Principal Component Analysis of Smoothed Tetrachoric Correlation Matrices as a Measure of Dimensionality

2012 ◽  
Vol 73 (1) ◽  
pp. 63-77 ◽  
Author(s):  
Rudolf Debelak ◽  
Ulrich S. Tran
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Tetrachoric Correlation ◽  
Correlation Matrices

scholarly journals Mathematical modeling - food price analysis

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Bingxian Leng ◽  
Yunfei Fu ◽  
Siyuan Li
Keyword(s):  
Principal Component Analysis ◽  
Clustering Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Food Prices ◽  
Food Price ◽  
Analysis Model ◽  
Correlation Matrices ◽  
Gray Prediction ◽  
Spss Software

This paper mainly uses the idea of pedigree clustering analysis, gray prediction and principal component analysis. The clustering analysis model, GM (1,1) model and principal component analysis model were established by using SPSS software to analyze the correlation matrices and principal component analysis. MATLAB software was used to calculate the correlation matrices. In January, The difference in price changes of major food prices in cities is calculated, and had forecasted the various food prices in June 2016. For the first issue, the main food is classified and the data are processed. After that, the SPSS software is used to classify the 27 kinds of food into four categories by using the pedigree cluster analysis model and the system clustering. The four categories are made by EXCEL. The price of food changes over time with a line chart that analyzes the characteristics of food price volatility. For the second issue, the gray prediction model is established based on the food classification of each kind of food price. First, the original data is cumulated, test and processed, so that the data have a strong regularity, and then establish a gray differential equation, and then use MATLAB software to solve the model. And then the residual test and post-check test, have C <0.35, the prediction accuracy is better. Finally, predict the price trend in June 2016 through the function. For the third issue, we analyzed the main components of 27 kinds of food types by celery, octopus, chicken (white striped chicken), duck and Chinese cabbage by using the data of principal given and analyzed by principal component analysis. It can be detected by measuring a small amount of food, this predict CPI value relatively accurate. Through the study of the characteristics of the region, select Shanghai and Shenyang, by looking for the relevant CPI and food price data, using spss software, principal component analysis, the impact of the CPI on several types of food, and then calculated by matlab algorithm weight, and then the data obtained by the analysis and comparison, different regions should be selected for different types of food for testing.

scholarly journals Indeks Sosio-ekonomi Menggunakan Principal Component Analysis

2006 ◽  
Vol 1 (2) ◽  
pp. 83
Author(s):  
Iwan Ariawan
Keyword(s):  
Principal Component Analysis ◽  
Economic Status ◽  
Principal Component ◽  
Household Survey ◽  
Component Analysis ◽  
Economic Index ◽  
Tetrachoric Correlation ◽  
Polychoric Correlation ◽  
Ordinal Variables ◽  
Economic Indices

In household survey, we could measure socio-economic status through income, expenditure and ownership of valuable goods. Measuring income and ex- penditure in developing countries has many weaknesses, therefore many researchers prefer to use the ownership of valuable goods as proxy of socio-eco- nomic status. Using ownership of valuable goods as proxy indicator creates another problem of having many variables for the socio-economic proxy. To show how to simplify many variables of ownership of valuable goods into 1 socio-economic index. Using prinicpal component analysis with Stata. Using Indonesia Demographic & Health Survey 2002-2003 data, 7 binomial variables of ownership of valuable goods and 3 ordinal variables of housing condition to construct socio-economic indices using principal component analysis (PCA), tetrachoric and polychoric correlation.We used Stata to construct the socio-economic in- dex. Correlation matrices were derived using tetrachoric command for tetrachoric correlation and polychoric command for polychoric correlation. Two socio- economic indices were constructed, 1 index was based only on 7 binomial variables of ownership of valuable goods and 1 index was based on 7 binomial variables of ownership of valuable goods and 3 ordinal variables of housing conditions. PCA was used to construct those 2 indices. In 7 variables model, the socio-economic index could explain 57% variance and in 10 variables model, the socio-economic index could explain 54% variance. We also showed the use of xtile command to regroup the subjects based on quintile of socio-economic indices. PCA, tetrachoric and polychoric correlation could be used to con- struct socio-economic indices based on information of ownership of valueable goods and housing conditions.Key words: Socio-economic indices, principal component analysis, tetrachoric correlation, polychoric correlation.Pada penelitian survei, kita dapat mengukur tingkat status sosio-ekonomi rumah tangga melalui pemasukan, pengeluaran dan kepemilikan barang-barang berharga. Penggunaan variabel pemasukan dan pengeluaran di negara berkembang memiliki banyak kelemahan, sehingga banyak peneliti lebih suka meng- gunakan variabel kepemilikan barang berharga untuk mengukur status sosio-ekpnomi. Namun, penggunaan variabel kepemilikan barang berharga menim- bulkan masalah lain, yaitu banyaknya variabel untuk mengukur status sosio-ekonomi. Tujuan penulisan ini adalah menyederhanakan banyak variabel kepemi- likan barang berharga menjadi 1 indeks sosio-ekonomi. Data yang digunakan adalah data Survei Demografi Kesehatan Indonesia 2002-2003 yang memili- ki 7 variabel binomial tentang kepemilikan barang berharga dan 3 variabel ordinal tentang keadaan rumah untuk membuat indeks sosio-ekonomi. Indeks diben- tuk dengan menggunakan principal component analysis (PCA), korelasi tetrakorik dan polikorik. Kami memperlihatkan bagaimana membuat indeks sosio- ekonomi dengan bantuan perangkat lunak Stata. Matriks korelasi tetrakorik dibentuk dengan perintah tetrachoric dan matriks korelasi polikorik dibentuk den- gan perintah polychoric. Dua indeks sosio-ekonomi dibentuk, 1 indeks berdasarkan 7 variabel binomial kepemilikan barang berharga dan 1 indeks lagi berdasarkan ke 7 variabel binomial tersebut ditambah 3 variabel ordinal kondisi rumah. Kedua indeks dibentuk dengan prosedur PCA. Pada model 7 vari- abel binomial, indeks yang terbentuk dapat menjelaskan 57% varians kepemilikan barang berharga dan pada model 7 variabel binomial ditambah 3 variabel ordinal, indeks dapat menjelaskan 54% varians kepemilikan barang berharga dan kondisi rumah. Kami juga memperlihatkan penggunaan perintah xtile un- tuk membagi subyek penelitian menurut kuintil indeks sosio-ekonomi. PCA, korrelasi tetrakorik dan polikorik dapat digunakan untuk membentuk indeks so- sio-ekonomi berdasarakan informasi tentang kepemilikan barang berharga dan kondisi rumah.Kata kunci: indeks sosio-ekonomi, principal component analysis, korelasi tetrakorik, korelasi polikorik.

The Foliar Composition of the Oil Palm in West Malaysia

1970 ◽  
Vol 6 (3) ◽  
pp. 191-196 ◽  
Author(s):  
Poon Yew Chin ◽  
J. A. Varley ◽  
J. B. Ward
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Oil Palm ◽  
Principal Component ◽  
Component Analysis ◽  
Foliar Nutrients ◽  
Correlation Matrices ◽  
Partial Correlations

SUMMARYTotal and partial correlations are presented between foliar nutrients in a uniformity trial. The method of principal component analysis is applied to the correlation matrices, comparing values for three fronds (3, g and 17). The results are also compared with values obtained elsewhere, and the use of principal components in relating yield to foliar composition is illustrated.

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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