A Nonparametrical Evaluation of Anova and MANOVA Designs Using Interaction Structure Analysis

The interaction structure analysis (ISA) is proposed as a nonparametric procedure for the evaluation of uni- and multivariate analysis of variance models. Main effects and interactions of (independent) treatment variables are replaced by interaction-types where types are defined as those treatment-response combinations which occur significantly more often than expected under a null hypothesis (H_o) of no treatment effects. The application of ISA and the typological interpretation of ISA results are illustrated for an ANOVA design from toxicology and for a MANOVA design from psychopharmacology.     

Genetic association studies: design,analysis and interpretation

This paper provides a review of the design and analysis of genetic association studies. In case control studies, the different contingency tables and their relationships to the underlying genetic model are defined. Population stratification is discussed, with suggested methods to identify and correct for the effect. The transmission disequilibrium test is provided as an alternative family-based test, which is robust to population stratification. The relative benefits of each analysis are summarised.     

The Probability Density Functions of the Contingency Coefficients,defined by KRAMER and PEARSON,in a 2-Dimensional Frequency Distribution of Grouped Bivariate Normal Variables. I

Two general measures for the degree of association in a contingency table are the contingency coefficients defined by PEARSON and KRAMER. In the case of a standardized bivariate normal distribution with correlation coefficient of the variables, whose realizations constitute the rows and columns of the table, the density functions of the two listed association measures are derived.     

Local and Regional vs. Global Contingency Testing

Contingency analysis of r × c-tables is systematized as follows: Global contingency testing based on the total chisquare under H₀ of independent row and column variables is least favorable to exhaustive interpretation. Local contingency testing based on chi-square components of the individual cells of an r × c-table and realized in Configural frequency analysis (KRAUTH and LIENERT, 1973) is more favourable to substantive interpretation. Compromising between global and local contingency testing leads to so-called generalized local and regional contingency testing. Every contingency testing (local, regional and naturally global) is including all N individuals of a sample supposed to have been drawn randomly from a defined population. Extension to 3-and t-dimensional contingency analysis is outlined, and biomedical applications are discussed.     

Classical conditioning,signal detection,and evolution

Strength of classical conditioning is increased either by increasing discriminability of the conditioned stimulus (CS) from the background, or by increasing contingency between conditioned and unconditioned stimili (US).Classical conditioning can be regarded as a decision process in which the subject has to decide whether or not to respond with a conditioned response in the presence or absence of the CS. According to modern evolutionary theories, it might be assumed that this decision process maximizes the trade-off between cost and benefits.By assuming that the decision rule maximizes expected benefit, the empirical relationship between contingency and the strength of classical conditioning is theoretically derived. In addition, when the decision rule is incorporated to a signal detection paradigm, theoretical results describing the relationship between CS discriminability and CS – US contingency with the strength of classical conditioning are in agreement with experimental data.     

The domain relativity of evolutionary contingency

A key issue in the philosophy of biology is evolutionary contingency, the degree to which evolutionary outcomes could have been different. Contingency is typically contrasted with evolutionary convergence, where different evolutionary pathways result in the same or similar outcomes. Convergences are given as evidence against the hypothesis that evolutionary outcomes are highly contingent. But the best available treatments of contingency do not, when read closely, produce the desired contrast with convergence. Rather, they produce a picture in which any degree of contingency is compatible with any degree of convergence. This is because convergence is the repeated production of a given outcome from different starting points, and contingency has been defined without reference to the size of the space of possible outcomes. In small spaces of possibilities, the production of repeated outcomes is almost assured. This paper presents a definition of contingency which includes this modal dimension in a way that does not reduce it to the binary notion of contingency found in standard modal logic. The result is a conception of contingency which properly contrasts with convergence, given some domain of possibilities and a measure defined over it. We should therefore not ask whether evolution is contingent or convergent simpliciter, but rather about the degree to which it is contingent or convergent in various domains, as measured in various ways.     

Pseudofactorialism,response structures and collective responsibility

Pseudofactorialism is defined as ‘the invalid statistical analysis that results from the misidentification of two or more response variables as representing different levels of an experimental variable or treatment factor. Most often the invalid analysis consists of use of an (n + 1)‐way anova in a situation where two or more n‐way anova s would be the appropriate approach’. I and my students examined a total of 1362 papers published from the 1960s to 2009 reporting manipulative experiments, primarily in the field of ecology. The error was present in 7% of these, including 9% of 80 experimental papers examined in 2009 issues of Ecology and the Journal of Animal Ecology. Key features of 60 cases of pseudofactorialism are tabulated as a basis for discussion of the varied ways and circumstances in which the error can occur. As co‐authors, colleagues, editors and anonymous referees and editors who approved them for publication, a total of 459 persons other than the senior authors shared responsibility for these 60 papers. Pseudofactorialism may sometimes be motivated by a desire to test whether different response variables respond in the same way to treatment factors. Proper procedures for doing that are briefly reviewed. A major cause of pseudofactorialism is the widespread failure in statistics texts, primary literature and documentation for statistics software packages to distinguish the three major components of experimental design – treatment structure, design structure, response structure – and clearly define key terms such as experimental unit, evaluation unit, split unit, factorial and repeated measures. A quick way to check for the possible presence of the pseudofactorialism is to determine whether the number of valid experimental units in a study is smaller than (i) the error degrees of freedom in a multi‐way anova ; or (ii) the total number of tallies (N) in a multi‐way contingency table. Such situations also can indicate the commission of pseudoreplication, however.     

Predicting the distribution of plant communities using annual rainfall and elevation: an example from southern Africa

Abstract. Using the results of a total floristic survey of two veld types (Arid Sweet Bushveld and Mixed Bushveld) in the northeastern Transvaal, South Africa, we linked median annual rainfall from a surface response model to each of 139 samples. The samples had been classified floristically into 15 plant communities. These communities represent two broad divisions, corresponding with the concepts embodied in the two veld types. Using contingency tables, we defined the conditions of median annual rainfall and elevation for each of the veld types. Using a geographic analysis system we predicted the distribution of the veld types in an area of 120 000 km² outside the study area. The predicted distribution was validated by comparison with a digitized version of the Acocks map. We conclude that the defined conditions of median annual rainfall and elevation provide confident criteria for the definition of these veld types.     

Stressor-response functions as a generalizable model for context dependence

Defining the context dependence of ecological states or processes is a fundamental goal of ecology. Stressor-response functions are the quantitative representation of context dependence, where the context (environmental contingency) is defined by location on the stressor (x) axis, and represents a unifying concept in biological science.     

The analysis of dependence in cross-classifications having ordered categories, using log-linear models for frequencies and log-linear models for odds

To analyse the dependence of a qualitative (dichotomous or polytomous) response variable upon one or more qualitative explanatory variables, log-linear models for frequencies are compared with log-linear models for odds, when the categories of the response variable are ordered and the categories of each explanatory variable may be either ordered or unordered. The log-linear models for odds express the odds (or log odds) pertaining to adjacent response categories in terms of appropriate multiplicative (or additive) factors. These models include the 'null log-odds model', the 'uniform log-odds model', the 'parallel log-odds model', and other log-linear models for the odds. With these models, the dependence of the response variable (with ordered categories) can be analyzed in a manner analogous to the usual multiple regression analysis and related analysis of variance and analysis of covariance. Application of log-linear models for the odds sheds light on earlier applications of log-linear models for the frequencies in contingency tables with ordered categories.     

On the use of the classical tests for detecting linkage.

Some drawbacks of the classical Mather's linkage text XL2 are considered, and the simple contingency analysis is suggested as an alternative method. The former test is conditional on Mendelian segregation at both loci, whereas the simple contingency test is not. Furthermore, the contingency test and the test for Mendelian segregation at each locus are orthogonal when performed using the G statistic. Simulation results show that, when the XL2 is used, the actual type I error probability (alpha a) can be dramatically perturbed. As expected, no alpha a perturbation is observed when the G contingency test is used. On the other hand, when segregation is Mendelian at both loci, the power of the XL2 method is larger than that of the contingency G test when sample size is small and strong marginal distortion is observed. Because strong marginal distortion may suggest that segregation may be non-Mendelian, the XL2 is in general discouraged in favor of the simple contingency analysis.     

Considering of certain relevant information in the analysis of contingency tables. A three-step procedure

A heuristic three-step procedure for analysing multidimensional contingency tables is given to meet the requirements of a mixed analysis from both hypotheses-ruled and data-ruled type. The first-step provides the structure of relationships among the attributes by fitting an appropriate unsaturated log-linear model to the data of the given contingency table. Restriction to elementary hierarchical models allows to get them by combining pairs of conditional independence. The result of the first step may be regarded as a certain validisation of real model ideas. In the second step the significant pairs of conditional dependence are analysed in regard to the levels of the condition complex. Only such significant pairs are to be considered, in general, where the condition complex does not include the response variable. The third-step may test special subtests in that significant two-dimensional tables found in step two or may extend the general statements by partitioning, the corresponding test statistics in additive components. Application examples demonstrate the general line of action.     

Determination analysis as a tool to study relationships between ecosystem components in water bodies of the Azov, Caspian, and Kara Sea basins

Contingencies of different components of freshwater biocenoses in the Don, Volga, Angara, and Northern Azov basins where studied by determination analysis. A positive contingency between the biomass of total phytoplankton or its divisions and the biomass of zooplankton was demonstrated. The most significant contingencies were revealed for zooplankton and green algae, dinoflagellates, and cyanobacteria. As against zooplankton, the biomass of zoobenthos features much less contingencies with the biomass of microalgae as well as zooplankton, since detritus, higher plant tissues, and small animals are the basis of the diet of benthic organisms. Differences in the contingency pattern between different geographical regions and time periods within the same basin have been revealed using the context procedure in determination analysis.     

Judgments of causal efficacy under constant and changing interevent contingencies

How do people judge constant and varying interevent contingencies? In two experiments, 150 college students rated the efficacy of a potential cause (an experimental fertilizer) of an effect (a plant's blooming). The prevailing probabilistic interevent relation could remain constant for the entirety of the problem or it could change without warning at the midway point: by contingency reversal, by shifting from noncontingency to contingency, or by shifting from contingency to noncontingency. Participants’ trial-by-trial ratings sensitively tracked the prevailing positive, negative, and noncontingent interevent relations, even those that entailed an unsignaled change in contingency. Changes in specific cells of the 2 × 2 contingency table differentially affected participants’ response to the altered interevent relations. All of this evidence was well described by an associative account of contingency and causal judgments.     

Odds and Ends: Risk,Mortality, and the Politics of Contingency

Medical anthropology's cogentrethinking of conventional biomedicalcategories has largely overlooked the coreproblems of one key concept of both biomedicaland social scientific analysis: risk. Inparticular, the use of the term in medicalanthropology (and the social sciences moregenerally) frequently rests on two assumptions:(1) that contingency necessarily constitutes athreat to individual experience or socialorder; and (2) that a risk management paradigmthat relies on a model of statisticalprobability is the ontologically preeminent wayof engaging chance. Other approaches which donot take risk as the starting point forunderstanding contingency also have problems;they too assume that contingency is necessarilycause for crisis. These problematic rootassumptions lead social analysts to miss howindividual actors and local communitiesvariously engage, rather than minimize,contingency. I suggest a new approach thatinstead aims to treat contingency asnormatively neutral and as arising in fourdomains of experience. Conventional approachesalso miss how attempts to account forunexpected events themselves involve strugglesbetween competing paradigms (or tropes) ofchance. This contest over accountability I callhere the politics of contingency, and Iseek thereby to signal the need to renovate ourlanguage of uncertainty in order to address itspolitical dimensions. I trace the literature toidentify some sources of these terminologicalproblems, and through an examination of thelife and death of a close contact in Chania,Crete, I explore his own approach to chance andthe different, competing interpretations of hisdeath. I thereby demonstrate the importance ofrevamping the conventional approach tounderstanding the contingent nature of humanlife.     

The critical dimensions of the response-reinforcer contingency

Two major dimensions of any contingency of reinforcement are the temporal relation between a response and its reinforcer, and the relative frequency of the reinforcer given the response versus when the response has not occurred. Previous data demonstrate that time, per se, is not sufficient to explain the effects of delay-of-reinforcement procedures; needed in addition is some account of the events occurring in the delay interval. Moreover, the effects of the same absolute time values vary greatly across situations, such that any notion of a standard delay-of-reinforcement gradient is simplistic. The effects of reinforcers occurring in the absence of a response depend critically upon the stimulus conditions paired with those reinforcers, in much the same manner as has been shown with Pavlovian contingency effects. However, it is unclear whether the underlying basis of such effects is response competition or changes in the calculus of causation.     

突发公共卫生事件与综合性医院重症监护超负荷扩容

医院是社会应对突发公共卫生事件的主要机构。在超出正常负荷的情况下,合理有序地进行基础卫生设施扩容是保证医院成功应对突发事件的关键。医院必须能够通过增加重症监护单元（Intensive Care Unit, ICU）容量或通过改造其他区域增加实际ICU收治能力;有次序地将相关区域改造为临时重症监护单元;储备充足的病床和相关监护设施,在应对偶发事件时必须能得到政府协助以获取额外的呼吸机;制定ICU阶段性扩容人员工作计划,保证在应对偶发事件或危机时重症监护的仍可有效执行;抽调临床专业人员参与应急管理组,共同制定和执行扩容计划;为重症监护活动提供充足的基础设施支持。     

Impact of Genetic Background on Allele Selection in a Highly Mutable Candida albicans Gene,PNG2

In many microbes rapid mutation of highly mutable contingency genes continually replenishes a pool of variant alleles from which the most suitable are selected, assisting in rapid adaptation and evasion of the immune response. In some contingency genes mutability is achieved through DNA repeats within the coding region. The fungal human pathogen Candida albicans has 2600 repeat-containing ORFs. For those investigated (ALS genes, HYR1, HYR2, CEK1, RLM1) many protein variants with differing amino acid repeat regions exist, as expected for contingency genes. However, specific alleles dominate in different clades, which is unexpected if allele variation is used for short-term adaptation. Generation of new alleles of repeat-containing C. albicans ORFs has never been observed directly. Here we present evidence for restrictions on the emergence of new alleles in a highly mutable C. albicans repeat-containing ORF, PNG2, encoding a putative secreted or cell surface glycoamidase. In laboratory cultures new PNG2 alleles arose at a rate of 2.8×10⁻⁵ (confidence interval 3.3×10⁻⁶−9. 9×10⁻⁵) per cell per division, comparable to rates measured for contingency genes. Among 80 clinical isolates 17 alleles of different length and 23 allele combinations were distinguishable; sequence differences between repeat regions of identical size suggest the existence of 36 protein variants. Specific allele combinations predominated in different genetic backgrounds, as defined by DNA fingerprinting and multilocus sequence typing. Given the PNG2 mutation rate, this is unexpected, unless in different genetic backgrounds selection favors different alleles. Specific alleles or allele combinations were not preferentially associated with C. albicans isolates from particular body sites or geographical regions. Our results suggest that the mutability of PNG2 is not used for short-term adaptation or evasion of the immune system. Nevertheless the large number of alleles observed indicates that mutability of PNG2 may assist C. albicans strains from different genetic backgrounds optimize their interaction with the host in the long term.     

Configural Frequency Analysis (CFA) Revisited — a New Look at an Old Approach

Configural frequency analysis (CFA) is a widely used method for the identification of types and syndromes in contingency tables. However, the type model of CFA shows some major deficiencies. In this paper, we propose an alternative modeling of types eliminating the shortcomings of CFA. Basically, a type is modeled as a combination of traits or symptoms that deviates from the pattern of association holding true for the complementary configurations of the contingency table. The new approach is formulated in terms of a log-linear model. It is shown that parameter estimation can be performed with methods known from the analysis of incomplete contingency tables. Test procedures for confirmatory analysis and methods for exploratory search for type configurations are developed. We illustrate the methodology with two practical examples.     

Learned helplessness: effects of response requirement and interval between treatment and testing

Three experiments investigated learned helplessness in rats manipulating response requirements, shock duration, and intervals between treatment and testing. In Experiment 1, rats previously exposed to uncontrollable or no shocks were tested under one of four different contingencies of negative reinforcement: FR 1 or FR 2 escape contingency for running, and FR1 escape contingency for jumping (differing for the maximum shock duration of 10s or 30s). The results showed that the uncontrollable shocks produced a clear operant learning deficit (learned helplessness effect) only when the animals were tested under the jumping FR 1 escape contingency with 10-s max shock duration. Experiment 2 isolated of the effects of uncontrollability from shock exposure per se and showed that the escape deficit observed using the FR 1 escape jumping response (10-s shock duration) was produced by the uncontrollability of shock. Experiment 3 showed that using the FR 1 jumping escape contingency in the test, the learned helplessness effect was observed one, 14 or 28 days after treatment. These results suggest that running may not be an appropriate test for learned helplessness, and that many diverging results found in the literature might be accounted for by the confounding effects of respondent and operant contingencies present when running is required of rats.