Output | Expression | 2D PCA Plots (Correlation and Principal Components)

2D PCA Plots (Correlation and Principal Components)
The 2D PCA Plots tab contains the following elements:
A scatterplot matrix showing the Correlation Analysis of Principal Components
This display shows computed Pearson correlations of the principal components , which are usually near zero as long as mean-centering has been performed.
The Scatterplot Matrix plots all pairs of components and individual histograms. These plots can reveal interrelationships among components and how they align with known experimental factors .
See Scatterplot Matrix for more information.
A parallel plot of the first three principal components.
See Parallel Plot for more information.
This plot illustrates the distance of specific observations from the mean center of the other observations. In this example, none of the observations show significant Mahalanobis distances.
See Mahalanobis Distances for more information about these plots.
This button is designed to let you eliminate samples that you consider to be outliers. First select the outliers in the plot, and then click the button to create a reduced experimental design data set. You can then use this new data set for further analyses and omit the outlying samples.
Tip : You can choose the design variable to color the points by and generate the corresponding legend by right-clicking within a plot and selecting Row Legend... from the pop-up menu. Select the desired variable, and specify a color scheme and marker style and click OK .