Description Usage Arguments Details Value Author(s) See Also Examples

Determines the number of latent variables K via AIC, BIC, and deviance reduction. A pdf file containing all three plots is generated.

1 |

`AIC` |
vector of AIC for each K returned from |

`BIC` |
vector of BIC for each K returned from |

`RSS` |
vector of RSS for each K returned from |

`K` |
vector of K returned from |

`filename` |
Filename of the output plot of AIC and RSS |

AIC: Akaike information criterion, used for model selection; BIC: Bayesian information criterion, used for model selection; RSS: residue sum of squares, used to plot scree plot and determine the 'elbow'.

pdf file with three plots: AIC, BIC, and percentage of variance explained versus the number of latent factors.

Yuchao Jiang yuchaoj@wharton.upenn.edu

1 2 3 4 5 6 7 8 | ```
AIC <- normObjDemo$AIC
BIC <- normObjDemo$BIC
RSS <- normObjDemo$RSS
K <- normObjDemo$K
projectname <- bambedObjDemo$projectname
chr <- bambedObjDemo$chr
choiceofK(AIC, BIC, RSS, K, filename = paste(projectname, "_", chr,
"_choiceofK", ".pdf", sep = ""))
``` |

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