The third component has large negative associations with income, education, and credit cards, so this component primarily measures the applicant's academic and income qualifications. The second component has large negative associations with Debt and Credit cards, so this component primarily measures an applicant's credit history. In these results, first principal component has large positive associations with Age, Residence, Employ, and Savings, so this component primarily measures long-term financial stability. Use the components in the steep curve before the first point that starts the line trend. The ideal pattern is a steep curve, followed by a bend, and then a straight line. Scree plot The scree plot orders the eigenvalues from largest to smallest. For example, using the Kaiser criterion, you use only the principal components with eigenvalues that are greater than 1. Retain the principal components with the largest eigenvalues. Eigenvalues You can use the size of the eigenvalue to determine the number of principal components. eigplot( mc ) creates a plot containing the eigenvalues of the transition matrix of the discrete-time Markov chain mc on the complex plane. However, if you want to perform other analyses on the data, you may want to have at least 90% of the variance explained by the principal components. For descriptive purposes, you may only need 80% of the variance explained.

Component plots show the component score of each observation or component.

The acceptable level depends on your application. In the Eigenvalues of the Correlation Matrix table, we can see that the first. Retain the principal components that explain an acceptable level of variance. Proportion of variance that the components explain Use the cumulative proportion to determine the amount of variance that the principal components explain.

Determine the minimum number of principal components that account for most of the variation in your data, by using the following methods.

Plot vs eigenvalues matlab