Amphibian Distribution with Respect to Pond Water Chemistry near Sudbury, Ontario

1992 ◽  
Vol 49 (S1) ◽  
pp. 114-121 ◽  
Author(s):  
V. Glooschenko ◽  
W. F. Weller ◽  
P. G. R. Smith ◽  
R. Alvo ◽  
J. H. G. Archbold
Keyword(s):  
Discriminant Analysis ◽  
Water Chemistry ◽  
Principal Components ◽  
Rana Sylvatica ◽  
Pond Water ◽  
Bufo Americanus ◽  
Breeding Sites ◽  
Metal Levels ◽  
Components Analysis ◽  
Detailed Chemical

Amphibians were present in 118 potential breeding sites 9–66 km northeast and southwest of Sudbury, Ontario. Detailed chemical analyses were done for 38 ponds, and 23 variables were subjected to principal components analysis to summarize the main gradients in pond chemistry. Discriminant analysis using scores of the first three principal components showed that the presence of Rana pipiens, R. clamitans and Hyla crucifer was positively related to buffering status (alkalinity, pH, and other correlated variables); the presence of H. crucifer was also negatively related to atmospheric deposition status (cadmium, nickel, other correlated metals, and sulphate). Discriminant analysis using the original water chemistry variables confirms these general patterns. Two species show relationships with buffering status variables: Rana sylvatica with conductivity and R. clamitans with alkalinity. Three species also show negative correlation with metal levels in pond water: Bufo americanus with nickel, R. clamitans with aluminum, and R. pipiens with zinc. Although most of the species expected do occur in the Sudbury area, the distributions of several species appear related to buffering status and metals present in their immediate environemnt. There were only two observations of Ambystoma maculatum, and low numbers of egg masses were noted for R. sylvatica.

Quantitative Phenotyping and Discriminant Analysis Improve Scoring Classification in Late B-Lymphoproliferative Disorders.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1463-1463
Author(s):  
Georges Jung ◽  
Sylvie Thiebault ◽  
Jean-Claude Eisenmann ◽  
Eckart Wunder ◽  
Marie Haas ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Scoring System ◽  
Low Grade ◽  
Surface Markers ◽  
Correct Classification ◽  
Chi Square ◽  
Components Analysis

Abstract Multivariate analysis classification of chronic lymphocytic leukemia (CLL) and lymphoma (non-CLL) disorders is investigated in 299 patients by an extended panel of surface markers, and compared with Matutes classical scoring proposal. Diagnosis was based on clinical features, cell morphology, node or bone marrow histology, and immunological scoring system. Results are obtained on directly labeled tumoral cells by flow cytometry gating. Patients included 154 CLL, 2 Richter transformation, and 143 lymphoma (26 follicular, 49 lymphocytic, 18 other low-grade, 7 Waldenström macroglobulinemia, 13 mantel, 11 diffuse large-cell, 6 Burkitt, 4 marginal zone-cell, 5 hairy-cell leukemia, 2 MALT, 1 prolymphocytic leukemia, 1 SLVL). For CD43, FMC7, CD23, CD5, CD79b (% stained cells) and CD20, CD22 surface antigen intensities Chi-Square values indicate very high probability of correct classification (varing from 621 to 94.9; p<0.0000). If, alternatively, % of CD22, CD20, CD19 and intensities of CD79b, CD5, CD19, CD43, CD23 and kappa/lamba chains are employed, Chi-Square yields values of lower significance (varing from 65 to 0.1; p<0.0000 to 0.6573). Using classical panel scoring with CD79b, 82.4 % of patients were correctly classified, compared to 84.5% after replacing CD79b by CD22 intensity. If CD43 is added, correct classification increased to 89.6% and 88.1% of patients, respectively; this improvement is due to better allocation of CLL. In discriminant analysis 91.3% of patients are correctly classified with the panel including CD79b, and 90.9% with CD22 intensity. CD43 enhances the allocation of either one to 94.3%. Using our previous discriminant analysis with CD79b (Jung G, et al. Br J Haematol.2003; 120:496–499), this blind analysis correctly classified the population in 87.1%, compared to 91.3% with the new one. By adding CD43, it moved from 92.4% up to 94.3%. In order to find the optimal combination of the selected best markers, a stepwise probit discrimination was performed. Using CD43 and FMC7 yields a correct classification of 90.3%; after addition of CD5, CD79b, CD23, and CD22 intensity, efficiency increased to 94.6%. Further added markers don’t improve classification. Efficiency of this panel was further confirmed by hierarchical cluster and principal components analysis. Cluster analysis with squared Euclidian distances separated CLL from non-CLL patients with low overlaps: 86.6% of cases are correctly identified. Separated points in the plot representing patients with CLL and non-CLL, obtained by principal components analysis of surface markers, confirm the high predictive potential of this panel. The same analysis of surface marker positions for non-CLL suggests use of: % of CD79b, FMC7, and CD22 intensity, and for CLL: % of CD5, CD23, CD43. So, the addition of CD43 improves as well the discriminant function as the scoring system. Our selected panel of best markers is useful in distinguishing CLL from non-CLL and offers a better distinction by discriminant analysis. Furthermore quantitative expression of each marker and its predictive value improve diagnosis and classification.

The Fitness Facility Membership Questionnaire: A Measure of Reasons for Joining

1997 ◽  
Vol 85 (3) ◽  
pp. 907-916 ◽  
Author(s):  
Jan L. Drummond ◽  
Heather S. Lenes
Keyword(s):  
Discriminant Analysis ◽  
Intrinsic Motivation ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Extrinsic Motivation ◽  
Varimax Rotation ◽  
Community Based ◽  
Recreational Facilities ◽  
Fitness Facility ◽  
Components Analysis

This study describes the development of the Fitness Facility Membership Questionnaire of 43 items, designed to identify reasons for joining a fitness facility. Items were generated from responses to an open-ended questionnaire. A 50-item version of the questionnaire was completed by 152 members of five community-based fitness facilities. Principal components analysis with varimax rotation yielded 8 factors, accounting for 63.8% of the variance. The factors were labeled Socialization, Aquatic-related Facilities, Extrinsic Motivation, Recreational Facilities, Intrinsic Motivation, Resistance Equipment, Aerobic Equipment, and Amenities. The internal consistency of the eight factors was acceptable with Cronbach coefficients alpha ranging from .72 to .89. Discriminant analysis of responses is also presented.

A Comparative Analysis of Influences on Choosing a Music Teaching Occupation

2019 ◽  
Vol 67 (3) ◽  
pp. 286-303 ◽  
Author(s):  
David A. Rickels ◽  
Edward C. Hoffman ◽  
William E. Fredrickson
Keyword(s):  
Discriminant Analysis ◽  
Principal Components ◽  
Music Teachers ◽  
Music Students ◽  
Music Teaching ◽  
College Population ◽  
School Music ◽  
Orthogonal Components ◽  
Components Analysis ◽  
High School Music

The purpose of this study was to compare motivations and influences of high school music students who express an interest in a career in music teaching and those who do not. A previous survey was adapted for the study and administered to a pre-college population that included musicians who stated a preference to pursue music teaching or other music careers and musicians who stated a preference for other nonmusic occupations. Items were analyzed using a discriminant analysis of principal components (DAPC) data reduction strategy, which used a principal components analysis as a first step to determine item loadings onto orthogonal components. The three occupation groups (music teaching, other music, and other nonmusic) were then compared using a discriminant analysis of the resulting components. The model correctly classified 69.8% of cases, with one significant component primarily separating both music groups from the nonmusic career group and three additional significant components separating the music teaching group from the other music careers group. Using this model, the choice of an occupation appears multidimensional, and discrete sources of influence (e.g., music teachers and music teaching experiences explored in prior studies) are not necessarily individually predictive of the overall choice to pursue music teaching.

scholarly journals Εφαρμογές πολυπαραμετρικών στατιστικών τεχνικών στη χημική ανάλυση

2012 ◽  
Author(s):  
Ελένη Φαρμάκη
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Classification Trees ◽  
Components Analysis ◽  
Artificial Neural

Σε αυτή τη διατριβή μελετήθηκε η εφαρμογή πολυπαραμετρικών τεχνικών σε μεγάλες βάσεις δεδομένων ταξινόμησης, με σκοπό τη θεωρητική τους παρουσίαση, τη σύγκριση αυτών και την εξαγωγή συμπερασμάτων, σχετικά με το πεδίο εφαρμογής τους και το χειρισμό τους, τις δυνατότητες και τους περιορισμούς τους. Χρησιμοποιήθηκαν μη επιβλεπόμενες τεχνικές όπως Principal Components Analysis/Factor Analysis (PCA/FA) και Cluster Analysis (CA) αλλά και επιβλεπόμενες όπως Discriminant Analysis (DA), Classification Trees (CT) και Artificial Neural Networks (ANN). Ιδιαίτερη έμφαση δόθηκε στις τεχνικές CT και ANN (μελετήθηκαν τρεις μέθοδοι και αρχιτεκτονικές αντίστοιχα για καθεμιά από αυτές). Ερευνήθηκαν τα πλεονεκτήματα, μειονεκτήματα και ιδιαιτερότητες τους και βελτιστοποιήθηκαν τα μοντέλα ταξινόμησης των τεχνικών. Όλες οι τεχνικές συγκρίθηκαν μεταξύ τους, με κριτήριο τα αποτελέσματα τους (της ορθής ταξινόμησης των δειγμάτων) σε τρεις βάσεις δεδομένων οι οποίες αφορούσαν τους προσδιορισμούς α) μετάλλων-μεταλλοειδών στους τρεις ταμιευτήρες που χρησιμοποιούνται για την ύδρευση της πρωτεύουσας (Υλίκη, Μόρνο και Μαραθώνα), β) μετάλλων-μεταλλοειδών και ανόργανων στοιχείων σε θαλάσσια δείγματα ιζημάτων από μεγάλες ιχθυοκαλλιέργειες της χώρας, γ) σπανίων γαιών σε δείγματα ελαιολάδων από διάφορες περιοχές. Η DA αν και είναι παραμετρική τεχνική με πολλούς περιορισμούς στην εφαρμογή της, ανταποκρίθηκε στις ανάγκες των προβλημάτων και παρείχε πάντα μια πρώτη άποψη για το πρόβλημα (δυνατότητα ή όχι γραμμικού διαχωρισμού των ομάδων με βάση το Canonical plot της ανάλυσης και αρχική αξιολόγηση των μεταβλητών). Τα ποσοστά ορθής ταξινόμησης που παρείχε ήταν αρκετές φορές συγκρίσιμα με των πιο προηγμένων τεχνικών. Τα CT με 3 διαφορετικές μεθόδους και αρκετή ευελιξία (παρείχαν πολλές παραμέτρους προς δοκιμή και βελτιστοποίηση), επέτυχαν υψηλά ποσοστά ταξινόμησης με λίγες ή πολλές μεταβλητές (περισσότερες συνήθως των ANN), κατασκευάζοντας επαναλήψιμα μοντέλα με δυνατότητες γενίκευσης. Τα ANN αποδείχθηκαν ιδιαίτερα ευέλικτη τεχνική, με δυνατότητες αποτελεσματικής αξιολόγησης των μεταβλητών και εφαρμογής τους σε απλές αλλά και πολυπλοκότερες βάσεις προσεγγίζοντας γραμμικές και μη γραμμικές συναρτήσεις. Κατασκευάστηκαν ανθεκτικά και ευέλικτα μοντέλα. Μειονέκτημά τους αποτέλεσαν ωστόσο, τα φαινόμενα υπερπροσαρμογής που παρουσιάζουν και χρειάστηκαν προσεκτικοί χειρισμοί για την αποφυγή τους. Έτσι, τα διαθέσιμα δείγματα διαχωρίστηκαν σε τρεις ομάδες: χρησιμοποιήθηκαν εκτός της συνήθους ομάδας εκπαίδευσης, επιπλέον ομάδες επικύρωσης και ελέγχου. Με τον τρόπο αυτό, έγινε άμεση ταυτοποίηση των φαινομένων υπερπροσαρμογής (ώστε να διακόπτεται αυτόματα η εκπαίδευση του μοντέλου), αλλά και δοκιμή των μοντέλων σε νέα, “’άγνωστα” δείγματα, ώστε να ελέγχεται η δυνατότητα γενίκευσης αυτών. Ο διαχωρισμός σε ομάδες έγινε είτε τυχαία (όπως επιτάσσει η σύγχρονη βιβλιογραφία), είτε με βάση της προκατεργασίας με DA (μέθοδος που δεν έχει χρησιμοποιηθεί ποτέ στο παρελθόν). Επιπλέον, έγινε προσπάθεια εφαρμογής όσο το δυνατόν απλούστερων δομών με λίγες παραμέτρους (μεταβλητές, βάρη) αλλά και λειτουργικές μονάδες επεξεργασίας (νευρώνες).

scholarly journals Intra- and interspecific discrimination of Scorpaena species from the Aegean, Black, Mediterranean and Marmara seas

2021 ◽  
Vol 85 (3) ◽  
pp. 197-209
Author(s):  
Serdar Yedier ◽  
Derya Bostanci
Keyword(s):  
Discriminant Analysis ◽  
Principal Components Analysis ◽  
Analysis Of Variance ◽  
Principal Components ◽  
Canonical Discriminant Analysis ◽  
Species Level ◽  
Morphometric Traits ◽  
Morphometric Measurements ◽  
Morphometric Characters ◽  
Components Analysis

This study was conducted to discriminate five Scorpaena species and populations of each species according to morphometric characters. A total of 1865 fish specimens were collected from the eight locations in the four Turkish seas: Antalya, Balıkesir, Çanakkale, Hatay, İzmir, Marmara Ereğlisi, Ordu and Şile. In the study, 26 morphometric traits were measured for intra- and interspecific discrimination of five Scorpaena species. The data were subjected to analysis of variance, principal components analysis (PCA) and canonical discriminant analysis. As results of the PCA, 10 traits for S. maderensis and S. scrofa, 12 traits for S. elongata and 13 traits for S. notata and S. porcus were found to be important for intraspcific discrimination. The overall classification scores of intraspecific discrimination were determined as 94.6% for S. elongata, 90.5% for S. maderensis, 96.7% for S. notata, 96.5% for S. porcus and 92.2% for S. scrofa. The PCA indicated that 13 morphometric measurements among the 26 traits are important in the interspecific discrimination of five Scorpaena species. The cross-validated canonical discriminant analysis was correctly classified as 97.4% at the Scorpaena species level. The discrimination of correctly classified species ranged from 94.8% to 100%. Finally, we demonstrated that the morphometric characters examined in the present study can be used successfully in the intra- and interspecific discrimination of Scorpaena species from different habitats.

Determination of Gasoline Adulteration by Principal Components Analysis-Linear Discriminant Analysis Applied to FTIR Spectra

2006 ◽  
Vol 20 (3) ◽  
pp. 1097-1102 ◽  
Author(s):  
Rita C. C. Pereira ◽  
Vinicius L. Skrobot ◽  
Eustáquio V. R. Castro ◽  
Isabel C. P. Fortes ◽  
Vânya M. D. Pasa
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Ftir Spectra ◽  
Linear Discriminant ◽  
Components Analysis
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