Multichannel-systems NeuroExplorer Manual do Utilizador descarregar pdf (Página 93)

2.25. Principal Component Analysis

This analysis calculates eigenvalues and eigenvectors (principal components) of the matrix of

correlations of rate histograms. The analysis creates population vectors corresponding to the principal

components.

Parameters

Parameter Description

Bin Bin size in seconds.

Vectors Prefix A string specifying how the population vector names will be generated. For

example, if prefix is pca1, the vector names will be pca1_01, pca1_02,

etc.

Select Data If Select Data is From Time Range, only the data from the specified (by

Select Data From and Select Data To parameters) time range will be used

in analysis. See also Data Selection Options.

Select Data From Start of the time range in seconds.

Select Data To End of the time range in seconds.

Interval filter Specifies the interval filter that will be used to preselect data before

analysis. See also Data Selection Options.

Send to Matlab An option to send the matrix of numerical results to Matlab. See also

Matlab Options.

Matrix Name Specifies the name of the results matrix in Matlab workspace.

Matlab command Specifies a Matlab command that is executed after the numerical results

are sent to Matlab.

Send to Excel An option to send numerical results or summary of numerical results to

Excel. See also Excel Options.

Sheet Name The name of the worksheet in Excel where to copy the numerical results.

TopLeft Specifies the Excel cell where the results are copied. Should be in the form

CR where C is Excel column name, R is the row number. For example, A1

is the top-left cell in the worksheet.

Summary of Numerical Results

The following information is available in the Summary of Numerical Results

Column Description

Variable Variable name and PCA statistics name.

pca1_N The weight of the variable in the N-th eigenvector. The rows at the bottom

of the table also show Eigenvalue of this eigenvector, % of variance it

explains, and the cumulative percent of the variance explained by this and

preceding eigenvectors.

Corr with VAR Correlation with the specified variable.

Numerical Results

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