AbstractBackgroundLinear mixed-effects models (LMM) are a leading method in conducting genome-wide association studies (GWAS) but require residual maximum likelihood (REML) estimation of variance components, which is computationally demanding. Previous work has reduced the computational burden of variance component estimation by replacing direct matrix operations with iterative and stochastic methods and by employing loose tolerances to limit the number of iterations in the REML optimization procedure. Here, we introduce two novel algorithms,stochastic Lanczos derivative-free REML(SLDF_REML) andLanczos first-order Monte Carlo REML(L_FOMC_REML), that exploit problem structure via the principle of Krylov subspace shift-invariance to speed computation beyond existing methods. Both novel algorithms only require a single round of computation involving iterative matrix operations, after which their respective objectives can be repeatedly evaluated using vector operations. Further, in contrast to existing stochastic methods,SLDF_REMLcan exploit precomputed genomic relatedness matrices (GRMs), when available, to further speed computation.ResultsResults of numerical experiments are congruent with theory and demonstrate that interpreted-language implementations of both algorithms match or exceed existing compiled-language software packages in speed, accuracy, and flexibility.ConclusionsBoth theSLDF_REMLandL_FOMC_REMLalgorithms outperform existing methods for REML estimation of variance components for LMM and are suitable for incorporation into existing GWAS LMM software implementations.Full list of author information is available at the end of the article

scholarly journals Generalized Confidence Intervals for Intra- and Inter-subject Coefficients of Variation in Linear Mixed-effects Models

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Johannes Forkman
Keyword(s):  
Confidence Intervals ◽  
Random Effects ◽  
Variance Components ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Coefficients Of Variation ◽  
Generalized Confidence Intervals ◽  
Generalized Pivotal Quantities

Abstract Linear mixed-effects models are linear models with several variance components. Models with a single random-effects factor have two variance components: the random-effects variance, i. e., the inter-subject variance, and the residual error variance, i. e., the intra-subject variance. In many applications, it is practice to report variance components as coefficients of variation. The intra- and inter-subject coefficients of variation are the square roots of the corresponding variances divided by the mean. This article proposes methods for computing confidence intervals for intra- and inter-subject coefficients of variation using generalized pivotal quantities. The methods are illustrated through two examples. In the first example, precision is assessed within and between runs in a bioanalytical method validation. In the second example, variation is estimated within and between main plots in an agricultural split-plot experiment. Coverage of generalized confidence intervals is investigated through simulation and shown to be close to the nominal value.

The Use of Theory of Linear Mixed-Effects Models to Detect Fraudulent Erasures at an Aggregate Level

2021 ◽  
pp. 001316442199489
Author(s):  
Luyao Peng ◽  
Sandip Sinharay
Keyword(s):  
Type I Error ◽  
Real Data ◽  
Mixed Effects ◽  
Error Rates ◽  
Mixed Effects Models ◽  
Type I ◽  
Aggregate Level ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Best Linear Unbiased

Wollack et al. (2015) suggested the erasure detection index (EDI) for detecting fraudulent erasures for individual examinees. Wollack and Eckerly (2017) and Sinharay (2018) extended the index of Wollack et al. (2015) to suggest three EDIs for detecting fraudulent erasures at the aggregate or group level. This article follows up on the research of Wollack and Eckerly (2017) and Sinharay (2018) and suggests a new aggregate-level EDI by incorporating the empirical best linear unbiased predictor from the literature of linear mixed-effects models (e.g., McCulloch et al., 2008). A simulation study shows that the new EDI has larger power than the indices of Wollack and Eckerly (2017) and Sinharay (2018). In addition, the new index has satisfactory Type I error rates. A real data example is also included.

scholarly journals Association of body condition with lameness in dairy cattle: a single-farm longitudinal study

2021 ◽  
pp. 1-4
Author(s):  
Michaela Kranepuhl ◽  
Detlef May ◽  
Edna Hillmann ◽  
Lorenz Gygax
Keyword(s):  
Body Condition ◽  
Body Condition Score ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Confounding Variables ◽  
Condition Score ◽  
The Impact ◽  
The Relationship

Abstract This research communication describes the relationship between the occurrence of lameness and body condition score (BCS) in a sample of 288 cows from a single farm that were repeatedly scored in the course of 9 months while controlling for confounding variables. The relationship between BCS and lameness was evaluated using generalised linear mixed-effects models. It was found that the proportion of lame cows was higher with decreasing but also with increasing BCS, increased with lactation number and decreased with time since the last claw trimming. This is likely to reflect the importance of sufficient body condition in the prevention of lameness but also raises the question of the impact of overcondition on lameness and the influence of claw trimming events on the assessment of lameness. A stronger focus on BCS might allow improved management of lameness that is still one of the major problems in housed cows.

Sign in / Sign up

Export Citation Format

Share Document