Principal components analysis of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans in sediments from Lake Champlain and Lake George, New York, USA

1994 ◽  
Vol 27 (3) ◽  
Author(s):  
P. O'Keefe ◽  
R. Smith ◽  
S. Connor ◽  
K. Aldous ◽  
H. Valente ◽  
...  
Keyword(s):  
New York ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Polychlorinated Dibenzofurans ◽  
Lake Champlain ◽  
Components Analysis ◽  
Lake George

scholarly journals Is principal components analysis necessary to characterise dietary behaviour in studies of diet and disease?

2001 ◽  
Vol 4 (4) ◽  
pp. 903-908 ◽  
Author(s):  
Susan E McCann ◽  
John Weiner ◽  
Saxon Graham ◽  
Jo L Freudenheim
Keyword(s):  
New York ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Dietary Patterns ◽  
Fruits And Vegetables ◽  
Disease Status ◽  
Dietary Behaviour ◽  
Food Groups ◽  
Processed Meats ◽  
Components Analysis

AbstractObjective:To assess the relative ability of principal components analysis (PCA)-derived dietary patterns to correctly identify cases and controls compared with other methods of characterising food intake.Subjects:Participants in this study were 232 endometrial cancer cases and 639 controls from the Western New York Diet Study, 1986–1991, frequency-matched to cases on age and county of residence.Design:Usual intake in the year preceding interview of 190 foods and beverages was collected during a personal interview using a detailed food-frequency questionnaire. Principal components analysis identified two major dietary patterns which we labelled ‘healthy’ and ‘high fat’. Classification on disease status was assessed with separate discriminant analyses (DAs) for four different characterisation schemes: stepwise DA of 168 food items to identify the subset of foods that best discriminated between cases and controls; foods associated with each PCA-derived dietary pattern; fruits and vegetables (47 items); and stepwise DA of USDA-defined food groups (fresh fruit, canned/frozen fruit, raw vegetables, cooked vegetables, red meat, poultry, fish and seafood, processed meats, snacks and sweets, grain products, dairy, and fats).Results:In general, classification of disease status was somewhat better among cases (54.7% to 67.7%) than controls (54.0% to 63.1%). Correct classification was highest for fruits and vegetables (67.7% and 62.9%, respectively) but comparable to that of the other schemes (49.5% to 66.8%).Conclusions:Our results suggest that the use of principal components analysis to characterise dietary behaviour may not provide substantial advantages over more commonly used, less sophisticated methods of characterising diet.

scholarly journals Analysis of patterns of food intake in nutritional epidemiology: food classification in principal components analysis and the subsequent impact on estimates for endometrial cancer

2001 ◽  
Vol 4 (5) ◽  
pp. 989-997 ◽  
Author(s):  
Susan E McCann ◽  
James R Marshall ◽  
John R Brasure ◽  
Saxon Graham ◽  
Jo L Freudenheim
Keyword(s):  
New York ◽  
Endometrial Cancer ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Dietary Patterns ◽  
Disease Risk ◽  
Nutritional Epidemiology ◽  
Food Groups ◽  
Diet Study ◽  
Components Analysis

AbstractObjective:To assess the effect of different methods of classifying food use on principal components analysis (PCA)-derived dietary patterns, and the subsequent impact on estimation of cancer risk associated with the different patterns.Methods:Dietary data were obtained from 232 endometrial cancer cases and 639 controls (Western New York Diet Study) using a 190-item semi-quantitative food-frequency questionnaire. Dietary patterns were generated using PCA and three methods of classifying food use: 168 single foods and beverages; 56 detailed food groups, foods and beverages; and 36 less-detailed groups and single food items.Results:Classification method affected neither the number nor character of the patterns identified. However, total variance explained in food use increased as the detail included in the PCA decreased (~8%, 168 items to ~17%, 36 items). Conversely, reduced detail in PCA tended to attenuate the odds ratio (OR) associated with the healthy patterns (OR 0.55, 95% confidence interval (CI) 0.35–0.84 and OR 0.77, 95% CI 0.49–1.20, 168 and 36 items, respectively) but not the high-fat patterns (OR 0.95, 95% CI 0.57–1.58 and OR 0.85, 0.51–1.40, 168 and 36 items, respectively).Conclusions:Greater detail in food-use information may be desirable in determination of dietary patterns for more precise estimates of disease risk.

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.

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.

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