Description Usage Arguments Details Value Author(s) References Examples
View source: R/preprocess_pca.R
The data is denoised and the missing values are imputed by using the top r eigenmatrices. After apply SVD on the centered and/or scaled data, the top r eigenmatrices are constructed where is r < min(n,m).
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dat |
a time-series data matrix with |
r |
the number of PCs or eigenmatrices to retain. |
method |
a method to perform singular-value decomposition when a dataset has missing values. See below for the explanation. |
center.dat |
a logical specifying to center the input and denoised data. By default, |
scale.dat |
a logical specifying to scale the input and denoised data. By default, |
verbose |
a logical specifying to print the computational progress. By default, |
seed |
a seed for the random number generator. |
... |
optional arguments. |
To impute missing values with PCA/SVD, two approximation methods are provided.
For method="nipals", a Non-linear Iterative Partial Least Squares (NIPALS) algorithm is used from nipals
in the mixOmics package.
For method="em", a low-rank SVD approximation by the EM algorithm is used from imputed.svd
in the bcv package.
preprocess_pca
returns a matrix of imputed and/or denoised data.
Neo Christopher Chung nchchung@gmail.com
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D. and Altman, R.B. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520–525.
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