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Ecologists are increasingly familiar with Bayesian statistical modeling and its associated Markov chain Monte Carlo (MCMC) methodology to infer about or to discover interesting effects in data. The complexity of ecological data often suggests implementation of (statistical) models with a commensurately rich structure of effects, including crossed or nested (i.e., hierarchical or multi-level) structures of fixed and/or random effects. Yet, our experience suggests that most ecologists are not familiar with subtle but important problems that often arise with such models and with their implementation in popular software. Of foremost consideration for us is the notion of effect identifiability, which generally concerns how well data, models, or implementation approaches inform about, i.e., identify, quantities of interest. In this paper, we focus on implementation pitfalls that potentially misinform subsequent inference, despite otherwise informative data and models. We illustrate the aforementioned issues using random effects regressions on synthetic data. We show how to diagnose identifiability issues and how to remediate these issues with model reparameterization and computational and/or coding practices in popular software, with a focus on JAGS, OpenBUGS, and Stan. We also show how these solutions can be extended to more complex models involving multiple groups of nested, crossed, additive, or multiplicative effects, for models involving random and/or fixed effects. Finally, we provide example code (JAGS/OpenBUGS and Stan) that practitioners can modify and use for their own applications.

Over the last two decades, the panel data model has become a focus of applied research. While there are numerous proposals for soft regression models in the literature, only a few linear regression models have been proposed based on fuzzy panel data. However, these models have serious limitations. This study is an attempt to propose a kind of two-way fuzzy panel regression model with crossed effects, fuzzy responses and crisp predictors to overcome the shortcomings of these models in real applications. The corresponding parameter estimation is provided based on a three-step procedure. For this purpose, the conventional least absolute error technique is employed. Two real data sets are analyzed to investigate the fitting and predictive capabilities of the proposed fuzzy panel regression model. These real data applications demonstrate that our proposed model has good fitting accuracy and predictive performance.

Linear mixed models with large imbalanced crossed random effects structures pose severe computational problems for maximum likelihood estimation and for Bayesian analysis. The costs can grow as fast as N 3 / 2 when there are N observations. Such problems arise in any setting where the underlying factors satisfy a many-to-many rather than a nested relationship. The former are common in electronic commerce applications, where N can be quite large. Methods that do not account for the correlation structure can greatly underestimate the uncertainty. Thus, we propose a method of moments approach that takes account of the correlation structure and that can be computed at a cost of O(N). The method of moments can be parallelized easily, because it is based on sums and it does not require parametric distributional assumptions, tuning parameters, or convergence diagnostics. For the regression coefficients, we give conditions for consistency and asymptotic normality, as well as a consistent variance estimate. We also provide the conditions necessary for a consistent estimation of the variance components, as well as consistent estimates of a mildly conservative upper bound on the variance of the variance component estimates. All of these computations require a total processing time of O(N). We illustrate the algorithm using data from Stitch Fix, where the crossed random effects correspond to clients and items. Here, a naive analysis can overestimate the effective sample size by hundreds and, thus yield unreliable conclusions about the parameters.

We consider a discrete latent variable model for two-way data arrays, which allows one to simultaneously produce clusters along one of the data dimensions (e.g., exchangeable observational units or features) and contiguous groups, or segments, along the other (e.g., consecutively ordered times or locations). The model relies on a hidden Markov structure but, given its complexity, cannot be estimated by full maximum likelihood. Therefore, we introduce a composite likelihood methodology based on considering different subsets of the data. The proposed approach is illustrated by simulation, and with an application to genomic data.

This paper considers the problem of testing statistical hypotheses about the variance components under linear models with crossed random effects. The objective here is two-fold. First, new derivations of exactly distributed F test statistics are presented. Second, an alternative Monte Carlo permutation procedure is proposed to approximate the distribution of the F statistics, which shows its usefulness when the error components distributions depart from normality. Transformations that uniquely decompose the covariance structure of the response vector and do not sacrifice any part of the data, as existing methods do, are presented in matrix form. The suggested transformations highlight the exchangeable covariance structure of the model under the null hypotheses of interest and thus motivate the use of the permutation procedure. Comments on the performance of the proposed procedure compared to existing tests as well as using a real data example are provided.

Rootstock micropropagation has been extensively used as an alternative to propagation by cuttings. Although studies have recently been conducted on other species, no conclusive reports have been published on the effect of rootstock micropropagation on the field performance of fruit trees. Here, we present the results of a five-year study of peach varieties grafted on two rootstocks (Adesoto 101 and Adafuel), either micropropagated or propagated by cuttings, to ascertain the effect of the rootstock propagation method on field performance. Fruit trees are woody plants with a long life cycle; so, to reduce the influence of environmental or cultural factors on the agronomical results, studies need to last for several years, in which data are obtained from the same individuals over time (longitudinal data). This hinders the analysis because these data lack independence. In contrast with a more traditional approach with data aggregation and repeated-measures ANOVA analysis, in this study, we used linear mixed-effects models to control the variance associated with random factors without data aggregation. The growth of the fruit trees did not appreciably differ between the rootstock propagation methods, neither in the flowering period nor in the yield. The models constructed for different parameters of the field performance (trunk cross-sectional area (TCSA), cumulative yield, cumulative yield efficiency, and cumulative crop load) showed a very good fit (R2 > 0.97), allowing the conclusion that the rootstock propagation method did not affect the field performance of fruit trees in this study.

We present new results for consistency of maximum likelihood estimators with a focus on multivariate mixed models. Our theory builds on the idea of using subsets of the full data to establish consistency of estimators based on the full data. It requires neither that the data consist of independent observations, nor that the observations can be modeled as a stationary stochastic process. Compared to existing asymptotic theory using the idea of subsets, we substantially weaken the assumptions, bringing them closer to what suffices in classical settings. We apply our theory in two multivariate mixed models for which it was unknown whether maximum likelihood estimators are consistent. The models we consider have nonstochastic predictors and multivariate responses which are possibly mixed-type (some discrete and some continuous).

Intensive fish farms are often affected by different organisms that produce infectious diseases. To control this situation, antibiotics have been used with negative repercussions for the environment and health. As an alternative to this, probiotics are used that show more effective and respectful results with the environment. The aim of this project is to obtain new potentially probiotic strains against one of the most relevant pathogens of marine aquaculture, vibrios. These bacterial strains were isolated from the gills and intestines of European seabass, meagre and common sole. Later, were evaluated in vitro against 6 pathogenic strains of the genus Vibrio to demonstrate the production of antagonistic effects, production of antibacterial substances, resistance to bile, resistance to pH gradients, adhesion and growth to mucus, competition for nutrients and hydrophobicity. A total of 156 bacterial strains were isolated, but only 7 strains of the genera Alcaligenes, Pseudomonas, Shewanella and Proteus, showed excellent in vitro results to be considered as candidates to be reevaluated by in vivo tests, to determine their harmlessness and protective effect after challenge, and elucidating in future studies their use as possible probiotic strains for aquaculture, highlighting the results obtained with the strain Alcaligenes faecalis subsp. faecalis-1.

This study examined how force tremor and muscle activity are altered between limbs when a visual target is removed for one limb during bilateral index finger abduction. Isometric index finger abduction force was examined in healthy adults (23 ± 4 years) when both index fingers abducted simultaneously. Abduction forces ranged from 5 to 20 % maximum voluntary contraction, and these target forces were displayed on a PC monitor in front of the subject. Force tremor and first dorsal interosseous (FDI) activity were first collected while subjects viewed visual targets for both index fingers and then when the visual target was removed for the non-dominant index finger. Subjects successfully matched the force amplitudes generated for both limbs regardless of visual condition. When the visual target was removed for one limb, force tremor increased in this limb ( p  < 0.01). Different power spectral profiles were evident for each FDI EMG when targets were available for both limbs ( p  < 0.05); however, when one target was removed, the pattern of FDI EMG for the limb without a visual target closely reflected FDI EMG for the limb which had the visual target. The CNS actively modulates muscle activity in each limb to perform visually guided isometric contractions. Given that the goal was to match force output with both limbs, the requirements of the task must be established from the limb that had a visual target, and a copy of those motor commands appears to have been sent to the FDI of the limb without a visual target.

Training a muscle group in one limb yields strength gains bilaterally--the so-called cross-education effect. However, to date there has been little study of the targeted application of this phenomenon in a manner relevant to clinical rehabilitation. For example, it may be applicable post-stroke, where hemiparesis leads to ankle flexor weakness. The purpose of this study was to examine the effects of high-intensity unilateral dorsiflexion resistance training on agonist (tibialis anterior, TA) and antagonist (plantarflexor soleus, SOL) muscular strength and H-reflex excitability in the trained and untrained limbs. Ankle flexor and extensor torque, as well as SOL and TA H-reflexes evoked during low-level contraction, were measured before and after 5 weeks of dorsiflexion training (n = 19). As a result of the intervention, dorsiflexor maximal voluntary isometric contraction force (MVIC) significantly increased (P < 0.05) in both the trained and untrained limbs by 14.7 and 8.4%, respectively. No changes in plantarflexor MVIC force were observed in either limb. Significant changes in H-reflex excitability threshold were also detected: H@thresh significantly increased in the trained TA and SOL; and H@max decreased in both SOL muscles. These findings reveal that muscular crossed effects can be obtained in the ankle dorsiflexor muscles and provide novel information on agonist and antagonist spinal adaptations that accompany unilateral training. It is possible that the ability to strengthen the ankle dorsiflexors bilaterally could be applied in post-stroke rehabilitation, where ankle flexor weakness could be counteracted via dorsiflexor training in the less-affected limb.