title: Affected States soft independent modeling by class analogy from the relation between independent variables, number of independent variables and sample size.
abstract: OBJECTIVE
  The aim of study is to introduce method of Soft Independent Modeling of Class Analogy (SIMCA), and to express whether the method is affected from the number of independent variables, the relationship between variables and sample size.
  
  
  METHODS
  Simulation study.
  
  
  METHODS
  SIMCA model is performed in two stages. In order to determine whether the method is influenced by the number of independent variables, the relationship between variables and sample size, simulations were done. Conditions in which sample sizes in both groups are equal, and where there are 30, 100 and 1000 samples; where the number of variables is 2, 3, 5, 10, 50 and 100; moreover where the relationship between variables are quite high, in medium level and quite low were mentioned.
  
  
  RESULTS
  Average classification accuracy of simulation results which were carried out 1000 times for each possible condition of trial plan were given as tables.
  
  
  CONCLUSIONS
  It is seen that diagnostic accuracy results increase as the number of independent variables increase. SIMCA method is a method in which the relationship between variables are quite high, the number of independent variables are many in number and where there are outlier values in the data that can be used in conditions having outlier values.
authors.affiliation: Department of Biostatistics and Bioinformatics, Faculty of Medicine, Mersin University, Mersin, Turkey.
authors.forename: Emine Arzu
authors.initials: EA
authors.lastname: Kanık
authors.affiliation: Department of Biostatistics and Bioinformatics, Faculty of Medicine, Mersin University, Mersin, Turkey.
authors.forename: Gülhan Orekici
authors.initials: GO
authors.lastname: Temel
authors.affiliation: Department of Biostatistics and Bioinformatics, Faculty of Medicine, Mersin University, Mersin, Turkey.
authors.forename: Semra
authors.initials: S
authors.lastname: Erdoğan
authors.affiliation: Department of Computer Systems, Mersin University, Mersin, Turkey.
authors.forename: Irem Ersöz
authors.initials: IE
authors.lastname: Kaya
keywords: Classification; multicollinearity; outlier values