How do you color PCA?

Hi, In the PCA plot, right click on the graph and select “ROW LEGEND”, you can then choose the column that will be used to color code the dots on your plot.

How do you describe a PCA plot?

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

How do you interpret PCA loads in R?

Positive loadings indicate a variable and a principal component are positively correlated: an increase in one results in an increase in the other. Negative loadings indicate a negative correlation. Large (either positive or negative) loadings indicate that a variable has a strong effect on that principal component.

What is PC1 and PC2 in PCA plot?

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.

What do PCA scores mean?

Principle Components Analysis
Principal component scores are a group of scores that are obtained following a Principle Components Analysis (PCA). In PCA the relationships between a group of scores is analyzed such that an equal number of new “imaginary” variables (aka principle components) are created.

What do PCA loadings represent?

PCA loadings are the coefficients of the linear combination of the original variables from which the principal components (PCs) are constructed.

What are PC1 and PC2 in a PCA plot?

What does PC1 mean in PCA?

first principal component
The first principal component (PC1) is the line that best accounts for the shape of the point swarm. It represents the maximum variance direction in the data. Each observation (yellow dot) may be projected onto this line in order to get a coordinate value along the PC-line. This value is known as a score.

What is the correlation between PC1 and PC2?

So that PC1 and PC2 are not correlated to each other.

What do eigenvalues tell us in PCA?

Eigenvalues are coefficients applied to eigenvectors that give the vectors their length or magnitude. So, PCA is a method that: Measures how each variable is associated with one another using a Covariance matrix. Understands the directions of the spread of our data using Eigenvectors.

What is PC1 and PC2 in PCA R?