Table of Contents

## What are modes in PCA?

Modes of variation in PCA Suppose the data represent independent drawings from some -dimensional population with mean vector and covariance matrix . These data yield the sample mean vector , and the sample covariance matrix with eigenvalue-eigenvector pairs . Then the -th mode of variation of can be estimated by.

## How many principal components are in PCA?

Each column of rotation matrix contains the principal component loading vector. This is the most important measure we should be interested in. This returns 44 principal components loadings.

**What is Q mode analysis?**

Q-mode factor analysis may be used to interpret rock genesis from relations among matrix rows, and the constant row-sum is a definite asset. The constant can be used to compute sealers, which, in turn, can be used to adjust the factor loadings and scores to conform with the original data.

**What does variation mean in PCA?**

In case of PCA, “variance” means summative variance or multivariate variability or overall variability or total variability. Below is the covariance matrix of some 3 variables. Their variances are on the diagonal, and the sum of the 3 values (3.448) is the overall variability.

### How many principal components should I use?

In this theoretical image taking 100 components result in an exact image representation. So, taking more than 100 elements is useless. If you want for example maximum 5% error, you should take about 40 principal components.

### How do you choose principal components in PCA?

Choosing the Principal Components The common way of selecting the Principal Components to be used is to set a threshold of explained variance, such as 80%, and then select the number of components that generate a cumulative sum of explained variance as close as possible of that threshold.

**Is high variance good in PCA?**

The % of variance explained by the PCA representation reflect the % of information that this representation bring about the original structure. Higher is the % of variance, higher is the % of information and less is the information loss.

**What is a good variance for PCA?**

Ideally, you would choose the number of components to include in your model by adding the explained variance ratio of each component until you reach a total of around 0.8 or 80% to avoid overfitting.

## Should I use factor analysis or PCA?

If you assume or wish to test a theoretical model of latent factors causing observed variables, then use factor analysis. If you want to simply reduce your correlated observed variables to a smaller set of important independent composite variables, then use PCA.

## How do you choose between PCA and factor analysis?

Firstly, the goal is different. PCA has as a goal to define new variables based on the highest variance explained and so forth. FA has as a goal to define new variables that we can understand and interpret in a business / practical manner.

**What is KMO and Bartlett’s test?**

KMO and Bartlett’s test. This table shows two tests that indicate the suitability of your data for structure detection. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy is a statistic that indicates the proportion of variance in your variables that might be caused by underlying factors.

**What is PC1 and PC2 and PC3?**

By definition PC is a profit measure in your P&L: revenues – costs. By default, PC1 is above PC2, which is above PC3. As such PC3 typically is the lowest margin of all 3 as it includes all expenses down to PC3 which are also included within PC1 and PC2.