Response surface model diagnosis in two-way tables

Graphical model diagnosis procedures for two-way tables have already been proposed by Mandel, as well as by Bradu and Gabriel who used Gabriel's biplot as a main tool.

In this paper, a graphical model diagnosis extending Mandel's approach is developed. Response surface models are obtained whereby the response is expressed in terms of ‘truth-connected’ latent variates and, ultimately, in terms of originally measured external variates.

Meaningful models are obtained in some cases, accurate smoothing and interpolation algorithms in others.

As a by–product, Euclidean maps, which represent a twodimensional scaling (for rows or columns) also displaying ,ANOVA features, are obtained. To an extent, these maps can be viewed as a substitute for a model in the event of partial failure of the modelling operation.     

TWO-WAY CHANGE-POINT MODEL AND ITS APPLICATION

Exact null and alternative distributions of the two-way maximally selected x² for interaction between the ordered rows and columns are derived for each of the normal and Poisson models, respectively. The method is one of the multiple comparison procedures for ordered parameters and is useful for defining a block interaction or a two-way change-point model as a simple alternative to the two-way additive model. The construction of a confidence region for the two-way change-point is then described. An important application is found in a dose-response clinical trial with ordered categorical responses, where detecting the dose level which gives significantly higher responses than the lower doses can be formulated as a problem of detecting a change in the interaction effects.     

Markov chain Monte Carlo exact tests for incomplete two-way contingency tables

We consider testing the quasi-independence hypothesis for two-way contingency tables which contain some structural zero cells. For sparse contingency tables where the large sample approximation is not adequate, the Markov chain Monte Carlo exact tests are powerful tools. To construct a connected chain over the two-way contingency tables with fixed sufficient statistics and an arbitrary configuration of structural zero cells, an algebraic algorithm proposed by Diaconis and Sturmfels [Diaconis, P. and Sturmfels, B. (1998). The Annals of statistics, 26, pp. 363–397.] can be used. However, their algorithm does not seem to be a satisfactory answer, because the Markov basis produced by the algorithm often contains many redundant elements and is hard to interpret. We derive an explicit characterization of a minimal Markov basis, prove its uniqueness, and present an algorithm for obtaining the unique minimal basis. A computational example and the discussion on further basis reduction for the case of positive sufficient statistics are also given.     

Influence diagnostics for stratified ordinal contingency tables

Influence diagnostics are investigated in this study. In particular, an approach based on the generalized linear mixed model setting is presented for formulating ordered categorical counts in stratified contingency tables. Deletion diagnostics and their first-order approximations are developed for assessing the stratum-specific influence on parameter estimates in the models. To illustrate the proposed model diagnostic technique, the method is applied to analyze two sets of data: a clinical trial and a survey study. The two examples demonstrate that the presence of influential strata may substantially change the results in ordinal contingency table analysis.     

Monte carlo comparison of estimation methods for additive two-way tables

We considered the problem of estimating effects in the following linear model for data arranged in a two-way table: Response = Common effect + Row effect + Column effect + Residual. This work was occasioned by a project to analyse Federal Aviation Administration (FAA) data on daily temporal deviations from flight plans for commercial US flights, with rows and columns representing origin and destination airports, respectively. We conducted a large Monte Carlo study comparing the accuracy of three methods of estimation: classical least squares, median polish and least absolute deviations (LAD). The experiments included a wide spectrum of tables of different sizes and shapes, with different levels of non-linearity, noise variance, and percentages of empty cells and outliers. We based our comparison on the accuracy of the estimates and on computational speed. We identified factors that significantly affect accuracy and speed, and compared the methods based on their sensitivity to these factors. We concluded that there is no dominant method of estimation and identified conditions under which each method is most attractive.     

TESTING SUBHYPOTHESES AND ESTIMATING σ2 IN THE NONREPLICATED THREE-WAY MULTIPLICATIVE INTERACTION MODEL

ABSTRACT

Standard statistical techniques do not provide methods for analyzing data from nonreplicated factorial experiments. Such experiments occur for several reasons. Many experimenters may prefer conducting experiments having a large number of factor levels with no replications than conducting experiments with a few factor levels with replications particularly in pilot studies. Such experiments may allow one to identify factor combinations to be used in follow-up experiments. Another possibility is when the experimenter thinks that an experiment is replicated when in fact it is not. This occurs when a naive researcher believes that sub-samples are replicates when in reality they are not. Nonreplicated two-way experiments have been extensively studied. This paper discusses the analysis of nonreplicated three-way experiments. In particular, estimation of σ² is discussed and a test is derived for testing whether three-factor interaction is absent in sub-areas of three-way data using a nonreplicated three-way multiplicative interaction model with a single multiplicative term. Approximate null distribution of the derived test statistic is studied using Monte Carlo studies and results are illustrated through an example.     

Contingency tables with fuzzy information

ABSTRACT

This paper extends the classical methods of analysis of a two-way contingency table to the fuzzy environment for two cases: (1) when the available sample of observations is reported as imprecise data, and (2) the case in which we prefer to categorize the variables based on linguistic terms rather than as crisp quantities. For this purpose, the α-cuts approach is used to extend the usual concepts of the test statistic and p-value to the fuzzy test statistic and fuzzy p-value. In addition, some measures of association are extended to the fuzzy version in order to evaluate the dependence in such contingency tables. Some practical examples are provided to explain the applicability of the proposed methods in real-world problems.     

Bayesian clustering for row effects models

We deal with two-way contingency tables having ordered column categories. We use a row effects model wherein each interaction term is assumed to have a multiplicative form involving a row effect parameter and a fixed column score. We propose a methodology to cluster row effects in order to simplify the interaction structure and to enhance the interpretation of the model. Our method uses a product partition model with a suitable specification of the cohesion function, so that we can carry out our analysis on a collection of models of varying dimensions using a straightforward MCMC sampler. The methodology is illustrated with reference to simulated and real data sets.     

R×C ecological inference: bounds, correlations, flexibility and transparency of assumptions

Summary.  Despite its potential pitfalls, ecological inference is an unavoidable part of some quantitative settings, including US voting rights litigation. In such applications, the analyst will typically encounter two-way tables with more than two rows and columns. Although several ecological inference methods are currently available for 2×2 tables, there are fewer options for analysing general R × C tables, and virtually none that model counts as opposed to fractions. We propose a count R × C method that respects the bounds deterministically, that allows for complex relationships between internal cell quantities, that is easily extensible and that results from transparent assumptions. We study the method via simulation, and then apply it to an example that is drawn from the state of Texas relevant to recent redistricting litigation there.     

Improving Path Trimming in a Network Algorithm for Fisher's Exact Test in Two-way Contingency Tables

This article provides an improvement of the network algorithm for calculating the exact p value of the generalized Fisher's exact test in two-way contingency tables. We give a new exact upper bound and an approximate upper bound for the maximization problems encountered in the network algorithm. The approximate bound has some very desirable computational properties and the meaning is elucidated from a viewpoint of differential geometry. Our proposed procedure performs well regardless of the pattern of marginal totals of data.     

Nonparametric rank-based test procedures for non-additive models in the two-way layout I. no replications

One of the major unresolved problems in the area of nonparametric statistics is the need for satisfactory rank-based test procedures for non-additive models in the two-way layout, especially when there is only one observation on each combination of the levels of the experimental factors. In this paper we consider an arbitrary non-additive model for the two-way layout with n levels of each factor. We utilize both alignment and ranking of the data together with basic properties of Latin squares to develop rank tests for interaction (non-additivity). Our technique involves first aligning within one of the main effects, ranking within the other main effects (columns and rows) and then adding the resulting ranks within “interaction bands” corresponding to orthogonal partitions of the interaction for the model, as denoted by the letters of an n × n Latin square. A Friedman-type statistic is then computed on the resulting sums. This is repeated for each of (n?1) mutually orthogonal Latin squares (thus accounting for all the interaction degrees of freedom). The resulting (n?1) Friedman-type statistics are finally combined to obtain an overall test statistic. The necessary null distribution tables for applying the proposed test for non-additivity are presented and we discuss the results of a Monte Carlo simulation study of the relative powers of this new procedure and other (parametric and nonparametric) procedures designed to detect interaction in a two-way layout with one observation per cell.     

Conditional symmetry models for three-way contingency tables

We generalize the classical conditional or triangular symmetry model for I×I$I\times I$ contingency tables to three-way I×I×I$I\times I\times I$ tables with commensurable ordinal classification variables. The construction of the new family of models is such that the desirable property that connects conditional symmetry to complete symmetry and marginal homogeneity models in two-way tables is retained in three-way tables. Furthermore, connections between our proposed models obey a coherent structure. We provide maximum likelihood estimation for the new models which is illustrated with a real data example.     

On the decomposition of X2 for two-way contingency tables

When samples are taken independently from I populations and the subjects classified into J categories, can the Pearson's chisquare statistic X² testing the homogeneity model on the resulting I×J two-way table be decomposed into components familiar in the analysis of variance? Will the X² testing the homogeneity model on tables derived by collapsing columns in the spirit of orthogonal comparisons in factorial experiments be asymptotically independent? The answers to both questions are generally negative. This paper gives a theoretical justification.     

A method for testing interaction in unreplicated two-way tables: using all pairwise interaction contrasts

Several methods exist for testing interaction in unreplicated two-way layouts. Some are based on specifying a functional form for the interaction term and perform well provided that the functional form is appropriate. Other methods do not require such a functional form to be specified but only test for the presence of non-additivity and do not provide a suitable estimate of error variance for a non-additive model. This paper presents a method for testing for interaction in unreplicated two-way tables that is based on testing all pairwise interaction contrasts. This method (i) is easy to implement, (ii) does not assume a functional form for the interaction term, (iii) can find a sub-table of data which may be free from interaction and to base the estimate of unknown error variance, and (iv) can be used for incomplete two-way layouts. The proposed method is illustrated using examples and its power is investigated via simulation studies. Simulation results show that the proposed method is competitive with existing methods for testing for interaction in unreplicated two-way layouts.     

Comparison of dependent two-way contingency tables

Comparison of two-way contingency tables using measures of association is considered. Multiple comparison procedures for dependent tables are proposed, enabling us to compare tables that are faces from larger multl-dimensional tables. An example

is given to Illustrate the analysis of two 2 × 2-tables formed

from a 2⁴-table.     

SIMULATIONS AND DERIVED APPROXIMATIONS FOR THE MEANS AND STANDARD DEVIATIONS OF THE CHARACTERISTIC ROOTS OF A WISHART MATRIX

Monte Carlo simulations were done to estimate the means and standard deviations of the characteristic roots of a Wishart matrix which can be used in computing tests of hypotheses concerning multiplicative terms in balanced linear-bilinear (multiplicative) models for an m × n table of data. In this report we extend the previous results (Mandel, 1971; Cornelius, 1980) to r ≤ 199, c ≤ 149 or r ≤ 149, c ≤ 199, where r and c are row and column degrees of freedom, respectively, of the two-way array of residuals (with total degrees of freedom rc) after fitting the linear effects. For 187 combinations of r and c at intervals over this domain, we used 5000 simulations to estimate expectations and standard deviations of the Wishart roots. Using weighted linear regression variable selection techniques, symmetric functions of r and c were obtained for approximating the simulated means and standard deviations. Use of these approximating functions will avoid the need for reference to tables for input to computer programs which require these values for tests of significance of sequentially fitted terms in the analyses of balanced linear-bilinear models.     

An analysis of the effects of atmospheric sulphate and ammonium on soil solution chemistry using median polishing

Soils from locations near Amsterdam, Dublin, Exeter (SW England), Göttingen, Paris and in the Grizedale Forest (NW England) were placed in lysimeters in a Norway spruce (Picea abies) stand in the Grizedale Forest and the soil solutions, rainfall and throughfall were monitored for 54 weeks. Two-way data tables for SO4, NH4, NO3, Al, Mg, Ca and pH were analysed by median polishing, in which each observed data value is decomposed into a typical value plus a row effect plus a column effect plus a residual. The row effects suggested that SO4 was the main acidifying influence in the Göttingen soil, but in the other soils SO4 had less effect than did NO3. The column effects showed little variation over the 54 weeks. Low values for NO3 in March-May and September were associated with periods of low or zero rainfall. The residuals contained from 27% (Al) to 59% (NO3) of the variation. Some were interpretable and contained useful information. Where the residuals are considered to represent uninterpretable random variation (‘noise’) they can be subtracted from the original data to give corrected data for other types of analysis. In median polishing, changing a few values in the input data, even drastically, will result in unusual residuals only in the cells with altered values. Hence, it is a useful technique for screening two-way data tables obtained from field experiments.     

TESTING THE SIGNIFICANCE OF GENOTYPE-ENVIRONMENT INTERACTION

The AMMI (additive main effects-multiplicative interaction) model is often used to investigate interactions in two-way tables, in particular for genotype-environment interactions. Both Gollob (1968) and Mandel (1969, 1971) proposed methods for testing the significance of such interactions. These methods are compared using simulated data. Our results support Mandel's conclusions, but his method is conservative and we recommend a test proposed by Johnson & Graybill (1972).