faMAP | R Documentation |

Uses Velicer's MAP (i.e., matrix of partial correlations) procedure to determine the number of components from a matrix of partial correlations.

```
faMAP(R, max.fac = 8, Print = TRUE, Plot = TRUE, ...)
```

`R` |
input data in the form of a correlation matrix. |

`max.fac` |
maximum number of dimensions to extract. |

`Print` |
(logical) Print = TRUE will print complete results. |

`Plot` |
(logical) Plot = TRUE will plot the MAP values. |

`...` |
Arguments to be passed to the plot functions (see |

`MAP` |
Minimum partial correlations |

`MAP4` |
Minimum partial correlations |

`fm` |
average of the squared partial correlations after the first m components are partialed out. |

`fm4` |
see Velicer, Eaton, & Fava, 2000. |

`PlotAvgSq` |
A saved object of the original MAP plot (based on the average squared partial r's.) |

`PlotAvg4th` |
A saved object of the revised MAP plot (based on the average 4th power of the partial r's.) |

Niels Waller

Velicer, W. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3):321–327.

Velicer,W. F., Eaton, C. A. , & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In R. D. Goffin & E. Helmes (Eds.). Problems and Solutions in Human Assessment: Honoring Douglas N. Jackson at Seventy (pp. 41-71. Boston, MA: Kluwer Academic.

```
# Harman's data (1967, p 80)
# R = matrix(c(
# 1.000, .846, .805, .859, .473, .398, .301, .382,
# .846, 1.000, .881, .826, .376, .326, .277, .415,
# .805, .881, 1.000, .801, .380, .319, .237, .345,
# .859, .826, .801, 1.000, .436, .329, .327, .365,
# .473, .376, .380, .436, 1.000, .762, .730, .629,
# .398, .326, .319, .329, .762, 1.000, .583, .577,
# .301, .277, .237, .327, .730, .583, 1.000, .539,
# .382, .415, .345, .365, .629, .577, .539, 1.000), 8,8)
F <- matrix(c( .4, .1, .0,
.5, .0, .1,
.6, .03, .1,
.4, -.2, .0,
0, .6, .1,
.1, .7, .2,
.3, .7, .1,
0, .4, .1,
0, 0, .5,
.1, -.2, .6,
.1, .2, .7,
-.2, .1, .7),12,3)
R <- F %*% t(F)
diag(R) <- 1
faMAP(R, max.fac = 8, Print = TRUE, Plot = TRUE)
```

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