Search results for "Procrustes analysis"

CONSTRUCTING, BOOTSTRAPPING, AND COMPARING MORPHOMETRIC AND PHYLOGENETIC TREES: A CASE STUDY OF NEW WORLD MONKEYS (PLATYRRHINI, PRIMATES)

2005

Morphometric data sets are often phenetically analyzed by using various kinds of spatial, metric, or nonmetric multivariate analyses. Such methods produce results that are difficult to compare directly with molecular or morphological phylogenetic hypotheses, which are usually expressed by using nonspatial tree representations. Therefore, it is useful in a comparative approach to analyze, and above all to visualize, morphometric pairwise relationships as tree structures. For this purpose, several additive or ultrametric methods exist, which often return different topologies for the same data set. Objective criteria are thus needed to identify the tree-building algorithm (or algorithm family)…

Metric discrimination and distribution of the species of Crocidura occuring in Tunisia

1992

A recent paper on the occurrence of the genus Crocidura in Tunisia reports a single specimen identified as C. suaveolens. Therefore a third species, besides C. russula and C. whitakeri would occur in the country. However, the presence of C. suaveolens in North-Africa is controversial and was recently ruled out from the other Maghrebi countries (Algieria and Morocco). During the period 1989-90, 71 specimens of shrews were collected from owls pellets or trapped at Tunisian 12 sites. This material was measured and studied both by classic morphometric and multivariate methods (Fuzzy test, Principal Coordinate Analysis and Generalized Procrustes Analysis), considering also reference samples (C. …

Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items in a Preference Matrix

2020

In the framework of preference rankings, the interest can lie in clustering individuals or items in order to reduce the complexity of the preference space for an easier interpretation of collected data. The last years have seen a remarkable flowering of works about the use of decision tree for clustering preference vectors. As a matter of fact, decision trees are useful and intuitive, but they are very unstable: small perturbations bring big changes. This is the reason why it could be necessary to use more stable procedures in order to clustering ranking data. In this work, a Projection Clustering Unfolding (PCU) algorithm for preference data will be proposed in order to extract useful info…

Multiple factor analysis: principal component analysis for multitable and multiblock data sets

2013

3D geometric morphometric analysis of variation in the human lumbar spine

2019

[Objectives]: The shape of the human lumbar spine is considered to be a consequence of erect posture. In addition, several other factors such as sexual dimorphism and variation in genetic backgrounds also influence lumbar vertebral morphology. Here we use 3D geometric morphometrics (GM) to analyze the 3D morphology of the lumbar spine in different human populations, exploring those potential causes of variation.

Comparison of odour sensory profiles performed by two independent trained panels following the same descriptive analysis procedures

2000

Odour sensory profiling of 28 associations of cheese ripening micro-organisms was performed by two panels of 10 assessors on two different sites. Sample preparation, training protocols and references, tasting procedures and scoring were similar in the two laboratories. Panel 2 used 10 attributes and panel 1 used these terms plus 4 extra descriptors. Analysis of variance and multivariate methods (canonical variate analysis, generalised procrustes analysis and STATIS) exhibited differences between assessors within a panel and between panels concerning the use of the scoring scale and the strength of product discrimination by attribute. Panel 1 was more sensitive to fruity notes and panel 2 to…

A new approach to examine the relationships between sensory and gas chromatography-olfactometry data using generalized procrustes analysis applied to…

2003

Six French Chardonnay wines were submitted to both sensory and combined headspace/gas chromatography-olfactometry analyses. The detection frequencies allowed five hierarchical levels to be distinguished: P25, the odorant areas (OAs) having a detection frequencyor =25% (the complete olfactogram without the odor noise); P40,or =40%; P55,or =55%; P70,or =70%; and P85,or =85%. Moreover, the detection frequencies were analyzed to distinguish 21 discriminative OAs. Wines tested by sensory analysis and the headspace samples analyzed by gas chromatography-olfactometry (GC-O) were described by a heterogeneous vocabulary distributed into nine overall classes of descriptors. The new statistical treatm…

Quantification of cranial convergences in arvicolids (Rodentia)

1997

Cranial convergence in nine species of arvicolids is quantified phenetically using geometric morphometry. In a preliminary step, a hypothesis about phyletic relationships is proposed as a framework against which to examine morphological comparisons. The cranial morphology is then depicted in three series of 37, 20 and 13 landmarks characterizing the lower, upper and lateral sides of the skull respectively. Superimposition (Procrustes) methods are used to quantify shape differences and establish phenograms for the three sides of the skull. The phenogram obtained for lateral sides reveals a strong connection between skull profile and mode of life: surface dwelling forms have elongated skulls …

Sampling effort and information quality provided by rare and common species in estimating assemblage structure

2020

Made available in DSpace on 2020-12-12T01:06:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-03-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Academy of Finland Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Reliable biological assessments are essential to answer ecological and management questions but require well-designed studies and representative sample sizes. However, large sampling effort is rarely possible, because it demands large financial resources and time, restricting the number of sites sampled, the duration of the study and the sampling effort at each site. In…