Number of principal components to calculate. Svd PCA is chosen for data wihout missing values and Numberic variables are used to fit the PCA. Can also be a data frame in which case all Also takesĮxpressionSet in which case the transposed expression Such) with samples in rows and variables as columns. Numerical matrix with (or an object coercible to Pca ( object, method, nPcs = 2, scale = c ( "none", "pareto", "vector", "uv" ), center = TRUE, completeObs = TRUE, subset = NULL, cv = c ( "none", "q2" ). plot.pcaRes: Plot diagnostics (screeplot).pcaRes: Class for representing a PCA result.pcaNet: Class representation of the NLPCA neural net.pcaMethods-deprecated: Deprecated methods for pcaMethods.pca: Perform principal component analysis.optiAlgCgd: Conjugate gradient optimization.nVar-pcaRes-method: Get the number of variables used to build the PCA model.nObs-pcaRes-method: Get the number of observations used to build the PCA model.nniRes: Class for representing a nearest neighbour imputation result.
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