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Mixed Modelling (mixed + modelling)
Selected AbstractsA Mixed Modelling Approach for Randomized Experiments with Repeated MeasuresJOURNAL OF AGRONOMY AND CROP SCIENCE, Issue 4 2004H. P. Piepho Abstract Repeated measurements on the same experimental unit are common in plant research. Due to lack of randomization and the serial ordering of observations on the same unit, such data give rise to correlations, which need to be accounted for in statistical analysis. Mixed modelling provides a flexible framework for this task. The present paper proposes a general method to formulate mixed models for designed experiments with repeated measurements. The approach is exemplified by way of several examples. [source] Inferential non-centred principal curve analysis of time-intensity curves in sensory analysis: the methodology and its application to beer astringency evaluationJOURNAL OF CHEMOMETRICS, Issue 5-6 2007Nancy François Abstract Improving technologies and better understanding of sensory phenomena have lead sensory analysts to develop statistical methods to assess sensations that endure over time (e.g. the bitterness or astringency of a beer) dynamically. The data produced by this type of experiment is classically a time-intensity (TI) curve, and their analysis remains an active research topic. The classical approach, widely used in this context, starts by extracting some significant parameters from the initial curves (maximum intensity, area under the curve (AUC), etc.). Descriptive data analysis or statistical modelling is then applied to get information from these summary parameters. This paper presents a different method, called inferential non-centred principal curve analysis (INCPCA), for the analysis of TI curves. It combines multivariate analysis (to visualise the curves in a space of smaller dimensions) with statistical modelling (aimed at enhancing the significance of factor effects). Non-centred principal curves (NCPCs) are first extracted from the curves matrix. They decompose the TI curves into different interpretable components. Score plots are used to represent the projection of the initial curves in the space of the first principal curves and allow factors and judge effects to be visualised. Mixed modelling is then applied to test the significance of these effects using PCA scores as model responses. The classical and INCPCA methods are illustrated on a TI experiment exploring the relation between beer astringency and three factors of interest: pH, O2 content and aging. Eight beers arranged in a 23 factorial design were tested in triplicate by eight trained judges. Copyright © 2007 John Wiley & Sons, Ltd. [source] Density effects on life-history traits in a wild population of the great tit Parus major: analyses of long-term data with GIS techniquesJOURNAL OF ANIMAL ECOLOGY, Issue 2 2006TEDDY A. WILKIN Summary 1Population density often has strong effects on the population dynamics and reproductive processes of territorial animals. However, most estimates of density-dependent effects use the number of breeding pairs per unit area in a given season and look for correlations across seasons, a technique that assigns the same density score to each breeding pair, irrespective of local spatial variation. 2In this study, we employed GIS techniques to estimate individual breeding densities for great tits breeding in Wytham Woods UK, between 1965 and 1996. We then used linear mixed modelling to analyse the effect of density on reproductive processes. 3The areas of Thiessen polygons formed around occupied nestboxes were used to approximate territory size (necessarily inverse of breeding density). There were significant, independent and positive relationships between clutch size, fledging mass and the number of offspring recruited to the population, and territory size (all P < 0·001), but no effect of territory size on lay-date or egg mass. 4Thiessen polygons are contiguous and cover all of the available area. Therefore, at low nest densities territory polygons were excessively oversized. Using a novel procedure to address this limitation, territory sizes were systematically capped through a range of maxima, with the greatest effect in the models when territories were capped at 0·9,2·3 ha. This figure approximates to the maximum effective territory size in our population and is in close agreement with several field-based studies. This capping refinement also revealed a significant negative relationship between lay-date and territory size capped at 0·9 ha (P < 0·001). 5These density-dependent effects were also detected when analyses were restricted to changes within individual females, suggesting that density effects do not merely result from either increased proportions of low-quality individuals, or increased occupation of poor sites, when population density is high. 6Overall, these results suggest that, in the current population, great tits with territories smaller than c. 2 ha independently lay smaller and later clutches, have lighter fledglings, and recruit fewer offspring to the breeding population. These analyses thus suggest a pervasive and causal role of local population density in explaining individual reproductive processes. [source] Habitat models of bird species' distribution: an aid to the management of coastal grazing marshesJOURNAL OF APPLIED ECOLOGY, Issue 5 2000T. P. Milsom 1.,Coastal grazing marshes comprise an important habitat for wetland biota but are threatened by agricultural intensification and conversion to arable farmland. In Britain, the Environmentally Sensitive Area (ESA) scheme addresses these problems by providing financial incentives to farmers to retain their grazing marshes, and to follow conservation management prescriptions. 2.,A modelling approach was used to aid the development of management prescriptions for ground-nesting birds in the North Kent Marshes ESA. This ESA contains the largest area of coastal grazing marsh remaining in England and Wales (c. 6500 ha) and supports nationally important breeding populations of lapwing Vanellus vanellus and redshank Tringa totanus. 3.,Counts of ground-nesting birds, and assessments of sward structure, surface topography and wetness, landscape structure and sources of human disturbance were made in 1995 and again in 1996, on 19 land-holdings with a combined area of c. 3000 ha. The land-holdings varied from nature reserves at one extreme to an intensive dairy farm at the other. 4.,Models of relationship between the presence or absence of ground-nesting birds and the grazing marsh habitat in each of c. 430 marshes were constructed using a generalized linear mixed modelling (GLMM) method. This is an extension to the conventional logistic regression approach, in which a random term is used to model differences in the proportion of marshes occupied on different land-holdings. 5.,The combined species models predicted that the probability of marshes being occupied by at least one ground-nesting species increased concomitantly with the complexity of the grass sward and surface topography but decreased in the presence of hedgerows, roads and power lines. 6.,Models were also prepared for each of the 10 most widespread species, including lapwing and redshank. Their composition differed between species. Variables describing the sward were included in models for five species: heterogeneity of sward height tended to be more important than mean sward height. Surface topography and wetness were important for waders and wildfowl but not for other species. Effects of boundaries, proximity to roads and power lines were included in some models and were negative in all cases. 7.,Binomial GLMMs are useful for investigating habitat factors that affect the distribution of birds at two nested spatial scales, in this case fields (marshes) grouped within farms. Models of the type presented in this paper provide a framework for targeting of conservation management prescriptions for ground-nesting birds at the field scale on the North Kent Marshes ESA and on lowland wet grassland elsewhere in Europe. [source] | ||||||||||