eigengap.act: Rank selection via adjust correlation thresholding
In sgdGMF: Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent
eigengap.act R Documentation
Rank selection via adjust correlation thresholding
Description
Select the number of significant principal components of a matrix via adjust
correlation threshold (ACT)
Usage
eigengap.act(covmat, nobs, maxcomp = NULL)
Arguments
covmat
matrix to be decomposed
nobs
number of observations used to compute the covariance matrix
maxcomp
maximum number of eigenvalues to compute
References
Fan, J., Guo, j. and Zheng, S. (2020).
Estimating number of factors by adjusted eigenvalues thresholding.
Journal of the American Statistical Association, 117(538): 852–861
sgdGMF documentation built on June 8, 2025, 12:05 p.m.
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| eigengap.act | R Documentation |
Rank selection via adjust correlation thresholding
Description
Select the number of significant principal components of a matrix via adjust correlation threshold (ACT)
Usage
eigengap.act(covmat, nobs, maxcomp = NULL)
Arguments
covmat |
matrix to be decomposed |
nobs |
number of observations used to compute the covariance matrix |
maxcomp |
maximum number of eigenvalues to compute |
References
Fan, J., Guo, j. and Zheng, S. (2020). Estimating number of factors by adjusted eigenvalues thresholding. Journal of the American Statistical Association, 117(538): 852–861
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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