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PCA

PCA performs a principal component analysis on a given data matrix based on eigen values.

Version 1.1
Bundle tools
Categories Multivariate Statistics
Authors Minna Miettinen (Minna.Miettinen@Helsinki.FI), Ville Rantanen (ville.rantanen@helsinki.fi)
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Source files component.xml analyzer.R
Usage Example with default values

Inputs

Name Type Mandatory Description
matrix CSV Mandatory The data matrix on which PCA is applied.

Outputs

Name Type Description
loadings CSV The matrix of variable loadings.
scores CSV The matrix of scores on each principal component. Scores are computed by multiplying the data by the matrix of loadings.
variation CSV The amount of variation in the original data explained by the principal components i.e. the standard deviations of the principal components.

Parameters

Name Type Default Description
center boolean true A logical value indicating whether the variables should be shifted to be zero centered. Centering is recommended; Mean subtraction (a.k.a. "mean centering") is necessary for performing PCA to ensure that the first principal component describes the direction of maximum variance. If mean subtraction is not performed, the first principal component will instead correspond to the mean of the data.
direction string "column" Direction of the summarization i.e. should PCA be applied row- or column-wise. The possible values are "column" and "row".
scale boolean true A logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. In general, scaling is advisable.
seed int 12345 Seed number for the pseudo random number generator

Test cases

Test case Parameters▼ IN
matrix		 OUT
loadings		 OUT
scores		 OUT
variation
case1 (missing) matrix loadings scores variation
case2 properties matrix loadings scores variation

direction = row,
scale = false,
center = false,
seed = 1234
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