scholarly journals Biological and statistical interpretation of size-at-age, mixed-effects models of growth

2020 ◽  
Vol 7 (4) ◽  
pp. 192146 ◽  
Author(s):  
Simone Vincenzi ◽  
Dusan Jesensek ◽  
Alain J. Crivelli
Keyword(s):  
Mixed Effects ◽  
Mixed Effects Models ◽  
Individual Growth ◽  
Model Parameters ◽  
Statistical Interpretation ◽  
Von Bertalanffy ◽  
Mixed Effect ◽  
Single Observation ◽  
Growth Functions ◽  
Size At Age

The differences in life-history traits and processes between organisms living in the same or different populations contribute to their ecological and evolutionary dynamics. We developed mixed-effect model formulations of the popular size-at-age von Bertalanffy and Gompertz growth functions to estimate individual and group variation in body growth, using as a model system four freshwater fish populations, where tagged individuals were sampled for more than 10 years. We used the software Template Model Builder to estimate the parameters of the mixed-effect growth models. Tests on data that were not used to estimate model parameters showed good predictions of individual growth trajectories using the mixed-effects models and starting from one single observation of body size early in life; the best models had R 2 > 0.80 over more than 500 predictions. Estimates of asymptotic size from the Gompertz and von Bertalanffy models were not significantly correlated, but their predictions of size-at-age of individuals were strongly correlated ( r > 0.99), which suggests that choosing between the best models of the two growth functions would have negligible effects on the predictions of size-at-age of individuals. Model results pointed to size ranks that are largely maintained throughout the lifetime of individuals in all populations.

scholarly journals Biological and statistical interpretation of size-at-age, mixed-effects models of growth

2019 ◽  
Author(s):  
Simone Vincenzi ◽  
Dusan Jesensek ◽  
Alain J Crivelli
Keyword(s):  
Life History ◽  
Life History Traits ◽  
Individual Growth ◽  
Model Parameters ◽  
Statistical Interpretation ◽  
Von Bertalanffy ◽  
Mixed Effect ◽  
Single Observation ◽  
Group Variation ◽  
Size At Age

AbstractThe differences in life-history traits and processes between organisms living in the same or different populations contribute to determine their ecological and evolutionary dynamics. Recent advances in statistical and computational methods make it easier to investigate individual and group variation in life-history traits.We developed mixed-effect model formulations of the popular size-at-age von Bertalanffy and Gompertz growth functions to estimate individual and group variation in body growth, using as a model system four freshwater fish populations living in Slovenian streams, where tagged individuals were sampled for more than 10 years. We used the software Template Model Builder to estimate the parameters of the mixed-effect growth models.Estimates of asymptotic size from the Gompertz and von Bertalanffy models were not significantly correlated, but their predictions of size-at-age of individuals were strongly correlated (r > 0.99). Tests on data that were not used to estimate model parameters showed that predictions of individual growth trajectories using the random-effects model were accurately predicted (R2 > 0.80 for the best models over more than 500 predictions) starting from one single observation of body size early in life. Model results pointed to size ranks that are largely maintained throughout the lifetime of individuals in all populations.

scholarly journals Spatial variability in floodplain sedimentation: the use of generalized linear mixed-effects models

2010 ◽  
Vol 7 (1) ◽  
pp. 1589-1619 ◽  
Author(s):  
A. Cabezas ◽  
M. Angulo-Martínez ◽  
M. Gonzalez-Sanchís ◽  
J. J. Jimenez ◽  
F. A. Comín
Keyword(s):  
Spatial Scales ◽  
Size Composition ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Small Scale ◽  
Mixed Effect ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Generalized Linear Mixed Effects ◽  
Artificial Grass

Abstract. Sediment, Total Organic Carbon (TOC) and total nitrogen (TN) accumulation during one overbank flood (1.15 y) were examined at one reach of the Middle Ebro River (NE Spain) for elucidating spatial patterns. To achieve this goal, four areas with different geomorphological features and located within the study reach were examined by using artificial grass mats. Within each area, 1 m2 study plots consisting on three pseudo-replicates were placed in a semi-regular grid oriented perpendicular to the main channel. TOC, TN and Particle-Size composition of deposited sediments were examined and accumulation rates estimated. Generalized linear mixed-effects models were used to analyze sedimentation patterns in order to handle clustered sampling units, specific-site effects and spatial self-correlation between observations. Our results confirm the importance of channel-floodplain morphology and site micro-topography in explaining sediment, TOC and TN deposition patterns, although the importance of another factors as vegetation morphology should be included in further studies to explain small scale variability. Generalized linear mixed-effect models provide a good framework to deal with the high spatial heterogeneity of this phenomenon at different spatial scales, and should be further investigated in order to explore its validity when examining the importance of factors such as flood magnitude or suspended sediment solid concentration.

Integrated mixed-effect growth models for species with incomplete ageing histories: a case study for the loggerhead sea turtle Caretta caretta

2020 ◽  
Vol 636 ◽  
pp. 221-234 ◽  
Author(s):  
BE Chasco ◽  
JT Thorson ◽  
SS Heppell ◽  
L Avens ◽  
J Braun McNeill ◽  
...  
Keyword(s):  
Sexual Maturity ◽  
Growth Process ◽  
Growth Parameters ◽  
Caretta Caretta ◽  
Model Parameters ◽  
Von Bertalanffy ◽  
Growth Processes ◽  
Mixed Effect ◽  
Western Atlantic ◽  
Capture Recapture

For stochastic growth processes, integrated mixed-effects (IME) models of capture-recapture data and size-at-age data from calcified structures such as otoliths can reduce bias in model parameters. Researchers have not fully explored the performance of IME models for simultaneously estimating the unknown ages, growth model parameters, and derived variables. We simulated capture-recapture observations for tagging experiments and skeletochronology (i.e. humerus growth) observations for stranded loggerhead sea turtles Caretta caretta based on previously published parameter estimates for 3 growth processes (logistic, Gompertz, and von Bertalanffy). We then fit IME models to the integrated and non-integrated data. For the integrated data (both tagging and skeletochronology), we found decreased bias and uncertainty in estimated growth parameters and ages, and decreased misspecification of the growth process based on AIC. Applying the IME model to Western Atlantic loggerheads, the von Bertalanffy growth process provided the best fit to the skeletochronology data for the humeri from 389 stranded turtles and capture-recapture data from 480 tagged turtles. The estimated mean growth coefficient (μk) and mean asymptotic straight carapace length (μ×) were equal to 0.076 yr-1 and 92.1 cm, respectively. The estimated mean ages of the stranded turtles and recaptured tagged turtles were 13.5 and 14.6 yr, respectively. Assuming the size-at-sexual maturity (SSM) is 95% of the asymptotic size, the mean and 95% predictive interval for the age-at-sexual maturity (ASM) was 38 (29, 49) yr. Our results demonstrate that IME models provide reduced bias of the growth parameters, unknown ages, and derived variables such as ASM.

A note on estimating single-class piecewise mixed-effects models with unknown change points

2018 ◽  
Vol 42 (5) ◽  
pp. 518-524 ◽  
Author(s):  
Nidhi Kohli ◽  
Yadira Peralta ◽  
Cengiz Zopluoglu ◽  
Mark L. Davison
Keyword(s):  
Bayesian Estimation ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Mixed Effects Models ◽  
Change Points ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Residual Variance ◽  
Monte Carlo Simulation Study ◽  
Ml Estimation

Piecewise mixed-effects models are useful for analyzing longitudinal educational and psychological data sets to model segmented change over time. These models offer an attractive alternative to commonly used quadratic and higher-order polynomial models because the coefficients obtained from fitting the model have meaningful substantive interpretation. The current study thus focuses on the estimation of piecewise mixed-effects model with unknown random change points using maximum likelihood (ML) as described in Du Toit and Cudeck (2009). Previous simulation work (Wang & McArdle, 2008) showed that Bayesian estimation produced reliable parameter estimates for the piecewise model in comparison to frequentist procedures (i.e., first-order Taylor expansion and the adaptive Gaussian quadrature) across all simulation conditions. In the current article a small Monte Carlo simulation study was conducted to assess the performance of the ML approach, a frequentist procedure, and the Bayesian approach for fitting linear–linear piecewise mixed-effects model. The obtained findings show that ML estimation approach produces reliable and accurate estimates under the conditions of small residual variance of the observed variables, and that the size of the residual variance had the most impact on the quality of model parameter estimates. Second, neither ML nor Bayesian estimation procedures performed well under all manipulated conditions with respect to the accuracy and precision of the estimated model parameters.

Fitting Growth Model Using Nonlinear Regression with Random Parameters

2011 ◽  
Vol 480-481 ◽  
pp. 1308-1312
Author(s):  
Yao Xiang Li ◽  
Li Chun Jiang
Keyword(s):  
Random Effects ◽  
Repeated Measures ◽  
Fixed Effects ◽  
Model Building ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Ratio Test ◽  
Nonlinear Mixed Effects ◽  
Nonlinear Mixed Effects Models ◽  
Mixed Effect

Mixed Effect models are flexible models to analyze grouped data including longitudinal data, repeated measures data, and multivariate multilevel data. One of the most common applications is nonlinear growth data. The Chapman-Richards model was fitted using nonlinear mixed-effects modeling approach. Nonlinear mixed-effects models involve both fixed effects and random effects. The process of model building for nonlinear mixed-effects models is to determine which parameters should be random effects and which should be purely fixed effects, as well as procedures for determining random effects variance-covariance matrices (e.g. diagonal matrices) to reduce the number of the parameters in the model. Information criterion statistics (AIC, BIC and Likelihood ratio test) are used for comparing different structures of the random effects components. These methods are illustrated using the nonlinear mixed-effects methods in S-Plus software.

scholarly journals Practical identifiability in the frame of nonlinear mixed effects models: the example of the in vitro erythropoiesis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ronan Duchesne ◽  
Anissa Guillemin ◽  
Olivier Gandrillon ◽  
Fabien Crauste
Keyword(s):  
Information Matrix ◽  
Mixed Effects ◽  
Individual Variability ◽  
Mixed Effects Models ◽  
Model Parameters ◽  
Nonlinear Mixed Effects ◽  
Nonlinear Mixed Effects Models ◽  
Erythroid Progenitors ◽  
Practical Identifiability

Abstract Background Nonlinear mixed effects models provide a way to mathematically describe experimental data involving a lot of inter-individual heterogeneity. In order to assess their practical identifiability and estimate confidence intervals for their parameters, most mixed effects modelling programs use the Fisher Information Matrix. However, in complex nonlinear models, this approach can mask practical unidentifiabilities. Results Herein we rather propose a multistart approach, and use it to simplify our model by reducing the number of its parameters, in order to make it identifiable. Our model describes several cell populations involved in the in vitro differentiation of chicken erythroid progenitors grown in the same environment. Inter-individual variability observed in cell population counts is explained by variations of the differentiation and proliferation rates between replicates of the experiment. Alternatively, we test a model with varying initial condition. Conclusions We conclude by relating experimental variability to precise and identifiable variations between the replicates of the experiment of some model parameters.

scholarly journals Meta-Analysis of Fixed, Random and Mixed Effects Models

2019 ◽  
Vol 4 (1) ◽  
pp. 199-218
Author(s):  
Savita Jain ◽  
Suresh K. Sharma ◽  
Kanchan Jain
Keyword(s):  
Meta Analysis ◽  
The Elderly ◽  
Mixed Effects ◽  
Age Related Macular Degeneration ◽  
Mixed Effects Models ◽  
Data Sets ◽  
Mixed Effect ◽  
Severe Visual Loss ◽  
Age Related ◽  
Genetic And Environmental Factors

A statistical procedure used for integrating the results obtained from a number of findings is termed as Meta-analysis. In a systematic review, all the information collected can be best used by increasing the power of analysis. The precision of treatment effects can be improved and accessed by statistically combining the results of similar studies. A very common degenerative disease among the elderly population with multiple genetic and environmental factors is Age-Related Macular Degeneration (AMD) that leads to the distorted central vision and severe visual loss in the early and advanced stage, respectively. In this study, the data sets from the results of 20 studies on the effectiveness of the treatments against AMD disease were analysed using fixed, random and mixed effect models. Various plots Forest, Funnel, Radial, QQ were also fitted for fixed, random and mixed effects models.

scholarly journals Spatial variability in floodplain sedimentation: the use of generalized linear mixed-effects models

2010 ◽  
Vol 14 (8) ◽  
pp. 1655-1668 ◽  
Author(s):  
A. Cabezas ◽  
M. Angulo-Martínez ◽  
M. Gonzalez-Sanchís ◽  
J. J. Jimenez ◽  
F. A. Comín
Keyword(s):  
Spatial Scales ◽  
Size Composition ◽  
Vegetation Pattern ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Small Scale ◽  
Mixed Effect ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Generalized Linear Mixed Effects

Abstract. Sediment, Total Organic Carbon (TOC) and total nitrogen (TN) accumulation during one overbank flood (1.15 y return interval) were examined at one reach of the Middle Ebro River (NE Spain) for elucidating spatial patterns. To achieve this goal, four areas with different geomorphological features and located within the study reach were examined by using artificial grass mats. Within each area, 1 m2 study plots consisting of three pseudo-replicates were placed in a semi-regular grid oriented perpendicular to the main channel. TOC, TN and Particle-Size composition of deposited sediments were examined and accumulation rates estimated. Generalized linear mixed-effects models were used to analyze sedimentation patterns in order to handle clustered sampling units, specific-site effects and spatial self-correlation between observations. Our results confirm the importance of channel-floodplain morphology and site micro-topography in explaining sediment, TOC and TN deposition patterns, although the importance of other factors as vegetation pattern should be included in further studies to explain small-scale variability. Generalized linear mixed-effect models provide a good framework to deal with the high spatial heterogeneity of this phenomenon at different spatial scales, and should be further investigated in order to explore its validity when examining the importance of factors such as flood magnitude or suspended sediment concentration.

Exploiting random intercepts: Two case studies in sociophonetics

2012 ◽  
Vol 24 (1) ◽  
pp. 59-78 ◽  
Author(s):  
Katie Drager ◽  
Jennifer Hay
Keyword(s):  
Case Studies ◽  
Vocal Tract ◽  
Random Effect ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Mixed Effect ◽  
Accurate Model ◽  
Mixed Effect Modeling ◽  
Potential Tool

AbstractAn increasing number of sociolinguists are using mixed effects models, models which allow for the inclusion of both fixed and random predicting variables. In most analyses, random effect intercepts are treated as a by-product of the model; they are viewed simply as a way to fit a more accurate model. This paper presents additional uses for random effect intercepts within the context of two case studies. Specifically, this paper demonstrates how random intercepts can be exploited to assist studies of speaker style and identity and to normalize for vocal tract size within certain linguistic environments. We argue that, in addition to adopting mixed effect modeling more generally, sociolinguists should view random intercepts as a potential tool during analysis.

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