Adequacy of approximations to distributions of test statistics in complex mixed linear models

Schaalje, G. Bruce; McBride, Justin B.; Fellingham, Gilbert W.

doi:10.1198/108571102726

Cited by 231 publications

(220 citation statements)

References 8 publications

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“…Covariance-structure selection was based on REML information criteria (24). The denominator degrees of freedom for the tests of the fixed effects were computed by using the Kenward-Roger method (25). Nonsignificant fixed effects (here run number and its interaction with d value) were dropped from the final model statement.…”

Section: Methodsmentioning

confidence: 99%

Multiroute memories in desert ants

Sommer

Beeren

2008

Proc. Natl. Acad. Sci. U.S.A.

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When offered a permanent food source, central Australian desert ants, Melophorus bagoti, develop individually distinct, view-based foraging routes, which they retrace with amazing accuracy during each foraging trip. Using a particular channel setup connected to an artificial feeder, we trained M. bagoti ants to either two or three inward routes that led through different parts of their maze-like foraging grounds. Here, we show that ants are able to adopt multiple habitual paths in succession and that they preserve initially acquired route memories even after they have been trained to new routes. Individual ants differ in the consistency with which they run along habitual pathways. However, those ants that follow constant paths retain their route-specific memories for at least 5 days of suspended foraging, which suggests that even multiple route memories, once acquired, are preserved over the entire lifetime of a forager.landmark guidance ͉ Melophorus ͉ memory retention ͉ route following ͉ visual navigation

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Section: Methodsmentioning

confidence: 99%

Multiroute memories in desert ants

Sommer

Beeren

2008

Proc. Natl. Acad. Sci. U.S.A.

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“…Because weights change through time, the model also included week as a fixed effect to account for the change in weight across weeks within a growth period. Denominator degrees of freedom were determined using the Kenward-Roger approximation, which is similar to the Sattherthwaite method used in the univariate models, but is preferred for repeated measures designs (Kenward and Roger 1997;Schaalje et al 2001).…”

Section: Qtl Analysismentioning

confidence: 99%

Disentangling Prenatal and Postnatal Maternal Genetic Effects Reveals Persistent Prenatal Effects on Offspring Growth in Mice

et al. 2011

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Mothers are often the most important determinant of traits expressed by their offspring. These "maternal effects" (MEs) are especially crucial in early development, but can also persist into adulthood. They have been shown to play a role in a diversity of evolutionary and ecological processes, especially when genetically based. Although the importance of MEs is becoming widely appreciated, we know little about their underlying genetic basis. We address the dearth of genetic data by providing a simple approach, using combined genotype information from parents and offspring, to identify "maternal genetic effects" (MGEs) contributing to natural variation in complex traits. Combined with experimental cross-fostering, our approach also allows for the separation of pre-and postnatal MGEs, providing rare insights into prenatal effects. Applying this approach to an experimental mouse population, we identified 13 ME loci affecting body weight, most of which (12/13) exhibited prenatal effects, and nearly half (6/13) exhibiting postnatal effects. MGEs contributed more to variation in body weight than the direct effects of the offsprings' own genotypes until mice reached adulthood, but continued to represent a major component of variation through adulthood. Prenatal effects always contributed more variation than postnatal effects, especially for those effects that persisted into adulthood. These results suggest that MGEs may be an important component of genetic architecture that is generally overlooked in studies focused on direct mapping from genotype to phenotype. Our approach can be used in both experimental and natural populations, providing a widely practicable means of expanding our understanding of MGEs.

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“…In this case the statistical tests about the fixed effects of mixed linear models are generally not exact and their degrees of freedom must be determined by approximation. For some types of mixed models, the available methods for approximating degrees of freedom have been well examined (Schaalje et al 2002;Spuke et al 2004Spuke et al , 2005. For mixed models with spatial covariance structures, however, the use of the approximation methods has to be cleared.…”

Section: Smentioning

confidence: 99%

“…The Kenward-Roger method is put forward for both approximating degrees of freedom and correcting eventually underestimated standard errors, but it is not advisable to use it for complex covariance structures (Schaalje et al 2002;Spilke et al 2004). The present examination showed no problems for the invested spatial covariance structures.…”

Section: Comments and Conclusionmentioning

confidence: 99%

Comparison of various spatial models for the analysis of cultivar trials

Hu¹,

Spilke

2009

New Zealand Journal of Agricultural Research

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Because spatial variability often exists in field trials, spatial analysis is suggested for their evaluation. This paper investigated the consequences of mixed linear models with various spatial covariance structures on line statistical tests and selections of plant cultivar trials by using the Satterthwaite and Kenward-Roger methods for approximating degrees of freedom. The investigated covariance structures were experiential, spherical, Gaussian, linear, linearlog, anisotropic power and anisotropic exponential, as well as first order autoregressive. The data used in this study were grain yields from three separate cultivar trials. The results showed that the spatial models provided a smaller standard error than the classical analysis of variance models, but only the optimally fitting spatial model could control spatial variability more effectively with less loss of error degrees of freedom and hence generally result in a higher power for line tests than the classical analysis of variance of block designs. Furthermore, the spatial models resulted in a different ranking of line selections both in comparison with the analysis of variance and under the different spatial models. The first order autoregressive model often used in the literature was generally not an optimal option for spatial analysis. The Kenward-Roger method effectively provided a correction of the standard error for line contrasts and hence a reasonable power of spatial model analysis. These confirm the necessity of selecting the covariance structures and using the Kenward-Roger method for approximating degrees of freedom in the spatial analysis of cultivar field trials using mixed linear models.

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Adequacy of approximations to distributions of test statistics in complex mixed linear models

Cited by 231 publications

References 8 publications

Multiroute memories in desert ants

Multiroute memories in desert ants

Disentangling Prenatal and Postnatal Maternal Genetic Effects Reveals Persistent Prenatal Effects on Offspring Growth in Mice

Comparison of various spatial models for the analysis of cultivar trials

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