scholarly journals Multivariate Characterization of Hydrogen Balmer Emission in Cataclysmic Variables

2006 ◽  
Vol 23 (3) ◽  
pp. 106-118 ◽  
Author(s):  
Gordon E. Sarty ◽  
Kinwah Wu
Keyword(s):  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Cataclysmic Variables ◽  
Data Set ◽  
Analysis Methods ◽  
Wide Range ◽  
Components Analysis

AbstractThe ratios of hydrogen Balmer emission line intensities in cataclysmic variables are signatures of the physical processes that produce them. To quantify those signatures relative to classifications of cataclysmic variable types, we applied the multivariate statistical analysis methods of principal components analysis and discriminant function analysis to the spectroscopic emission data set of Williams (1983). The two analysis methods reveal two different sources of variation in the ratios of the emission lines. The source of variation seen in the principal components analysis was shown to be correlated with the binary orbital period. The source of variation seen in the discriminant function analysis was shown to be correlated with the equivalent width of the Hβ line. Comparison of the data scatterplot with scatterplots of theoretical models shows that Balmer line emission from T CrB systems is consistent with the photoionization of a surrounding nebula. Otherwise, models that we considered do not reproduce the wide range of Balmer decrements, including ‘inverted’ decrements, seen in the data.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

scholarly journals Hybridization in Umbridae in the Hudson River, New York, with Designation of Neotypes for Umbra limi and Umbra pygmaea

Zootaxa ◽  
2006 ◽  
Vol 1113 (1) ◽  
pp. 1 ◽  
Author(s):  
ROBERT E. SCHMIDT ◽  
ROBERT A. DANIELS
Keyword(s):  
New York ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Tidal Marsh ◽  
Parental Species ◽  
Function Analysis ◽  
Hudson River ◽  
Natural Hybrid ◽  
Components Analysis

We document the occurrence of a natural hybrid between the Eastern Mudminnow, Umbra pygmaea (DeKay 1842) and the Central Mudminnow, U. limi (Kirtland 1840). Hybrid individuals were collected in a supratidal pool in a fresh-tidal marsh in the Hudson River, New York. ANOVA, ANCOVA, principal components analysis, and discriminant function analysis of meristics and morphometrics showed that the hybrids were distinguishable from the parental species and were generally intermediate between them. The tidal Hudson River is the only place these species are sympatric, and hybridization must have occurred within the last several decades. We designate neotypes for Umbra pygmaea and Umbra limi.

Acoustic Differentiation of Laryngeal, Esophageal, and Tracheoesophageal Speech

1984 ◽  
Vol 27 (4) ◽  
pp. 577-585 ◽  
Author(s):  
Joanne Robbins
Keyword(s):  
Speech Production ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Group Membership ◽  
Function Analysis ◽  
Relative Importance ◽  
Vocal Rehabilitation ◽  
Tracheoesophageal Speech ◽  
Components Analysis ◽  
Temporal Measures

This investigation was designed to determine if a multivariate acoustic classifier could effectively discriminate group membership for 15 tracheoesophageal, esophageal, and laryngeal speakers. Seven intensity, 10 frequency, and 13 duration measures were quantified from recorded voice samples. Using principal components analysis, a subset of the 13 least redundant acoustic and temporal measures was systematically selected from the 30 original measures and analyzed singly and jointly in terms of its ability to discriminate among the three speaker groups. Discriminant function analysis revealed perfect categorization of the 45 subjects, indicating that the three methods of speech production are acoustically and temporally distinct from one another. The relative importance of the selected variables which, in combination, significantly differentiated the three groups is discussed in relation to physiologic differences among groups and clinical application for postlaryngectomy vocal rehabilitation.

Beyond principal components: a critical comparison of factor analysis methods for subspace modelling in chemistry

2021 ◽  
Vol 13 (37) ◽  
pp. 4188-4219
Author(s):  
Peter D. Wentzell ◽  
Cannon Giglio ◽  
Mohsen Kompany-Zareh
Keyword(s):  
Analytical Chemistry ◽  
Factor Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Critical Comparison ◽  
Analysis Methods ◽  
Analysis Tools ◽  
Components Analysis

Principal components analysis (PCA) is widely used in analytical chemistry, but is only one type of broader range of factor analysis tools that are described in this article.

scholarly journals Dietary patterns in UK adolescents obtained from a dual-source FFQ and their associations with socio-economic position, nutrient intake and modes of eating

2013 ◽  
Vol 17 (7) ◽  
pp. 1476-1485 ◽  
Author(s):  
Kate Northstone ◽  
Andrew DAC Smith ◽  
Victoria L Cribb ◽  
Pauline M Emmett
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Dietary Patterns ◽  
Data Set ◽  
Sociodemographic Differences ◽  
Traditional Health ◽  
Parents And Children ◽  
Components Analysis ◽  
Positive Correlations ◽  
Dietary Data

AbstractObjectiveTo derive dietary patterns using principal components analysis from separate FFQ completed by mothers and their teenagers and to assess associations with nutrient intakes and sociodemographic variables.DesignTwo distinct FFQ were completed by 13-year-olds and their mothers, with some overlap in the foods covered. A combined data set was obtained.SettingAvon Longitudinal Study of Parents and Children (ALSPAC), Bristol, UK.SubjectsTeenagers (n 5334) with adequate dietary data.ResultsFour patterns were obtained using principal components analysis: a ‘Traditional/health-conscious’ pattern, a ‘Processed’ pattern, a ‘Snacks/sugared drinks’ pattern and a ‘Vegetarian’ pattern. The ‘Traditional/health-conscious’ pattern was the most nutrient-rich, having high positive correlations with many nutrients. The ‘Processed’ and ‘Snacks/sugared drinks’ patterns showed little association with important nutrients but were positively associated with energy, fats and sugars. There were clear gender and sociodemographic differences across the patterns. Lower scores were seen on the ‘Traditional/health conscious’ and ‘Vegetarian’ patterns in males and in those with younger and less educated mothers. Higher scores were seen on the ‘Traditional/health-conscious’ and ‘Vegetarian’ patterns in girls and in those whose mothers had higher levels of education.ConclusionsIt is important to establish healthy eating patterns by the teenage years. However, this is a time when it is difficult to accurately establish dietary intake from a single source, since teenagers consume increasing amounts of foods outside the home. Further dietary pattern studies should focus on teenagers and the source of dietary data collection merits consideration.

The use of principal components analysis for the investigation of an organic air pollutants data set

1984 ◽  
Vol 18 (11) ◽  
pp. 2471-2478 ◽  
Author(s):  
J. Smeyers-Verbeke ◽  
J.C. Den Hartog ◽  
W.H. Dehker ◽  
D. Coomans ◽  
L. Buydens ◽  
...  
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Air Pollutants ◽  
Data Set ◽  
Components Analysis

scholarly journals Pathologies of Between-Groups Principal Components Analysis in Geometric Morphometrics

2019 ◽  
Author(s):  
Fred L. Bookstein
Keyword(s):  
Geometric Morphometrics ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Null Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Wide Range ◽  
Pls Analysis ◽  
Components Analysis ◽  
First Time

AbstractGood empirical applications of geometric morphometrics (GMM) typically involve several times more variables than specimens, a situation the statistician refers to as “highp/n,” wherepis the count of variables andnthe count of specimens. This note calls your attention to two predictable catastrophic failures of one particular multivariate statistical technique, between-groups principal components analysis (bgPCA), in this high-p/nsetting. The more obvious pathology is this: when applied to the patternless (null) model ofpidentically distributed Gaussians over groups of the same size, both bgPCA and its algebraic equivalent, partial least squares (PLS) analysis against group, necessarily generate the appearance of huge equilateral group separations that are actually fictitious (absent from the statistical model). When specimen counts by group vary greatly or when any group includes fewer than about ten specimens, an even worse failure of the technique obtains: the smaller the group, the more likely a bgPCA is to fictitiously identify that group as the end-member of one of its derived axes. For these two reasons, when used in GMM and other high-p/nsettings the bgPCA method very often leads to invalid or insecure bioscientific inferences. This paper demonstrates and quantifies these and other pathological outcomes both for patternless models and for models with one or two valid factors, then offers suggestions for how GMM practitioners should protect themselves against the consequences for inference of these lamentably predictable misrepresentations. The bgPCA method should never be used unskeptically — it is never authoritative — and whenever it appears in partial support of any biological inference it must be accompanied by a wide range of diagnostic plots and other challenges, many of which are presented here for the first time.

Planktonic Biomass Trajectories In Lake Ecosystems

1983 ◽  
Vol 40 (10) ◽  
pp. 1752-1760 ◽  
Author(s):  
Michael A. Gates ◽  
Ann P. Zimmerman ◽  
W. Gary Sprules ◽  
Roy Knoechel
Keyword(s):  
Hypothesis Testing ◽  
Principal Components Analysis ◽  
Biomass Allocation ◽  
Principal Components ◽  
Primary Component ◽  
Size Category ◽  
Data Set ◽  
Lake Ecosystems ◽  
The Mean ◽  
Components Analysis

We introduce a method, based on principal components analysis, for studying temporal changes in biomass allocation among 16 size–category compartments of lake plankton. Applied to data from a series of 12 Ontario lakes over three sampling seasons, the technique provides a simple means of visualizing shifts in patterns of biomass allocation, and it allows comparative analyses of biomass fluctuations in different lakes. Each of the primary component axes is interpretable. Furthermore, a large proportion of the variance in both the mean position of a lake and its movement along these axes is interpreted as a function of lake physicochemistry. The analysis also provides weighted scores for use in hypothesis testing which are an improvement over mean biomass values alone, because they take into account the structure of variation in the data set.

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