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FEBS Open Bio, EarlyView.KDAC6 has been associated with cell motility and actin structures, but specific domain contributions are not established. The two catalytic domains have differential effects on motility and F‐actin regulation, affecting cortical F‐actin density, motility, stress fibers, and cell spreading.
Taylor V. Joseph +7 more
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2010
Mixed effects models, or simply mixed models, are widely used in practice. These models are characterized by the involvement of the so-called random effects. To understand the basic elements of a mixed model, let us first recall a linear regression model, which can be expressed as y = Xβ + e, where y is a vector of observations, X is a matrix of known ...
Donghui Zhang, Chunpeng Fan, A Gould
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Mixed effects models, or simply mixed models, are widely used in practice. These models are characterized by the involvement of the so-called random effects. To understand the basic elements of a mixed model, let us first recall a linear regression model, which can be expressed as y = Xβ + e, where y is a vector of observations, X is a matrix of known ...
Donghui Zhang, Chunpeng Fan, A Gould
+4 more sources
Mixed-effects models in psychophysiology
Psychophysiology, 2000The current methodological policy in Psychophysiology stipulates that repeated-measures designs be analyzed using either multivariate analysis of variance (ANOVA) or repeated-measures ANOVA with the Greenhouse-Geisser or Huynh-Feldt correction. Both techniques lead to appropriate type I error probabilities under general assumptions about the variance ...
E, Bagiella, R P, Sloan, D F, Heitjan
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2021
Abstract Mixed effects models are powerful techniques for controlling for non-independence of data or repeated measures, and can be harnessed for both normal and non-normal data structures. Chapter 8 teaches readers how to code, assess, interpret, and troubleshoot both linear and generalized linear mixed models using the same RxP dataset
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Abstract Mixed effects models are powerful techniques for controlling for non-independence of data or repeated measures, and can be harnessed for both normal and non-normal data structures. Chapter 8 teaches readers how to code, assess, interpret, and troubleshoot both linear and generalized linear mixed models using the same RxP dataset
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2007
Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
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Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
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Functional Mixed Effects Models
Biometrics, 2002Summary.In this article, a new class of functional models in which smoothing splines are used to model fixed effects as well as random effects is introduced. The linear mixed effects models are extended to non‐parametric mixed effects models by introducing functional random effects, which are modeled as realizations of zero‐mean stochastic processes ...
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2018
We have covered a variety of statistical models in this book, but all have shared a common feature: The criterion and error term were treated as random variables, but all of the predictors were assumed to be fixed. In this chapter, we will consider models that include a broader mixture of fixed and random variables.
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We have covered a variety of statistical models in this book, but all have shared a common feature: The criterion and error term were treated as random variables, but all of the predictors were assumed to be fixed. In this chapter, we will consider models that include a broader mixture of fixed and random variables.
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1989
In most design set-ups such as block designs or row-column designs classification effects such as block effects or row (column) effects are regarded fixed. When these are also considered random variables, we have one or more additional sources of information for estimating treatment effect parameters. Such models are known as mixed effects models.
Kirti R. Shah, Bikas K. Sinha
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In most design set-ups such as block designs or row-column designs classification effects such as block effects or row (column) effects are regarded fixed. When these are also considered random variables, we have one or more additional sources of information for estimating treatment effect parameters. Such models are known as mixed effects models.
Kirti R. Shah, Bikas K. Sinha
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2012
With one exception the models that we have treated before this chapter contain a single source of variability. In the linear models with Gaussian error, the variance of the population was estimated from the residuals. In the GLM models used to describe the judgments of observers, the variability of observer’s choices is given by that of the binomial ...
Kenneth Knoblauch, Laurence T. Maloney
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With one exception the models that we have treated before this chapter contain a single source of variability. In the linear models with Gaussian error, the variance of the population was estimated from the residuals. In the GLM models used to describe the judgments of observers, the variability of observer’s choices is given by that of the binomial ...
Kenneth Knoblauch, Laurence T. Maloney
openaire +1 more source