昆虫种群数据分析及在SPSS软件上的实现

选择合适的统计分析方法对昆虫种群分析至关重要。本文以昆虫种群数据常用的分析方法为基础,介绍了单因素方差分析、多因素方差分析、重复测量方差分析和回归分析等多种分析方法的基本原理,强调了各种分析方法的应用前提,避免误用方法导致结果判读产生偏差,并结合SPSS软件的使用,实现相应的分析,旨在为昆虫种群数据分析提供方法论的参考。     

Bayesian Methods for Regression Using Surrogate Variables

If a dependent variable in a regression analysis is exceptionally expensive or hard to obtain the overall sample size used to fit the model may be limited. To avoid this one may use a cheaper or more easily collected “surrogate” variable to supplement the expensive variable. The regression analysis will be enhanced to the degree the surrogate is associated with the costly dependent variable. We develop a Bayesian approach incorporating surrogate variables in regression based on a two‐stage experiment. Illustrative examples are given, along with comparisons to an existing frequentist method. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Application of Quasi-Steady-State Methods to Nonlinear Models of Intracellular Transport by Molecular Motors

Molecular motors such as kinesin and dynein are responsible for transporting material along microtubule networks in cells. In many contexts, motor dynamics can be modelled by a system of reaction–advection–diffusion partial differential equations (PDEs). Recently, quasi-steady-state (QSS) methods have been applied to models with linear reactions to approximate the behaviour of the full PDE system. Here, we extend this QSS reduction methodology to certain nonlinear reaction models. The QSS method relies on the assumption that the nonlinear binding and unbinding interactions of the cellular motors occur on a faster timescale than the spatial diffusion and advection processes. The full system dynamics are shown to be well approximated by the dynamics on the slow manifold. The slow manifold is parametrized by a single scalar quantity that satisfies a scalar nonlinear PDE, called the QSS PDE. We apply the QSS method to several specific nonlinear models for the binding and unbinding of molecular motors, and we use the resulting approximations to draw conclusions regarding the parameter dependence of the spatial distribution of motors for these models.     

Combining Global and Marginal Tests to Compare Two Treatments on Multiple Endpoints

We consider the problem of comparing two treatments on multiple endpoints where the goal is to identify the endpoints that have treatment effects, while controlling the familywise error rate. Two current approaches for this are (i) applying a global test within a closed testing procedure, and (ii) adjusting individual endpoint p‐values for multiplicity. We propose combining the two current methods. We compare the combined method with several competing methods in a simulation study. It is concluded that the combined approach maintains higher power under a variety of treatment effect configurations than the other methods and is thus more power‐robust.     

Calibrating Statistical Population Reconstruction Models Using Catch-Effort and Index Data

Abstract: This paper illustrates how age-at-harvest data, when combined with hunter-effort information routinely collected by state game management agencies, can be used to estimate and monitor trends in big game abundance. Twenty-four years of age-at-harvest data for black-tailed deer (Odocoileus hemionus) were analyzed to produce abundance estimates ranging from 1,281 adult females to 3,232 adult females on a 22,079-ha tree farm in Pierce County, Washington, USA. The annual natural survival probability was estimated to be 0.7293 ( = 0.0097) for this female population. The estimated abundance was highly correlated with an independent browse damage index (r = 0.8131, P < 0.001). A population reconstruction incorporating the browse index did not substantially improve the model fit but did provide an auxiliary model for predicting deer abundance. This population reconstruction illustrates a cost-effective alternative to expensive big game survey methods.     

Statistical Analysis of Uniparental Disomy Data Using Hidden Markov Models

Genetic studies of uniparental disomy (UPD) employing many markers have helped geneticists to gain a better understanding of the molecular mechanisms underlying nondisjunction. However, most existing methods cannot simultaneously analyze all genetic markers and consistently incorporate crossover interference; they thus fail to make the most use of genetic information in the data. In the present article, we describe a hidden Markov model for multilocus uniparental disomy data. This method is based on the chi-square model for the crossover process and can simultaneously incorporate all marker information including untyped and uninformative markers. We then apply this novel method to analyze a set of UPD15 data.     

Using Regression Models to Analyze Randomized Trials: Asymptotically Valid Hypothesis Tests Despite Incorrectly Specified Models

Summary .  Regression models are often used to test for cause-effect relationships from data collected in randomized trials or experiments. This practice has deservedly come under heavy scrutiny, because commonly used models such as linear and logistic regression will often not capture the actual relationships between variables, and incorrectly specified models potentially lead to incorrect conclusions. In this article, we focus on hypothesis tests of whether the treatment given in a randomized trial has any effect on the mean of the primary outcome, within strata of baseline variables such as age, sex, and health status. Our primary concern is ensuring that such hypothesis tests have correct type I error for large samples. Our main result is that for a surprisingly large class of commonly used regression models, standard regression-based hypothesis tests (but using robust variance estimators) are guaranteed to have correct type I error for large samples, even when the models are incorrectly specified. To the best of our knowledge, this robustness of such model-based hypothesis tests to incorrectly specified models was previously unknown for Poisson regression models and for other commonly used models we consider. Our results have practical implications for understanding the reliability of commonly used, model-based tests for analyzing randomized trials.     

Fitting Mixed Poisson Regression Models Using Quasi-Likelihood Methods

This paper is to investigate the use of the quasi-likelihood, extended quasi-likelihood, and pseudo-likelihood approach to estimating and testing the mean parameters with respect to two variance models, M1: φ μ^θ(1+μphis;) and M2: φ μ^θ(1+τ). Simulation was conducted to compare the bias and standard deviation, and type I error of the Wald tests, based on the model-based and robust variance estimates, using the three semi-parametric approaches under four mixed Poisson models, two variance structures, and two sample sizes. All methods perform reasonably well in terms of bias. Type I error of the Wald test, based on either the model-based or robust estimate, tends to be larger than the nominal level when over-dispersion is moderate. The extended quasi-likelihood method with the variance model M1 performs more consistently in terms of the efficiency and controlling the type I error than with the model M2, and better than the pseudo-likelihood approach with either the M1 or M2 model. The model-based estimate seems to perform better than the robust estimate when the sample size is small.     

Statistical Methods for Identifying Wolf Kill Sites Using Global Positioning System Locations

Abstract: Accurate estimates of kill rates remain a key limitation to addressing many predator—prey questions. Past approaches for identifying kill sites of large predators, such as wolves (Canis lupus), have been limited primarily to areas with abundant winter snowfall and have required intensive ground-tracking or aerial monitoring. More recently, attempts have been made to identify clusters of locations obtained using Global Positioning System (GPS) collars on predators to identify kill sites. However, because decision rules used in determining clusters have not been consistent across studies, results are not necessarily comparable. We illustrate a space—time clustering approach to statistically define clusters of wolf GPS locations that might be wolf kill sites, and we then use binary and multinomial logistic regression to model the probability of a cluster being a non—kill site, kill site of small-bodied prey species, or kill site of a large-bodied prey species. We evaluated our approach using field visits of kills and assessed the accuracy of the models using an independent dataset. The cluster-scan approach identified 42–100% of wolf-killed prey, and top logistic regression models correctly classified 100% of kills of large-bodied prey species, but 40% of small-bodied prey species were classified as nonkills. Although knowledge of prey distribution and vulnerability may help refine this approach, identifying small-bodied prey species will likely remain problematic without intensive field efforts. We recommend that our approach be utilized with the understanding that variation in prey body size and handling time by wolves will likely have implications for the success of both the cluster scan and logistic regression components of the technique. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):798–807; 2008)     

Investigating Polycyclic Aromatic Hydrocarbons Profiles in Higher Plants Using Statistical Models

Thirty-six higher plants sampled from Olomoro, Irri, Uzere, and Oginni exploration sites in the Niger Delta region of Nigeria were subjected to GC/MS analysis to assess the occurrence, distribution and profiles of polycyclic aromatic hydrocarbons (PAHs) contained in them. The Σ₂₈PAHs ranged from 335 to 3094 ng/g. The results of the nonparametric regression models showed that PAHs concentration in a plant cannot be used in isolation to deduce the total PAHs concentration in soils hosting the plant since PAHs concentration in a plant is influenced by the presence (or absence) of other plants in that location. A combination of Factor analysis (FA) and principal component analysis (PCA) were used to recognize PAHs concentration patterns among the plants in the studied locations and individual PAHs compounds. Woody annuals and perennial plants formed similar patterns in Oginni and Irri locations. Three main clusters were formed by all the compounds with naphthalene and 2-methylnaphthalene standing as outliers in all the four locations.

Supplemental materials are available for this article. Go to the publisher's online edition of International Journal of Phytoremediation to view the supplemental file.     

Methods for analysing cardiovascular studies with repeated measures

Background. Repeated measurements in a single subject are generally more similar than unrepeated measurements in different subjects. Unrepeated analyses of repeated data cause underestimation of the treatment effects. Objective. To review methods adequate for the analysis of cardiovascular studies with repeated measures. Results. (1) For between-subjects comparisons, summary measures and random-effects mixedlinear models are possible. Examples of summary measures include the area under the curve of drug time-concentration and time-efficacy curves, maximal values, mean values, and changes from baseline. A problem is that precision is lost because averages, rather than individual data, are applied. Random-effects mixed-linear models, available in SPSS statistical software and other software programmes, provide better precision for that purpose. (2) For within-subjects comparisons, repeated-measures ANOVAs are available in SPSS and other software programmes. Subgroup factors such as gender differences and age class can be included. Discussion. For non-Gaussian data, Wilcoxon's and Friedman's tests are available, for binary data McNemar's tests can be used in case of two repeated observations. No standard methods are available for repeated binary measures with more than two observations. The purpose of this review was not to present a complete report but, rather, to underline that ample efforts should be made to account for the special nature of repeated measures. (Neth Heart J 2009;17:429–33.)     

Estimating Litter Decomposition Rate in Single-Pool Models Using Nonlinear Beta Regression

Litter decomposition rate (k) is typically estimated from proportional litter mass loss data using models that assume constant, normally distributed errors. However, such data often show non-normal errors with reduced variance near bounds (0 or 1), potentially leading to biased k estimates. We compared the performance of nonlinear regression using the beta distribution, which is well-suited to bounded data and this type of heteroscedasticity, to standard nonlinear regression (normal errors) on simulated and real litter decomposition data. Although the beta model often provided better fits to the simulated data (based on the corrected Akaike Information Criterion, AIC_c), standard nonlinear regression was robust to violation of homoscedasticity and gave equally or more accurate k estimates as nonlinear beta regression. Our simulation results also suggest that k estimates will be most accurate when study length captures mid to late stage decomposition (50–80% mass loss) and the number of measurements through time is ≥5. Regression method and data transformation choices had the smallest impact on k estimates during mid and late stage decomposition. Estimates of k were more variable among methods and generally less accurate during early and end stage decomposition. With real data, neither model was predominately best; in most cases the models were indistinguishable based on AIC_c, and gave similar k estimates. However, when decomposition rates were high, normal and beta model k estimates often diverged substantially. Therefore, we recommend a pragmatic approach where both models are compared and the best is selected for a given data set. Alternatively, both models may be used via model averaging to develop weighted parameter estimates. We provide code to perform nonlinear beta regression with freely available software.     

MSMBuilder: Statistical Models for Biomolecular Dynamics

MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. MSMBuilder was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements.     

Optimum Sorption Isotherm by Linear and Nonlinear Methods for Safranin onto Alkali-Treated Rice Husk

Rice husk, a lignocellulosic by-product of the agroindustry, was treated with alkali and used as a low-cost adsorbent for the removal of safranin from aqueous solution in batch adsorption procedure. In order to estimate the equilibrium parameters, the equilibrium adsorption data were analyzed using the following two-parameter isotherms: Freundlich, Langmuir, and Temkin. A comparison of linear and nonlinear regression methods in selecting the optimum adsorption isotherm was applied on the experimental data. Six linearized isotherm models (including four linearized Langmuir models) and three nonlinear isotherm models are thus discussed in this paper. In order to determine the best-fit isotherm predicted by each method, seven error functions namely, coefficient of determination (r ²), the sum of the squares of the errors (SSE), sum of the absolute errors (SAE), average relative error (ARE), hybrid fractional error-function (HYBRID), Marquardt's percent standard deviation (MPSD), and the chi-square test (χ²) were used. It was concluded that the nonlinear method is a better way to obtain the isotherm parameters and the data were in good agreement with the Langmuir isotherm model.     

赤拟谷盗模拟种公畜选择──常规与改进方法的比较

本实验用实验昆虫赤拟谷盗两个世代各３０个家系的８５９和７２６只蛹的２３日龄蛹长和蛹宽作为选择性状模拟种公畜选择,对常规方法和改进方法进行比较。以Ｈｅｎｄｅｒｓｏｎ方法Ⅲ和综合指数作为常规方法,用约束最大似然法（ＲＥＭＬ）和ＢＬＵＰ估计方差组分和育种值为改进方法。结果表明,常规法估计遗传参数在两世代中不稳定,而改进方法弥补此缺点。对两世代个体和家系育种值排队,结果表明两种方法在前几名无差别,说明在选择强度大（留种率低）时,两种方法效果基本一致。     

Predictors for Physical Activity in Adolescent Girls Using Statistical Shrinkage Techniques for Hierarchical Longitudinal Mixed Effects Models

We examined associations among longitudinal, multilevel variables and girls’ physical activity to determine the important predictors for physical activity change at different adolescent ages. The Trial of Activity for Adolescent Girls 2 study (Maryland) contributed participants from 8th (2009) to 11th grade (2011) (n=561). Questionnaires were used to obtain demographic, and psychosocial information (individual- and social-level variables); height, weight, and triceps skinfold to assess body composition; interviews and surveys for school-level data; and self-report for neighborhood-level variables. Moderate to vigorous physical activity minutes were assessed from accelerometers. A doubly regularized linear mixed effects model was used for the longitudinal multilevel data to identify the most important covariates for physical activity. Three fixed effects at the individual level and one random effect at the school level were chosen from an initial total of 66 variables, consisting of 47 fixed effects and 19 random effects variables, in additional to the time effect. Self-management strategies, perceived barriers, and social support from friends were the three selected fixed effects, and whether intramural or interscholastic programs were offered in middle school was the selected random effect. Psychosocial factors and friend support, plus a school’s physical activity environment, affect adolescent girl’s moderate to vigorous physical activity longitudinally.     

Nonlinear genotypic response to macro- and microenvironments

 Environmental variation can be due to macro- and microenvironments. Whereas macroenvironments, such as climate, density, and nutritional levels, are distinguishable, microenvironments including external random errors or internal “accidents” of an organism cannot be well specified. A quantitative genetic model is proposed to estimate the genetic control of response of genotypes to these two kinds of environments. The model extends Gimelfarb’s additive-multiplicative model for genotype×environment interaction by considering both macro- and microenvironmental variation and the mechanistic basis of genotypic response to a particular macroenvironmental factor. It is further extended to estimate genetic correlations between quantitative traits under the additive-multiplicative model. An example from a forest tree was used to illustrate the application and power of the new model. In many aspects, the model displays remarkable advantages over the traditional analysis of variance used to study genotype×environment interaction. Received: 15 July 1997 / Accepted: 19 September 1997     

Development and Application of Genomic Control Methods for Genome-Wide Association Studies Using Non-Additive Models

Genome-wide association studies (GWAS) comprise a powerful tool for mapping genes of complex traits. However, an inflation of the test statistic can occur because of population substructure or cryptic relatedness, which could cause spurious associations. If information on a large number of genetic markers is available, adjusting the analysis results by using the method of genomic control (GC) is possible. GC was originally proposed to correct the Cochran-Armitage additive trend test. For non-additive models, correction has been shown to depend on allele frequencies. Therefore, usage of GC is limited to situations where allele frequencies of null markers and candidate markers are matched. In this work, we extended the capabilities of the GC method for non-additive models, which allows us to use null markers with arbitrary allele frequencies for GC. Analytical expressions for the inflation of a test statistic describing its dependency on allele frequency and several population parameters were obtained for recessive, dominant, and over-dominant models of inheritance. We proposed a method to estimate these required population parameters. Furthermore, we suggested a GC method based on approximation of the correction coefficient by a polynomial of allele frequency and described procedures to correct the genotypic (two degrees of freedom) test for cases when the model of inheritance is unknown. Statistical properties of the described methods were investigated using simulated and real data. We demonstrated that all considered methods were effective in controlling type 1 error in the presence of genetic substructure. The proposed GC methods can be applied to statistical tests for GWAS with various models of inheritance. All methods developed and tested in this work were implemented using R language as a part of the GenABEL package.