Description Usage Arguments Value References See Also Examples
This function imputes missing data using a probabilistic principle component analysis framework and is a wrapper around functions implemented in the pcaMethods package (Stacklies et al. 2007), was proposed by Troyanskaya et al 2001 and is based on methods developed in Roweis 1997.
1 | completeData(data, n_pcs, cut.trait = 0.5, cut.ind = 0.5, show.test = TRUE)
|
data |
a (non-empty) numeric matrix of data values. |
n_pcs |
a (non-empty) numeric value indicating the desired number of principle component axes. |
cut.trait |
a number indicating the maximum proportion of missing traits before an individual is removed from data. A value of 1 will not remove any individuals and 0 will remove them all. |
cut.ind |
a number indicating the maximum proportion of individuals missing a trait score before that trait is removed from data. A value of 1 will not remove any traits and 0 will remove them all. |
show.test |
a logical statement indicating whether a diagnostic plot of the data imputation should be returned. |
Returns a list with two entries.
complete_dat |
an object of class matrix with missing values imputed using a probabilistic principle component framework. |
plots |
a list of plots stored as grid plots. |
Roweis S (1997). EM algorithms for PCA and sensible PCA. Neural Inf. Proc. Syst., 10, 626 - 632.
Stacklies W, Redestig H, Scholz M, Walther D, Selbig J (2007). pcaMethods - a Bioconductor package providing PCA methods for incomplete data. Bioinformatics, 23, 1164 - 1167.
Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman R (2001). Missing value estimation methods for DNA microarrays. Bioinformatics, 17(6), 520 - 5252.
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