Understanding the Role of the Prcomp Function
The prcomp option in R actually calculates the principal component which are those variables that account for the most variation in a dataset. These principal components are new variables that are constructed from the original variables are linearly independent of each other, and are ranked according to the extent of variance that they account for. This results from the fact that by projecting the original data onto these components, prcomp works to transform the high dimensionality data into a more manageable form without losing the inherent structure of the data.
The basic syntax of the prcomp function is:
Syntax: prcomp(x, center = TRUE, scale. = FALSE, rank. = NULL)
Where:
- x: A numeric matrix or data frame to be analyzed.
- center: A logical value indicating whether the variables should be centered to have mean zero. Default is TRUE.
- scale.: A logical value indicating whether the variables should be scaled to have unit variance. Default is FALSE.
- rank.: The number of principal components to retain. If NULL, all components are retained
Now we Consider a dataset containing measurements of various chemical compounds:
Compound | Feature 1 | Feature 2 | Feature 3 | Feature 4 |
|---|---|---|---|---|
A |
0.1 |
0.2 |
0.5 |
0.4 |
B |
0.3 |
0.6 |
0.2 |
0.8 |
C |
0.4 |
0.1 |
0.9 |
0.3 |
D |
0.7 |
0.5 |
0.4 |
0.6 |
prcomp in R
The prcomp function serves as a great tool for PCA performance. This article is an extensive discussion of PCA using prcomp in R, which covers concepts, functions, and a true illustration of its usage.