How do I plot PCA data in Matlab?

Description

  1. Select principal components for the x and y axes from the drop-down list below each scatter plot.
  2. Click a data point to display its label.
  3. Select a subset of data points by dragging a box around them.
  4. Select a label in the list box to highlight the corresponding data point in the plot.

What does a 3D PCA plot show?

What is 3D PCA? Most of the time, a PCA plot is a 2D scatter plot in which the data is plotted with two most descriptive principal components. However, you can choose to plot with three PCs instead, and this will create a 3D scatter plot, also called 3D PCA.

How do you do a PCA plot?

How do you do a PCA?

  1. Standardize the range of continuous initial variables.
  2. Compute the covariance matrix to identify correlations.
  3. Compute the eigenvectors and eigenvalues of the covariance matrix to identify the principal components.
  4. Create a feature vector to decide which principal components to keep.

What does PCA mean in Matlab?

Principal component analysis
Principal component analysis is a quantitatively rigorous method for achieving this simplification. The method generates a new set of variables, called principal components. Each principal component is a linear combination of the original variables.

What is a PCA graph?

A PCA plot shows clusters of samples based on their similarity. Figure 1. PCA plot. PCA does not discard any samples or characteristics (variables). Instead, it reduces the overwhelming number of dimensions by constructing principal components (PCs).

What is score plot?

The Score Plot involves the projection of the data onto the PCs in two dimensions. The PCs were computed to provide a new space of uncorrelated ‘variables’ which best carry the variation in the original data and in which to more succinctly represent the original ‘samples’.

How do you read PCoA plots?

Interpretation of a PCoA plot is straightforward: objects ordinated closer to one another are more similar than those ordinated further away. (Dis)similarity is defined by the measure used in the construction of the (dis)similarity matrix used as input.

How do you interpret PCA results?

The VFs values which are greater than 0.75 (> 0.75) is considered as “strong”, the values range from 0.50-0.75 (0.50 ≥ factor loading ≥ 0.75) is considered as “moderate”, and the values range from 0.30-0.49 (0.30 ≥ factor loading ≥ 0.49) is considered as “weak” factor loadings.

What is PC1 and PC2?

Principal components are created in order of the amount of variation they cover: PC1 captures the most variation, PC2 — the second most, and so on. Each of them contributes some information of the data, and in a PCA, there are as many principal components as there are characteristics.

How do I read a PCoA plot?