scholarly journals Identification of patterns related to linkage groups or disequilibrium by factor analysis

2021 ◽  
Vol 51 (5) ◽  
Author(s):  
Cristiano Ferreira de Oliveira ◽  
Gabriely Teixeira ◽  
Alex da Silva Temoteo ◽  
Moysés Nascimento ◽  
Cosme Damião Cruz
Keyword(s):  
Factor Analysis ◽  
Linkage Disequilibrium ◽  
Statistical Power ◽  
Linkage Groups ◽  
Data Set ◽  
Scree Plot ◽  
F2 Population ◽  
Number Of Factors ◽  
The Matrix ◽  
Genomic Studies

ABSTRACT: Empirical patterns of linkage disequilibrium (LD) can be used to increase the statistical power of genetic mapping. This study was carried out with the objective of verifying the efficacy of factor analysis (AF) applied to data sets of molecular markers of the SNP type, in order to identify linkage groups and haplotypes blocks. The SNPs data set used was derived from a simulation process of an F2 population, containing 2000 marks with information of 500 individuals. The estimation of the factorial loadings of FA was made in two ways, considering the matrix of distances between the markers (A) and considering the correlation matrix (R). The number of factors (k) to be used was established based on the graph scree-plot and based on the proportion of the total variance explained. Results indicated that matrices A and R lead to similar results. Based on the scree-plot we considered k equal to 10 and the factors interpreted as being representative of the bonding groups. The second criterion led to a number of factors equal to 50, and the factors interpreted as being representative of the haplotypes blocks. This showed the potential of the technique, making it possible to obtain results applicable to any type of population, helping or corroborating the interpretation of genomic studies. The study demonstrated that AF was able to identify patterns of association between markers, identifying subgroups of markers that reflect factor binding groups and also linkage disequilibrium groups.

Factor Analysis, Random Data, and Patterned Results

1981 ◽  
Vol 46 (2) ◽  
pp. 272-283 ◽  
Author(s):  
Robert K. Vierra ◽  
David L. Carlson
Keyword(s):  
Factor Analysis ◽  
Correlation Matrix ◽  
Statistical Significance ◽  
Principal Component ◽  
Multivariate Statistical Techniques ◽  
Random Data ◽  
Multivariate Statistical ◽  
Data Set ◽  
Bartlett’S Test ◽  
The Matrix

Multivariate statistical techniques such as factor analysis are capable of producing patterned results with most, if not all, data matrices. This paper demonstrates that patterned results are obtainable when principal component analysis is applied to a random data set. It is suggested that Bartlett's test for the statistical significance of a correlation matrix be employed in deciding whether a factor analysis of the matrix is justified.

The Application and Misapplication of Factor Analysis in Marketing Research

1981 ◽  
Vol 18 (1) ◽  
pp. 51-62 ◽  
Author(s):  
David W. Stewart
Keyword(s):  
Factor Analysis ◽  
Marketing Research ◽  
Dimensional Structure ◽  
Data Set ◽  
Number Of Factors

The use of factor analysis as a method for examining the dimensional structure of data is contrasted with its frequent misapplication as a tool for identifying clusters and segments. Procedures for determining when a data set is appropriate for factoring, for determining the number of factors to extract, and for rotation are discussed.

scholarly journals DETERMINANTS OF BRAND PREFERENCE OF COMMERCIAL THREE WHEELER PASSENGER AUTO RICKSHAW DRIVERS IN ADAMA CITY, ETHIOPIA

2019 ◽  
Vol 7 (1) ◽  
pp. 484-492
Author(s):  
Mohd Arif Shaikh ◽  
Devi Prasad U ◽  
Pagadala Sugandha Devi
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Parallel Analysis ◽  
Primary Data ◽  
Brand Preference ◽  
Scree Plot ◽  
Component Solution ◽  
Number Of Factors ◽  
Eigen Values ◽  
Relevant Factors

Purpose of the study: The aim of this study is to find out the factors that influence drivers of three wheeler auto rickshaw in their brand preference towards different brands of commercial three wheeler passenger auto rickshaw in Adama City, Ethiopia Methodology:  Primary data was collected from 500 auto drivers using a pilot tested questionnaire consisting of 40 questions. Cronbach’s alpha measure was used to test constructs reliability and in order to identify brand preference, exploratory factor analysis and parallel analysis was conducted. Main Findings: PCA revealed that there are 11 factors whose Eigen values are above 1. A look at scree plot indicated that there is a need to reconsider the number of factors to be used for further analysis. This decision was supported by Parallel analysis and 8 relevant factors identified. This 8 component solution explained 64.25 % of the variance. Applications of this study: Identification of determinants of brand preference can be used by three wheeler passenger auto manufacturers and distributors in Ethiopia Novelty/Originality of this study: There is no study conducted on drivers brand preference of three wheeler passenger auto rickshaws in Ethiopia.

Towards a Validation of Multiple Features in the Assessment of Emotions

1998 ◽  
Vol 14 (3) ◽  
pp. 202-210 ◽  
Author(s):  
Suzanne Skiffington ◽  
Ephrem Fernandez ◽  
Ken McFarland
Keyword(s):  
Factor Analysis ◽  
Pilot Study ◽  
Cognitive Appraisal ◽  
Negative Emotions ◽  
The Self ◽  
Factor Analyses ◽  
Data Set ◽  
Multiple Features ◽  
Action Tendency ◽  
Intensity Range

This study extends previous attempts to assess emotion with single adjective descriptors, by examining semantic as well as cognitive, motivational, and intensity features of emotions. The focus was on seven negative emotions common to several emotion typologies: anger, fear, sadness, shame, pity, jealousy, and contempt. For each of these emotions, seven items were generated corresponding to cognitive appraisal about the self, cognitive appraisal about the environment, action tendency, action fantasy, synonym, antonym, and intensity range of the emotion, respectively. A pilot study established that 48 of the 49 items were linked predominantly to the specific emotions as predicted. The main data set comprising 700 subjects' ratings of relatedness between items and emotions was subjected to a series of factor analyses, which revealed that 44 of the 49 items loaded on the emotion constructs as predicted. A final factor analysis of these items uncovered seven factors accounting for 39% of the variance. These emergent factors corresponded to the hypothesized emotion constructs, with the exception of anger and fear, which were somewhat confounded. These findings lay the groundwork for the construction of an instrument to assess emotions multicomponentially.

scholarly journals Analysis and Mapping of Gene Families Encoding β-1,3-Glucanases of Soybean

Genetics ◽  
1999 ◽  
Vol 153 (1) ◽  
pp. 445-452
Author(s):  
Wei Jin ◽  
Harry T Horner ◽  
Reid G Palmer ◽  
Randy C Shoemaker
Keyword(s):  
Gene Families ◽  
Mrna Levels ◽  
Linkage Groups ◽  
Flower Bud ◽  
Cross Hybridization ◽  
Coding Regions ◽  
F2 Population ◽  
Young Leaves ◽  
Glycine Max L ◽  
Stem Leaf

Abstract Oligonucleotide primers designed for conserved sequences from coding regions of β-1,3-glucanase genes from different species were used to amplify related sequences from soybean [Glycine max (L.) Merr.]. Sequencing and cross-hybridization of amplification products indicated that at least 12 classes of β-1,3-glucanase genes exist in the soybean. Members of classes mapped to 34 loci on five different linkage groups using an F2 population of 56 individuals. β-1,3-Glucanase genes are clustered onto regions of five linkage groups. Data suggest that more closely related genes are clustered together on one linkage group or on duplicated regions of linkage groups. Northern blot analyses performed on total RNA from root, stem, leaf, pod, flower bud, and hypocotyl using DNA probes for the different classes of β-1,3-glucanase genes revealed that the mRNA levels of all classes were low in young leaves. SGlu2, SGlu4, SGlu7, and SGlu12 mRNA were highly accumulated in young roots and hypocotyls. SGlu7 mRNA also accumulated in pods and flower buds.

scholarly journals Optimal parameter selection in Weeks’ method for numerical Laplace transform inversion based on machine learning

2021 ◽  
Vol 15 ◽  
pp. 174830262199962
Author(s):  
Patrick O Kano ◽  
Moysey Brio ◽  
Jacob Bailey
Keyword(s):  
Laplace Transform ◽  
Optimal Parameter ◽  
Large Data ◽  
Matrix Exponential ◽  
Resolvent Matrix ◽  
Data Set ◽  
Network Training ◽  
The Matrix ◽  
The Laplace Transform ◽  
Space Function

The Weeks method for the numerical inversion of the Laplace transform utilizes a Möbius transformation which is parameterized by two real quantities, σ and b. Proper selection of these parameters depends highly on the Laplace space function F( s) and is generally a nontrivial task. In this paper, a convolutional neural network is trained to determine optimal values for these parameters for the specific case of the matrix exponential. The matrix exponential eA is estimated by numerically inverting the corresponding resolvent matrix [Formula: see text] via the Weeks method at [Formula: see text] pairs provided by the network. For illustration, classes of square real matrices of size three to six are studied. For these small matrices, the Cayley-Hamilton theorem and rational approximations can be utilized to obtain values to compare with the results from the network derived estimates. The network learned by minimizing the error of the matrix exponentials from the Weeks method over a large data set spanning [Formula: see text] pairs. Network training using the Jacobi identity as a metric was found to yield a self-contained approach that does not require a truth matrix exponential for comparison.

Factor Analysis of Students’ Exposure to Social Media for Food and Beverage

2021 ◽  
Vol 8 (2) ◽  
pp. 113-118
Author(s):  
Noora Shrestha
Keyword(s):  
Social Media ◽  
Factor Analysis ◽  
Data Set ◽  
Factors Associated ◽  
Rotation Method ◽  
The Social ◽  
Food And Beverage ◽  
Social Media Platforms ◽  
Strongly Agree ◽  
Self Administered Questionnaire

Food and beverage marketing on social media is a powerful factor to influence students’ exposure to social media and application for food and beverage. It is a well-known fact that most of the food and beverage business target young people on the social media. The objective of the study is to identify the factors associated to the students’ exposure in the social media platforms for food and beverage. The young students between the ages 20 to 26 years completed a self-administered questionnaire survey on their media use for food and beverages. The questionnaire was prepared using Likert scale with five options from strongly agree to strongly disagree. The data set was described with descriptive statistics such as mean and standard deviation. The exploratory factor analysis with varimax rotation method was used to extract the factors. The most popular social media among the respondents were Facebook, Instagram, and You Tube. 73.3% of the students were exposed to food and beverage application in their mobile device and 76% of them followed the popular food and beverage pages in social media. The result revealed that social media posts, promotional offer, and hygienic concept have positively influenced majority of the students’ exposure to social media for food and beverage. Keywords: Factor analysis, Social Media, Food and Beverage, Student, Promotional Offer.

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