Graphical representation of the eigenvalues of a correlation/covariance matrix. Usefull to determine the dimensional structure of a set of variables. Simulation are proposed to help the interpretation.

1 | ```
scree.plot(namefile, title = "Scree Plot", type = "R", use = "complete.obs", simu = "F")
``` |

`namefile` |
dataset |

`title` |
Title |

`type` |
type="R" to obtain the eigenvalues of the correlation matrix of dataset, type="V" for the covariance matrix, type="M" if the input data is directly the matrix, type="E" if the input data are directly the eigenvalues |

`use` |
omit missing values by default, use="P" to analyse the pairwise correlation/covariance matrix |

`simu` |
simu=p to add p screeplots of simulated random normal data (same number of patients and variables as in the original data set, same pattern of missing data if use="P") |

Simulations lead sometimes to underestimate the actual number of dimensions (as opposed to Kayser rule: eigen values superior to 1). Basically, simu=20 is enough.

a plot

Bruno Falissard

Horn, JL (1965) A Rationale and Test for the Number of Factors in Factor Analysis, Psychometrika, 30, 179-185. Cattell, RB (1966) The scree test for the number of factors. Multivariate Behavioral Research, 1, 245-276.

1 2 | ```
data(expsy)
scree.plot(expsy[,1:10],simu=20,use="P") #no obvious structure with such a small sample
``` |

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