Learn R Programming
nFactors (version 2.3.3)

nFactors-parameters: Argument and Value Parameters Common to the Different Functions Available in Package nFactors

Description

This help file describes the argument and value parameters used in the different functions available in package nFactors.

Arguments:

  1. adequacy: logical: if TRUE prints the recovered population matrix from the factor structure (structureSim)

  2. all: logical: if TRUE computes he Bentler and Yuan index (very long computing time to consider) (structureSim, studySim)

  3. alpha: numeric: statistical significance level (nBartlett, nBentler)

  4. aparallel: numeric: results of a parallel analysis (nScree)

  5. cent: depreciated numeric (use quantile instead): quantile of the distribution (moreStats, parallel)

  6. communalities: character: initial values for communalities ("component", "ginv", "maxr", or "multiple") (iterativePrincipalAxis, principalAxis)

  7. cor: logical: if TRUE computes eigenvalues from a correlation matrix, else from a covariance matrix (eigenComputes, nBartlett, nBentler, nCng, nMreg, nScree, nSeScree)

  8. correction: logical: if TRUE uses a correction for the degree of freedom after the first eigenvalue (nBartlett)

  9. criteria: numeric: by default fixed at \(\hat{\lambda}\). When the \(\lambda\)s are computed from a principal components analysis on a correlation matrix, it corresponds to the usual Kaiser \(\lambda >= 1\) rule. On a covariance matrix or from a factor analysis, it is simply the mean. To apply the \(\lambda >= 0\) sometimes used with factor analysis, fixed the criteria to \(0\) (nScree)

  10. details: logical: if TRUE also returns details about the computation for each eigenvalues (nBartlett, nBentler, nCng, nMreg, structureSim)

  11. diagCommunalities: logical: if TRUE, the correlation between the initial solution and the estimated one will use a correlation of one in the diagonal. If FALSE (default) the diagonal is not used in the computation of this correlation or covariance matrix (rRecovery)

  12. dir: character: directory where to save output (studySim)

  13. eig: depreciated parameter (use x instead): eigenvalues to analyse (nScree, plotParallel)

  14. Eigenvalue: depreciated parameter (use x instead): eigenvalues to analyse (plotuScree)

  15. fload: matrix: loadings of the factor structure (structureSim)

  16. graphic: logical: specific plot (bentlerParameters, structureSim)

  17. index: numeric: vector of the index of the selected indices (plot.structureSim, print.structureSim, summary.structureSim

  18. iterations: numeric: maximum number of iterations to obtain a solution (iterativePrincipalAxis)

  19. legend: logical indicator of the presence of a legend (plotnScree, plotParallel)

  20. loadings: numeric: loadings from a factor analysis solution (rRecovery, generateStructure, studySim)

  21. log: logical: if TRUE does the minimization on the log values (bentlerParameters, nBentler)

  22. main: character: main title (plotnScree, plotParallel, plotuScree, boxplot.structureSim, plot.structureSim)

  23. maxPar: numeric: maximums for the coefficient of the linear trend (bentlerParameters, nBentler)

  24. minPar: numeric: minimums for the coefficient of the linear trend (bentlerParameters, nBentler)

  25. method: character: actually only "giv" is supplied to compute the approximation of the communalities by maximum correlation (corFA, nCng, nMreg, nScree, nSeScree)

  26. mjc: numeric: number of major factors (factors with practical significance) (generateStructure)

  27. pmjc: numeric: number of variables that load significantly on each major factor (generateStructure)

  28. model: character: "components" or "factors" (nScree, parallel, plotParallel, plotuScree, structureSim, eigenBootParallel, eigenBootParallel, studySim)

  29. N: numeric: number of subjects (nBartlett, bentlerParameters, nBentler, studySim)

  30. nboot: numeric: number of bootstrap samples (eigenBootParallel)

  31. nFactors: numeric: number of components/factors to retained (componentAxis, iterativePrincipalAxis, principalAxis, bentlerParameters, boxplot.structureSim, studySim)

  32. nScree: results of a previous nScree analysis (plotnScree)

  33. option: character: "permutation" or "bootstrap" (eigenBootParallel)

  34. object: nScree: an object of the class nScree is.nScree, summary.nScree

  35. object: structureSim: an object of the class structureSim (is.structureSim, summary.structureSim)

  36. parallel: numeric: vector of the result of a previous parallel analysis (plotParallel)

  37. pmjc: numeric: number of major loadings on each factor factors (generateStructure, studySim)

  38. quantile: numeric: quantile that will be reported (parallel, moreStats, eigenBootParallel, structureSim, studySim)

  39. R: numeric: correlation or covariance matrix (componentAxis, iterativePrincipalAxis, principalAxis, principalComponents, rRecovery, corFA)

  40. r2limen: numeric: R2 limen value for the R2 Nelson index (structureSim, nSeScree, studySim)

  41. rep: numeric: number of replications of the correlation or the covariance matrix (default is 100) (parallel)

  42. reppar: numeric: number of replications for the parallel analysis (structureSim, studySim)

  43. repsim: numeric: number of replications of the matrix correlation simulation (structureSim, studySim)

  44. resParx: numeric: restriction on the \(\alpha\) coefficient (x) to graph the function to minimize (bentlerParameters)

  45. resolution: numeric: resolution of the 3D graph (number of points from \(\alpha\) and from \(\beta\)).

  46. resPary: numeric: restriction on the \(\beta\) coefficient (y) to graph the function to minimize (bentlerParameters)

  47. sd: numeric: vector of standard deviations of the simulated variables (for a parallel analysis on a covariance matrix) parallel)

  48. show: logical: if TRUE prints the quantile chosen (moreStats)

  49. stats: numeric: vector of the statistics to return: mean(1), median(2), sd(3), quantile(4), min(5), max(6) (studySim)

  50. subject: numeric: number of subjects (default is 100) (parallel)

  51. tolerance: numeric: minimal difference in the estimated communalities after a given iteration (iterativePrincipalAxis)

  52. trace: logical: if TRUE gives details of the status of the simulations (studySim)

  53. typePlot: character: plots the minimized function according to a 3D plot: "wireframe", "contourplot" or "levelplot" (bentlerParameters)

  54. unique: numeric: loadings on the non significant variables on each major factor (generateStructure, studySim)

  55. upper: logical: if TRUE upper diagonal is replaced with lower diagonal. If FALSE, lower diagonal is replaced with upper diagonal (diagReplace)

  56. use: character: how to deal with missing values, same as the parameter from the corr function (eigenBootParallel)

  57. var: numeric: number of variables (default is 10) (parallel, generateStructure, studySim)

  58. vLine: character: color of the vertical indicator line in the eigen boxplot (boxplot.structureSim)

  59. x: numeric: a vector of eigenvalues, a matrix of correlations or of covariances or a data.frame of data (eigenFrom, nBartlett, nCng, nMreg)

  60. xlab: character: label of the x axis (plotnScree, plotParallel, plotuScree, boxplot.structureSim)

  61. x: data.frame: data from which a correlation or covariance matrix will be obtained (eigenBootParallel)

  62. x: depreciated: (plotParallel)

  63. x: nScree: an object of the class nScree (plot.nScree, print.nScree)

  64. x: numeric: matrix (makeCor)

  65. x: numeric: matrix or data.frame (moreStats)

  66. x: structureSim: an object of the class structureSim (boxplot.structureSim, plot.structureSim, print.structureSim)

  67. ylab: character: label of the y axis (plotnScree, plotParallel, plotuScree, boxplot.structureSim)

Values:

  1. cor: numeric: Pearson correlation between initial and recovered estimated correlation or covariance matrix. Compution depend on the logical value of the communalities argument (rRecovery)

  2. details: numeric: matrix of the details for each index (nBartlett, bentlerParameters, nCng, nMreg)

  3. difference: numeric: difference between initial and recovered estimated correlation or covariance matrix (rRecovery)

  4. iterations: numeric: maximum number of iterations to obtain a solution (iterativePrincipalAxis)

  5. loadings: numeric: loadings of each variable on each component or factor retained (componentAxis, iterativePrincipalAxis, principalAxis, principalComponents)

  6. nFactors: numeric: vector of the number of components or factors retained by the Bartlett, Anderson and Lawley procedures (nBartlett, bentlerParameters, nCng, nMreg)

  7. R: numeric: correlation or covariance matrix (diagReplace, rRecovery)

  8. recoveredR: numeric: recovered estimated correlation or covariance matrix (rRecovery)

  9. tolerance: numeric: minimal difference in the estimated communalities after a given iteration (iterativePrincipalAxis)

  10. values: numeric: data.frame of information (nScree, parallel, plotnScree, plotParallel, plotuScree, structureSim)

  11. values: numeric: data.frame of statistics (moreStats)

  12. values: numeric: full matrix of correlation or covariance (makeCor)

  13. values: numeric: variance of each component or factor (iterativePrincipalAxis, principalComponents)

  14. values: data.frame: mean, median, quantile, standard deviation, minimum and maximum of bootstrapped eigenvalues (eigenBootParallel)

  15. values: numeric: matrix of correlation or covariance with communalities in the diagonal (corFA)

  16. values: numeric: variance of each component or factor retained (componentAxis, principalAxis)

  17. values: numeric: matrix factor structure (generateStructure)

  18. varExplained: numeric: variance explained by each component or factor retained (componentAxis, iterativePrincipalAxis, principalAxis, principalComponents)

  19. varExplained: numeric: cumulative variance explained by each component or factor retained (componentAxis, iterativePrincipalAxis, principalAxis, principalComponents)

Arguments

References

Raiche, G., Riopel, M. and Blais, J.-G. (2006). Non graphical solutions for the Cattell's scree test. Paper presented at the International Annual meeting of the Psychometric Society, Montreal. [http://www.er.uqam.ca/nobel/r17165/RECHERCHE/COMMUNICATIONS/]

See Also

Other packages are also very useful for principal component and factor analysis. The R psychometric view is instructive at this point. See http://cran.stat.sfu.ca/web/views/Psychometrics.html for further details.