Primjena tetrahoričkog i polihoričkog koeficijenta korelacijeu verifikaciji prognoza

The measure of association in 2 x 2 (K x K) contingency tables known as tetrachoric (polychoric) correlation coefficient is recalled. These measures rely on two assumptions: 1) there exist continuous latent variables underlying the contingency table and 2) joint distribution of corresponding standard normal deviates is bivariate normal. It is shown that, in practice, the tetrachoric (polychoric) correlation coefficient is an estimate of Pearson correlation coefficient between the latent variables. Consequently, these measures do not depend on bias nor on marginal frequencies of the table, which implies a natural and convenient partition of information (carried by the contingency table), between association, bias and probability of the event and subsequently enables the analysis of how other scores depend on bias and marginal frequencies. Results extended to K x K tables lead to eventual reduction in dimensionality from K2 to 2K. The theoretical findings are illustrated through analysis of real-life, 6 x 6 contingency tables on verification of quantitative precipitation forecasts.Tetrahorički (polihorički) koeficijent korelacije dobro je poznata mjera asocijacije u kontingencijskim tablicama veličine 2 x 2 (K x K). Ove mjere počivaju na dvjema pretpostavkama: 1) U pozadini kontingencijske tablice nalaze se neprekidne latentne varijable, te 2) zajednička funkcija distribucije pripadnih standardnih normalnih devijata je bivarijantna normalna razdioba. Pokazano je da tetrahorički, odnosno polihorički koeficijent korelacije predstavlja procjenu Pearsonovog koeficijenta korelacije izmedju latentnih varijabli. Posljedično, ove mjere ne ovise o pristranosti, kao ni o marginalnim čestinama, što rezultira rasčlambom informacije sadržane u kontingencijskoj tablici na tri dijela. Prvi se odnosi na povezanost, drugi na pristranost, a treći daje čestinu razmatrane pojave. Korištenjem dobivenog rastava analizirana je ovisnost drugih verifikacijskih mjera o pristranosti i o marginalnim čestinama. Rezultati su prirodno prošireni na tablice oblika K x K, pri čemu se dimenzija problema smanjuje s K2 na 2K. Teorija je primjenjena u analizi tablica veličine 6 x 6 koje opisuju kvantitativne prognoze oborine

ProbCD: enrichment analysis accounting for categorization uncertainty

As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test. We developed an open-source R package to deal with probabilistic categorical data analysis, ProbCD, that does not require a static contingency table. The contingency table for
the enrichment problem is built using the expectation of a Bernoulli Scheme stochastic process given the categorization probabilities. An on-line interface was created to allow usage by non-programmers and is available at: http://xerad.systemsbiology.net/ProbCD/. We present an analysis framework and software tools to address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis, ProbCD can accommodate: (i) the stochastic nature of the high-throughput experimental techniques and (ii) probabilistic gene annotation

Measuring reliability and consistency in contingency tables

The association between two categorical variables is very often assessed by making a cross-tabulation and calculating the x2 statistic for that table. However there are many other related parameters which can be used to assess subtle patterns in the table. In this article we will discuss parameters which can be fruitfully used in situations such as the test-retest method for the reliability of questions in a pilot questionnaire ; the measurement of the change of people’s attitude with time ; the comparison of two medical diagnoses of a given patient ; the prediction of heart disease status using an independent risk scale.peer-reviewe

Dietary diversity as a food security indicator

Household food security is an important measure of well-being. Although it may not encapsulate all dimensions of poverty, the inability of households to obtain access to enough food for an active, healthy life is surely an important component of their poverty. Accordingly, devising an appropriate measure of food security outcomes is useful in order to identify the food insecure, assess the severity of their food shortfall, characterize the nature of their insecurity (for example, seasonal versus chronic), predict who is most at risk of future hunger, monitor changes in circumstances, and assess the impact of interventions. However, obtaining detailed data on food security status—such as 24- hour recall data on caloric intakes—can be time consuming and expensive and require a high level of technical skill both in data collection and analysis. This paper examines whether an alternative indicator, dietary diversity, defined as the number of unique foods consumed over a given period of time, provides information on household food security. It draws on data from 10 countries (India, the Philippines, Mozambique, Mexico, Bangladesh, Egypt, Mali, Malawi, Ghana, and Kenya) that encompass both poor and middle-income countries, rural and urban sectors, data collected in different seasons, and data on calories acquisition obtained using two different methods. ....[D]ietary diversity would appear to show promise as a means of measuring food security and monitoring changes and impact, particularly when resources available for such measurement are scarce.Food security. ,Poverty. ,Caloric intake. ,India. ,Philippines. ,Mozambique. ,Mexico. ,Bangladesh. ,Egypt. ,Mali. ,Malawi. ,Ghana. ,Kenya. ,Diet Developing countries. ,