944 resultados para LOG-LINEAR MODELS


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Includes index.

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Capybaras were monitored weekly from 1998 to 2006 by counting individuals in three anthropogenic environments (mixed agricultural fields, forest and open areas) of southeastern Brazil in order to examine the possible influence of environmental variables (temperature, humidity, wind speed, precipitation and global radiation) on the detectability of this species. There was consistent seasonality in the number of capybaras in the study area, with a specific seasonal pattern in each area. Log-linear models were fitted to the sample counts of adult capybaras separately for each sampled area, with an allowance for monthly effects, time trends and the effects of environmental variables. Log-linear models containing effects for the months of the year and a quartic time trend were highly significant. The effects of environmental variables on sample counts were different in each type of environment. As environmental variables affect capybara detectability, they should be considered in future species survey/monitoring programs.

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Aims: The heterogeneity of the Brazilian population renders the extrapolation of pharmacogenomic data derived from well-defined ethnic groups inappropriate. We investigated the influence of self-reported `race/color`, geographical origin and genetic ancestry on the distribution of four VKORC1 SNPs and haplotypes in Brazilians. Comparative data were obtained from two major ancestral roots of Brazilians: Portuguese and Africans from former Portuguese colonies. Materials & methods: A total of 1037 healthy adults Brazilians, recruited at four different geographical regions and self identified as white, brown or black (race/color categories), 89 Portuguese and 216 Africans from Angola and Mozambique were genotyped for the VKORC1 3673G>A (rs9923231), 5808T>G (rs2884737), 6853G>C (rs8050894) and 9041G>A (rs7294) polymorphisms using TaqMan (R) (Applied Biosystems, CA, USA) assays. VKORC1 haplotypes were statistically inferred using the haplo.stats software. We inferred the statistical association between the distribution of the VKORC1 polymorphisms among Brazilians and self-reported color, geographical region and genetic ancestry by fitting multinomial log linear models via neural networks. Individual proportions of European and African ancestry were used to assess the impact of genetic admixture on the frequency distribution of VKORC1 polymorphisms among Brazilians, and for the comparison of Brazilians with Portuguese and Africans. Results: The frequency distribution of the 3673G>A and 5808T>G polymorphisms, and VKORC1 haplotypes among Brazilians varies across geographical regions, within self-reported color categories and according to the individual proportions of European and African genetic ancestry. Notably, the frequency of the warfarin sensitive VKORC1 3673A allele and the distribution of VKORC1 haplotypes varied continuously as the individual proportion of European ancestry increased in the entire cohort, independently of race/color categorization and geographical origin. Brazilians with more than 80% African ancestry differ significantly from Angolans and Mozambicans in frequency of the 3673G>A, 5808T>G and 6853G>C polymorphisms and haplotype distribution, whereas no such differences are observed between Brazilians with more than 90% European ancestry and Portuguese individuals. Conclusion: The diversity of the Brazilian population, evident in the distribution of VKORC1 polymorphisms, must be taken into account in the design of pharmacogenetic clinical trials and dealt with as a continuous variable. Warfarin dosing algorithms that include `race` terms defined for other populations are clearly not applicable to the heterogeneous and extensively admixed Brazilian population.

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A joint distribution of two discrete random variables with finite support can be displayed as a two way table of probabilities adding to one. Assume that this table hasn rows and m columns and all probabilities are non-null. This kind of table can beseen as an element in the simplex of n · m parts. In this context, the marginals areidentified as compositional amalgams, conditionals (rows or columns) as subcompositions. Also, simplicial perturbation appears as Bayes theorem. However, the Euclideanelements of the Aitchison geometry of the simplex can also be translated into the tableof probabilities: subspaces, orthogonal projections, distances.Two important questions are addressed: a) given a table of probabilities, which isthe nearest independent table to the initial one? b) which is the largest orthogonalprojection of a row onto a column? or, equivalently, which is the information in arow explained by a column, thus explaining the interaction? To answer these questionsthree orthogonal decompositions are presented: (1) by columns and a row-wise geometric marginal, (2) by rows and a columnwise geometric marginal, (3) by independenttwo-way tables and fully dependent tables representing row-column interaction. Animportant result is that the nearest independent table is the product of the two (rowand column)-wise geometric marginal tables. A corollary is that, in an independenttable, the geometric marginals conform with the traditional (arithmetic) marginals.These decompositions can be compared with standard log-linear models.Key words: balance, compositional data, simplex, Aitchison geometry, composition,orthonormal basis, arithmetic and geometric marginals, amalgam, dependence measure,contingency table

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Este trabalho baseia-se na análise de dados do desemprego em Cabo Verde nos anos de 2006 e 2008, usando informação da base de dados do INE e IEFP. Partindo da análise dos dados em estudo vai-se procurar descrever e perspectivar metodologias que contemplam as variáveis qualitativas e quantitativas com significado social positivo para a sociedade deste país. Após a introdução no capítulo 1, fez-se, no capítulo 2, a análise exploratória dos dados do desemprego em Cabo Verde referente aos anos 2006 e 2008. No capítulo 3 estudam-se associações entre variáveis, usando a metodologia de tabelas contingência, através da realização de testes de independência e testes de homogeneidade, e análise de medidas de associação. As variáveis usadas, vão ser essencialmente, o escalão etário, o género e o ano. O capítulo 4 é dedicado ao estudo de modelos Log - lineares em tabela de contingência, finalizando-se o trabalho com a apresentação das principais conclusões.

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A joint distribution of two discrete random variables with finite support can be displayed as a two way table of probabilities adding to one. Assume that this table has n rows and m columns and all probabilities are non-null. This kind of table can be seen as an element in the simplex of n · m parts. In this context, the marginals are identified as compositional amalgams, conditionals (rows or columns) as subcompositions. Also, simplicial perturbation appears as Bayes theorem. However, the Euclidean elements of the Aitchison geometry of the simplex can also be translated into the table of probabilities: subspaces, orthogonal projections, distances. Two important questions are addressed: a) given a table of probabilities, which is the nearest independent table to the initial one? b) which is the largest orthogonal projection of a row onto a column? or, equivalently, which is the information in a row explained by a column, thus explaining the interaction? To answer these questions three orthogonal decompositions are presented: (1) by columns and a row-wise geometric marginal, (2) by rows and a columnwise geometric marginal, (3) by independent two-way tables and fully dependent tables representing row-column interaction. An important result is that the nearest independent table is the product of the two (row and column)-wise geometric marginal tables. A corollary is that, in an independent table, the geometric marginals conform with the traditional (arithmetic) marginals. These decompositions can be compared with standard log-linear models. Key words: balance, compositional data, simplex, Aitchison geometry, composition, orthonormal basis, arithmetic and geometric marginals, amalgam, dependence measure, contingency table

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We review some issues related to the implications of different missing data mechanisms on statistical inference for contingency tables and consider simulation studies to compare the results obtained under such models to those where the units with missing data are disregarded. We confirm that although, in general, analyses under the correct missing at random and missing completely at random models are more efficient even for small sample sizes, there are exceptions where they may not improve the results obtained by ignoring the partially classified data. We show that under the missing not at random (MNAR) model, estimates on the boundary of the parameter space as well as lack of identifiability of the parameters of saturated models may be associated with undesirable asymptotic properties of maximum likelihood estimators and likelihood ratio tests; even in standard cases the bias of the estimators may be low only for very large samples. We also show that the probability of a boundary solution obtained under the correct MNAR model may be large even for large samples and that, consequently, we may not always conclude that a MNAR model is misspecified because the estimate is on the boundary of the parameter space.

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The impact of biogeographical ancestry, self-reported 'race/color' and geographical origin on the frequency distribution of 10 CYP2C functional polymorphisms (CYP2C8*2, *3, *4, CYP2C9*2, *3, *5, *11, CYP2C19*2, *3 and *17) and their haplotypes was assessed in a representative cohort of the Brazilian population (n = 1034). TaqMan assays were used for allele discrimination at each CYP2C locus investigated. Individual proportions of European, African and Amerindian biogeographical ancestry were estimated using a panel of insertion-deletion polymorphisms. Multinomial log-linear models were applied to infer the statistical association between the CYP2C alleles and haplotypes (response variables), and biogeographical ancestry, self-reported Color and geographical origin (explanatory variables). The results showed that CYP2C19*3, CYP2C9*5 and CYP2C9*11 were rare alleles (<1%), the frequency of other variants ranged from 3.4% (CYP2C8*4) to 17.3% (CYP2C19*17). Two distinct haplotype blocks were identified: block 1 consists of three single nucleotide polymorphisms (SNPs) (CYP2C19*17, CYP2C19*2 and CYP2C9*2) and block 2 of six SNPs (CYP2C9*11, CYP2C9*3, CYP2C9*5, CYP2C8*2, CYP2C8*4 and CYP2C8*3). Diplotype analysis generated 41 haplotypes, of which eight had frequencies greater than 1% and together accounted for 96.4% of the overall genetic diversity. The distribution of CYP2C8 and CYP2C9 (but not CYP2C19) alleles, and of CYP2C haplotypes was significantly associated with self-reported Color and with the individual proportions of European and African genetic ancestry, irrespective of Color self-identification. The individual odds of having alleles CYP2C8*2, CYP2C8*3, CYP2C9*2 and CYP2C9*3, and haplotypes including these alleles, varied continuously as the proportion of European ancestry increased. Collectively, these data strongly suggest that the intrinsic heterogeneity of the Brazilian population must be acknowledged in the design and interpretation of pharmacogenomic studies of the CYP2C cluster in order to avoid spurious conclusions based on improper matching of study cohorts. This conclusion extends to other polymorphic pharmacogenes among Brazilians, and most likely to other admixed populations of the Americas. The Pharmacogenomics Journal (2012) 12, 267-276; doi: 10.1038/tpj.2010.89; published online 21 December 2010

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In this paper, we carry out robust modeling and influence diagnostics in Birnbaum-Saunders (BS) regression models. Specifically, we present some aspects related to BS and log-BS distributions and their generalizations from the Student-t distribution, and develop BS-t regression models, including maximum likelihood estimation based on the EM algorithm and diagnostic tools. In addition, we apply the obtained results to real data from insurance, which shows the uses of the proposed model. Copyright (c) 2011 John Wiley & Sons, Ltd.

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Nell’ambito della ricerca scientifica nel campo dello sport, la Performance Analysis si sta ritagliando un crescente spazio di interesse. Per Performance Analysis si intende l’analisi della prestazione agonistica sia dal punto di vista biomeccanico che dal punto di vista dell’analisi notazionale. In questa tesi è stata analizzata la prestazione agonistica nel tennistavolo attraverso lo strumento dell’analisi notazionale, partendo dallo studio degli indicatori di prestazione più importanti dal punto di vista tecnico-tattico e dalla loro selezione attraverso uno studio sull’attendibilità nella raccolta dati. L’attenzione è stata posta quindi su un aspetto tecnico originale, il collegamento spostamenti e colpi, ricordando che una buona tecnica di spostamento permette di muoversi rapidamente nella direzione della pallina per effettuare il colpo migliore. Infine, l’obbiettivo principale della tesi è stato quello di confrontare le tre categorie di atleti selezionate: alto livello mondiale maschile (M), alto livello junior europeo (J) ed alto livello mondiale femminile (F). La maggior parte delle azioni cominciano con un servizio corto al centro del tavolo, proseguono con una risposta in push (M) o in flik di rovescio (J). Il colpo che segue è principalmente il top spin di dritto dopo un passo pivot o un top di rovescio senza spostamento. Gli alteti M e J contrattaccano maggiormente con top c. top di dritto e le atlete F prediligono colpi meno spregiudicati, bloccando di rovescio e proseguendo con drive di rovescio. Attraverso lo studio della prestazione di atleti di categorie e generi diversi è possibile migliorare le scelte strategiche prima e durante gli incontri. Le analisi statistiche multivariate (modelli log-lineari) hanno permesso di validare con metodo scientifico sia le procedure già utilizzate in letteratura che quelle innovative messe a punto per la prima volta in occasione di questo studio.

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In environmental epidemiology, exposure X and health outcome Y vary in space and time. We present a method to diagnose the possible influence of unmeasured confounders U on the estimated effect of X on Y and to propose several approaches to robust estimation. The idea is to use space and time as proxy measures for the unmeasured factors U. We start with the time series case where X and Y are continuous variables at equally-spaced times and assume a linear model. We define matching estimator b(u)s that correspond to pairs of observations with specific lag u. Controlling for a smooth function of time, St, using a kernel estimator is roughly equivalent to estimating the association with a linear combination of the b(u)s with weights that involve two components: the assumptions about the smoothness of St and the normalized variogram of the X process. When an unmeasured confounder U exists, but the model otherwise correctly controls for measured confounders, the excess variation in b(u)s is evidence of confounding by U. We use the plot of b(u)s versus lag u, lagged-estimator-plot (LEP), to diagnose the influence of U on the effect of X on Y. We use appropriate linear combination of b(u)s or extrapolate to b(0) to obtain novel estimators that are more robust to the influence of smooth U. The methods are extended to time series log-linear models and to spatial analyses. The LEP plot gives us a direct view of the magnitude of the estimators for each lag u and provides evidence when models did not adequately describe the data.

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Permutation tests are useful for drawing inferences from imaging data because of their flexibility and ability to capture features of the brain that are difficult to capture parametrically. However, most implementations of permutation tests ignore important confounding covariates. To employ covariate control in a nonparametric setting we have developed a Markov chain Monte Carlo (MCMC) algorithm for conditional permutation testing using propensity scores. We present the first use of this methodology for imaging data. Our MCMC algorithm is an extension of algorithms developed to approximate exact conditional probabilities in contingency tables, logit, and log-linear models. An application of our non-parametric method to remove potential bias due to the observed covariates is presented.

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Question: How do tree species identity, microhabitat and water availability affect inter- and intra-specific interactions between juvenile and adult woody plants? Location: Continental Mediterranean forests in Alto Tajo Natural Park, Guadalajara, Spain. Methods: A total of 2066 juveniles and adults of four co-occurring tree species were mapped in 17 plots. The frequency of juveniles at different microhabitats and water availability levels was analysed using log-linear models. We used nearest-neighbour contingency table analysis of spatial segregation and J-functions to describe the spatial patterns. Results: We found a complex spatial pattern that varied according to species identity and microhabitat. Recruitment was more frequent in gaps for Quercus ilex, while the other three species recruited preferentially under shrubs or trees depending on the water availability level. Juveniles were not spatially associated to conspecific adults, experiencing segregation from them inmany cases. Spatial associations, both positive and negative, were more common at higher water availability levels. Conclusions: Our results do not agree with expectations from the stressgradient hypothesis, suggesting that positive interactions do not increase in importance with increasing aridity in the study ecosystem. Regeneration patterns are species-specific and depend on microhabitat characteristics and dispersal strategies. In general, juveniles do not look for conspecific adult protection. This work contributes to the understanding of species co-existence, proving the importance of considering a multispecies approach at several plots to overcome limitations of simple pair-wise comparisons in a limited number of sites.

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Lately, several researchers have pointed out that climate change is expected to increase temperatures and lower rainfall in Mediterranean regions, simultaneously increasing the intensity of extreme rainfall events. These changes could have consequences regarding rainfall regime, erosion, sediment transport and water quality, soil management, and new designs in diversion ditches. Climate change is expected to result in increasingly unpredictable and variable rainfall, in amount and timing, changing seasonal patterns and increasing the frequency of extreme weather events. Consequently, the evolution of frequency and intensity of drought periods is of most important as in agro-ecosystems many processes will be affected by them. Realising the complex and important consequences of an increasing frequency of extreme droughts at the Ebro River basin, our aim is to study the evolution of drought events at this site statistically, with emphasis on the occurrence and intensity of them. For this purpose, fourteen meteorological stations were selected based on the length of the rainfall series and the climatic classification to obtain a representative untreated dataset from the river basin. Daily rainfall series from 1957 to 2002 were obtained from each meteorological station and no-rain period frequency as the consecutive numbers of days were extracted. Based on this data, we study changes in the probability distribution in several sub-periods. Moreover we used the Standardized Precipitation Index (SPI) for identification of drought events in a year scale and then we use this index to fit log-linear models to the contingency tables between the SPI index and the sub-periods, this adjusted is carried out with the help of ANOVA inference.