scholarly journals Switching principal component analysis for modeling means and covariance changes over time.

2014 ◽  
Vol 19 (1) ◽  
pp. 113-132 ◽  
Author(s):  
Kim De Roover ◽  
Marieke E. Timmerman ◽  
Ilse Van Diest ◽  
Patrick Onghena ◽  
Eva Ceulemans
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Changes Over Time ◽  
Over Time

scholarly journals Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data

2020 ◽  
Author(s):  
Torfinn S. Madssen ◽  
Guro F. Giskeødegård ◽  
Age K. Smilde ◽  
Johan A. Westerhuis
Keyword(s):  
Clinical Trials ◽  
Repeated Measures ◽  
Intervention Studies ◽  
Linear Models ◽  
Principal Component ◽  
Component Analysis ◽  
Outcome Data ◽  
Biological Data ◽  
Changes Over Time ◽  
Over Time

AbstractLongitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of “omics”-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate outcome data. Analysis of such data must take both the longitudinal intervention structure and multivariate nature of the data into account. The ASCA+-framework combines general linear models with principal component analysis, and can be used to separate and visualize the multivariate effect of different experimental factors. However, this methodology has not yet been developed for the more complex designs often found in longitudinal intervention studies, which may be unbalanced, involve randomized interventions, and have substantial missing data. Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed. This approach can facilitate easier interpretation of data from longitudinal clinical trials with multivariate outcomes.Author summaryClinical trials are increasingly generating large amounts of complex biological data. Examples can include measuring metabolism or gene expression in tissue or blood sampled repeatedly over the course of a treatment. In such cases, one might wish to compare changes in not one, but hundreds, or thousands of variables simultaneously. In order to effectively analyze such data, both the study design and the multivariate nature of the data should be considered during data analysis. ANOVA simultaneous component analysis+ (ASCA+) is a statistical method which combines general linear models with principal component analysis, and provides a way to separate and visualize the effects of different factors on complex biological data. In this work, we describe how repeated measures linear mixed models, a class of models commonly used when analyzing changes over time and treatment effects in longitudinal studies, can be used together with ASCA+ for analyzing clinical trials in a novel method called repeated measures-ASCA+ (RM-ASCA+).

A Functional PCA Model for the Study of Time Series of Pressure Maps

2013 ◽  
Vol 29 (2) ◽  
pp. 135-140 ◽  
Author(s):  
Juan C. Chicote ◽  
Juan V. Durá ◽  
Juan M. Belda ◽  
Rakel Poveda
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Sampling Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Regression ◽  
Pressure Distributions ◽  
Functional Pca ◽  
Over Time

Principal component analysis and functional regression are combined in a model to analyze a time series of pressure maps. The model is tested measuring the pressures over a chair seat while a subject performs a combination of simple movements. A sampling rate of 3 Hz is adequate for applying the model in sitting postures. The model is able to detect patterns of movement over time, although more variables are necessary if the movements produce similar pressure distributions.

ALL FOR ONE … ONE FOR ALL? A PRINCIPAL COMPONENT ANALYSIS OF LATIN AMERICAN BRADY BOND DEBT FROM 1994 TO 2000

2002 ◽  
Vol 05 (01) ◽  
pp. 79-106 ◽  
Author(s):  
KEVIN PAUL SCHERER ◽  
MARCO AVELLANEDA
Keyword(s):  
Principal Component Analysis ◽  
Latin American ◽  
Principal Component ◽  
Component Analysis ◽  
Volatility Risk ◽  
Characteristic Roots ◽  
Changes Over Time ◽  
Empirical Covariance ◽  
Variance Explained ◽  
Observation Period

We use Principal Component Analysis (PCA) to study the Brady Bond Debt of the four primary Latin American sovereign issuers: Argentina, Brazil, Mexico, and Venezuela. Our dataset covers a period of 5½ years starting in July 1994 and consists of daily sovereign ("stripped") yield levels for the par and discount debt securities of each country. We examine the behavior of the characteristic roots and eigenvectors of the empirical covariance matrices computed sequentially over different periods. We show that, by and large, there exist two statistically significant components, or factors, which explain up to 90% of the realized variance. The eigenvector with largest eigenvalue corresponds to the variance attributable to "regional" ("Latin") risk. The second component strongly suggests the existence of a volatility risk factor associated to Venezuelan debt in relation to the rest of the region. A time-dependent factor analyis reveals that the importance of the variance explained by the factor changes over time and that this variation can be interpreted to some extent in terms of market events. In particular, we investigate the relation between the evolution of the PCA factors with the market dislocations that ocurred during the observation period, including the so-called Tequila effect, Asian flu, Ruble devaluation, and Real devaluation.

Press Characterization Recalibration by Principal Component Analysis and DEBP Model

2012 ◽  
Vol 200 ◽  
pp. 670-675
Author(s):  
Ting Chen ◽  
Wei Li ◽  
Dong Bai Zhao ◽  
Ting Zhang
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Low Cost ◽  
Back Propagation ◽  
Principal Component ◽  
Color Difference ◽  
Component Analysis ◽  
Training Samples ◽  
Hidden Layer ◽  
Over Time

Artificial neural networks (ANN) combined with PCA are widely being used. This study addresses the problem of updating a CMYK printer characterization in response to systematic changes（printing material, press）in device characteristics with PCA-DEBP model. In this study, training samples of test chart, which were normalized through principal component analysis (PCA), were applied as inputs to a differential evolution back propagation (DEBP) neural network with one hidden layer. This DEBP model has been used to predict the present printing characterization using the last ICC profile by measurement with high convergence speed. Experiment results show that the predicted printing characterizations compare with that by the measurement has little color difference. So a PCA-DEBP model can be used to exactly recalibrate the ICC profile over time with low cost.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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