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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