scholarly journals Nonlinear mixed-effects growth models: A tutorial using 'saemix' in R

Methodology ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. 250-270
Author(s):  
Peter Boedeker
Keyword(s):  
Repeated Measures ◽  
Mixed Model ◽  
Growth Parameters ◽  
Growth Models ◽  
Simulated Data ◽  
Growth Curves ◽  
Rate Of Change ◽  
Model Framework ◽  
Growth Variability ◽  
Starting Point

Modeling growth across repeated measures of individuals and evaluating predictors of growth can reveal developmental patterns and factors that affect those patterns. When growth follows a sigmoidal shape, the Logistic, Gompertz, and Richards nonlinear growth curves are plausible. These functions have parameters that specifically control the starting point, total growth, overall rate of change, and point of greatest growth. Variability in growth parameters across individuals can be explained by covariates in a mixed model framework. The purpose of this tutorial is to provide analysts a brief introduction to these growth curves and demonstrate their application. The 'saemix' package in R is used to fit models to simulated data to answer specific research questions. Enough code is provided in-text to describe how to execute the analyses with the complete code and data provided in Supplementary Materials.

Growth Models with Corrections for the Retardative Effects of Tagging

1994 ◽  
Vol 51 (2) ◽  
pp. 263-267 ◽  
Author(s):  
Yongshun Xiao
Keyword(s):  
Growth Parameters ◽  
Growth Models ◽  
Simulated Data ◽  
Fish Growth ◽  
Lates Calcarifer ◽  
General Utility ◽  
Potential Applications ◽  
Length Increment ◽  
Animal Growth ◽  
Special Case

Length increment data from mark–recapture experiments are commonly used to obtain information on animal growth, assuming that tagging does not affect the growth of marked animals. The assumption is violated in many studies, but the effects of tagging on growth and estimates of growth parameters have not been and cannot be examined without appropriate models. This paper describes a model allowing quantification and estimation of the retarding effects of tagging on animal growth simultaneously with growth parameters in all existing growth models, reduction or elimination of biases in growth parameters induced by tagging, and relaxation of a key assumption in growth analysis using length increment data. A special case of this model was applied to simulated data and to tagging data from a centropomid perch (Lates calcarifer) to demonstrate its general utility. Tagging was inferred to have stopped the fish growth for 36.44 d (ASE = 12.70 d) if von Bertalanffy growth is assumed, but the period of recovery from tagging seemed size or age independent within the size range studied. If tagging retards animal growth, L∞ is slightly overestimated and K underestimated for unbiased data. Potential applications and limitations of the model are also discussed.

scholarly journals The Standard Deviation Structure as a New Approach to Growth Analysis in Weight and Length Data of Farmed Lutjanus guttatus

Fishes ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 60
Author(s):  
Sergio G. Castillo-Vargasmachuca ◽  
Eugenio Alberto Aragón-Noriega ◽  
Guillermo Rodríguez-Domínguez ◽  
Leonardo Martínez-Cárdenas ◽  
Eulalio Arámbul-Muñoz ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Growth Parameters ◽  
Growth Models ◽  
Growth Curves ◽  
Gompertz Model ◽  
Information Criterion ◽  
Weight Growth ◽  
Length Data ◽  
Size At Age ◽  
The Right

In the present study, size-at-age data (length and weight) of marine cage-reared spotted rose snapper Lutjanus guttatus were analyzed under four different variance assumptions (observed, constant, depensatory, and compensatory variances) to analyze the robustness of selecting the right standard deviation structure to parametrize the von Bertalanffy, Logistic, and Gompertz models. The selection of the best model and variance criteria was obtained based on the Bayesian information criterion (BIC). According to the BIC results, the observed variance in the present study was the best way to parametrize the three abovementioned growth models, and the Gompertz model best represented the length and weight growth curves. Based on these results, using the observed error structure to calculate the growth parameters in multi-model inference analyses is recommended.

scholarly journals Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior

2019 ◽  
Vol 11 (24) ◽  
pp. 2897 ◽  
Author(s):  
Yuhui Zheng ◽  
Feiyang Wu ◽  
Hiuk Jae Shim ◽  
Le Sun
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Simulated Data ◽  
Hyperspectral Data ◽  
Low Rank ◽  
Data Sets ◽  
Self Similarity ◽  
Model Framework

Hyperspectral unmixing is a key preprocessing technique for hyperspectral image analysis. To further improve the unmixing performance, in this paper, a nonlocal low-rank prior associated with spatial smoothness and spectral collaborative sparsity are integrated together for unmixing the hyperspectral data. The proposed method is based on a fact that hyperspectral images have self-similarity in nonlocal sense and smoothness in local sense. To explore the spatial self-similarity, nonlocal cubic patches are grouped together to compose a low-rank matrix. Then, based on the linear mixed model framework, the nuclear norm is constrained to the abundance matrix of these similar patches to enforce low-rank property. In addition, the local spatial information and spectral characteristic are also taken into account by introducing TV regularization and collaborative sparse terms, respectively. Finally, the results of the experiments on two simulated data sets and two real data sets show that the proposed algorithm produces better performance than other state-of-the-art algorithms.

Genotypic and environmental variability of yield from seven different crops in Croatian official variety trials and comparison with on-farm trends

2016 ◽  
Vol 155 (5) ◽  
pp. 804-811 ◽  
Author(s):  
M. ZORIĆ ◽  
J. GUNJAČA ◽  
D. ŠIMIĆ
Keyword(s):  
Variance Components ◽  
Western Europe ◽  
Mixed Model ◽  
Environmental Variability ◽  
Relative Magnitude ◽  
Genotypic Effect ◽  
Model Framework ◽  
Data Set ◽  
Starting Point ◽  
Genetic Value

SUMMARYAssessment of the value for cultivation and use (VCU) of a new cultivar, essential for its official registration, is done through a series of trials carried out over a 2–3-year period and across many locations. In a set of multi-environment VCU trials, evaluation of new genotypes can be a laborious task due to the presence of genotype by environment interactions, which can hide their true genetic value. In an attempt to reveal the true genetic value of new cultivars, a good starting point is investigation of the importance of various genetic and environmental sources of variation, which can be done by estimating relative magnitude of corresponding variance components within the mixed model framework.Genotype × location × year (G × L × Y) data set for seven crops taken from the 10-year period 2001–10 was used in the present study to estimate the variance components for main effects and their interactions in Croatian VCU trials. Depending on the crop, the most important and least important components were Y or LY, and L or GL, respectively. Genotypic effect was relatively small, ranging from 2·1 to 13·4% of the total variation. The current results are comparable with the relative sizes of the variance components obtained in studies from four- to sixfold larger countries, indicating that the environments within Croatia, if sufficiently widely sampled, can provide as extreme cultivar responses as a geographically more dispersed set of VCU trials. The gap range in different crops is much wider (30–60%) than in Western Europe (up to 30%), but it remained constant over the 10-year period.

Measuring growth patterns in the field: effects of sampling regime and methods on standardized estimates

2011 ◽  
Vol 89 (6) ◽  
pp. 529-537 ◽  
Author(s):  
J.G.A. Martin ◽  
F. Pelletier
Keyword(s):  
Mixed Models ◽  
Repeated Measures ◽  
Mixed Model ◽  
Linear Models ◽  
Simulated Data ◽  
Growth Patterns ◽  
Repeated Measurements ◽  
Least Squares Regression ◽  
Sampling Regime ◽  
Parturition Date

Although mixed effects models are widely used in ecology and evolution, their application to standardized traits that change within season or across ontogeny remains limited. Mixed models offer a robust way to standardize individual quantitative traits to a common condition such as body mass at a certain point in time (within a year or across ontogeny), or parturition date for a given climatic condition. Currently, however, most researchers use simple linear models to accomplish this task. We use both empirical and simulated data to underline the application of mixed models for standardizing trait values to a common environment for each individual. We show that mixed model standardizations provide more accurate estimates of mass parameters than linear models for all sampling regimes and especially for individuals with few repeated measures. Our simulations and analyses on empirical data both confirm that mixed models provide a better way to standardize trait values for individuals with repeated measurements compared with classical least squares regression. Linear regression should therefore be avoided to adjust or standardize individual measurements

scholarly journals Multivariate Generalized Linear Mixed-Effects Models for the Analysis of Clinical Trial–Based Cost-Effectiveness Data

2021 ◽  
pp. 0272989X2110038
Author(s):  
Felix Achana ◽  
Daniel Gallacher ◽  
Raymond Oppong ◽  
Sungwook Kim ◽  
Stavros Petrou ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Simulated Data ◽  
Repeated Measurements ◽  
Mixed Data ◽  
Economic Evaluations ◽  
Data Types ◽  
Model Framework ◽  
New Methods

Economic evaluations conducted alongside randomized controlled trials are a popular vehicle for generating high-quality evidence on the incremental cost-effectiveness of competing health care interventions. Typically, in these studies, resource use (and by extension, economic costs) and clinical (or preference-based health) outcomes data are collected prospectively for trial participants to estimate the joint distribution of incremental costs and incremental benefits associated with the intervention. In this article, we extend the generalized linear mixed-model framework to enable simultaneous modeling of multiple outcomes of mixed data types, such as those typically encountered in trial-based economic evaluations, taking into account correlation of outcomes due to repeated measurements on the same individual and other clustering effects. We provide new wrapper functions to estimate the models in Stata and R by maximum and restricted maximum quasi-likelihood and compare the performance of the new routines with alternative implementations across a range of statistical programming packages. Empirical applications using observed and simulated data from clinical trials suggest that the new methods produce broadly similar results as compared with Stata’s merlin and gsem commands and a Bayesian implementation in WinBUGS. We highlight that, although these empirical applications primarily focus on trial-based economic evaluations, the new methods presented can be generalized to other health economic investigations characterized by multivariate hierarchical data structures.

scholarly journals Mathematical models to describe the growth curves of Vietnamese Ri chicken

2023 ◽  
Vol 83 ◽  
Author(s):  
T. H. Nguyen ◽  
C. X. Nguyen ◽  
M. Q. Luu ◽  
A. T. Nguyen ◽  
D. H. Bui ◽  
...  
Keyword(s):  
Growth Curve ◽  
Model Comparison ◽  
Growth Parameters ◽  
Growth Models ◽  
Growth Curves ◽  
Information Criterion ◽  
Chicken Breed ◽  
Richards Model ◽  
Genetics And Genomics ◽  
Backyard Chicken

Abstract Ri chicken is the most popular backyard chicken breed in Vietnam, but little is known about the growth curve of this breed. This study compared the performances of models with three parameters (Gompertz, Brody, and Logistic) and models containing four parameters (Richards, Bridges, and Janoschek) for describing the growth of Ri chicken. The bodyweight of Ri chicken was recorded weekly from week 1 to week 19. Growth models were fitted using minpack.lm package in R software and Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and root mean square error (RMSE) were used for model comparison. Based on these criteria, the models having four parameters showed better performance than the ones with three parameters, and the Richards model was the best one for males and females. The lowest and highest value of asymmetric weights (α) were obtained by Bridges and Brody models for each of sexes, respectively. Age and weight estimated by the Richard model were 8.46 and 7.51 weeks and 696.88 and 487.58 g for males and for females, respectively. Differences in the growth curves were observed between males and female chicken. Overall, the results suggested using the Richards model for describing the growth curve of Ri chickens. Further studies on the genetics and genomics of the obtained growth parameters are required before using them for the genetic improvement of Ri chickens.

Effects of Aging on the Subcortical Encoding of Stop Consonants

2020 ◽  
Vol 29 (3) ◽  
pp. 391-403
Author(s):  
Dania Rishiq ◽  
Ashley Harkrider ◽  
Cary Springer ◽  
Mark Hedrick
Keyword(s):  
Older Adults ◽  
Repeated Measures ◽  
Mixed Model ◽  
Auditory Brainstem ◽  
Auditory Brainstem Responses ◽  
Aging Effects ◽  
Formant Frequency ◽  
Place Of Articulation ◽  
Second Formant ◽  
Effects Of Aging

Purpose The main purpose of this study was to evaluate aging effects on the predominantly subcortical (brainstem) encoding of the second-formant frequency transition, an essential acoustic cue for perceiving place of articulation. Method Synthetic consonant–vowel syllables varying in second-formant onset frequency (i.e., /ba/, /da/, and /ga/ stimuli) were used to elicit speech-evoked auditory brainstem responses (speech-ABRs) in 16 young adults ( M age = 21 years) and 11 older adults ( M age = 59 years). Repeated-measures mixed-model analyses of variance were performed on the latencies and amplitudes of the speech-ABR peaks. Fixed factors were phoneme (repeated measures on three levels: /b/ vs. /d/ vs. /g/) and age (two levels: young vs. older). Results Speech-ABR differences were observed between the two groups (young vs. older adults). Specifically, older listeners showed generalized amplitude reductions for onset and major peaks. Significant Phoneme × Group interactions were not observed. Conclusions Results showed aging effects in speech-ABR amplitudes that may reflect diminished subcortical encoding of consonants in older listeners. These aging effects were not phoneme dependent as observed using the statistical methods of this study.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

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