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) 
Issue tracker  View/Report issues 
Source files  component.xml analyzer.R 
Usage  Example with default values 
Name  Type  Mandatory  Description 

matrix  CSV  Mandatory  The data matrix on which PCA is applied. 
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. 
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 columnwise. 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 case  Parameters▼  IN matrix 
OUT loadings 
OUT scores 
OUT variation 


case1  (missing)  matrix  loadings  scores  variation  
case2  properties  matrix  loadings  scores  variation  
direction = row, 