{ "_id": "6a181eb2acfb0bcc41db1a84", "Package": "hornpa", "Type": "Package", "Title": "Horn's (1965) Test to Determine the Number of Components/Factors", "Version": "1.1.0", "Date": "2022-05-09", "Author": "Francis Huang ", "Maintainer": "Francis Huang ", "Description": "A stand-alone function that generates a user specified\nnumber of random datasets and computes eigenvalues using the\nrandom datasets (i.e., implements Horn's [1965, Psychometrika]\nparallel analysis\n). Users\nthen compare the resulting eigenvalues (the mean or the\nspecified percentile) from the random datasets (i.e.,\neigenvalues resulting from noise) to the eigenvalues generated\nwith the user's data. Can be used for both principal components\nanalysis (PCA) and common/exploratory factor analysis (EFA).\nThe output table shows how large eigenvalues can be as a result\nof merely using randomly generated datasets. If the user's own\ndataset has actual eigenvalues greater than the corresponding\neigenvalues, that lends support to retain that\nfactor/component. In other words, if the i(th) eigenvalue from\nthe actual data was larger than the percentile of the (i)th\neigenvalue generated using randomly generated data, empirical\nsupport is provided to retain that factor/component. Horn, J.\n(1965). A rationale and test for the number of factors in\nfactor analysis. 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