How many observations are needed for PCA?

Traditional recommendations – at least within psychometrics – would be to have at least x observations per variable (with x typically anywhere from 5 to 20) so in any case n≫p.

Why is it important to conduct PCA on standardized variables?

More specifically, the reason why it is critical to perform standardization prior to PCA, is that the latter is quite sensitive regarding the variances of the initial variables. Mathematically, this can be done by subtracting the mean and dividing by the standard deviation for each value of each variable.

Does PCA need dependent variable?

PCA is a technique to account for the variability of the system from the linear combination of independent variables, thus it should not include dependent variables.

Is PCA applicable to categorical data?

While it is technically possible to use PCA on discrete variables, or categorical variables that have been one hot encoded variables, you should not. The only way PCA is a valid method of feature selection is if the most important variables are the ones that happen to have the most variation in them .

What is PCA with varimax rotation?

Change of coordinates used in principal component analysis (PCA) is known as Varimax rotation. It maximizes the sum of the variances of the squared loadings as all the coefficients will be either large or near zero, with few intermediate values. The goal is to associate each variable to at most one factor.

What is the minimum sample size for PCA?

In the context of PCA and FA, some workers have proposed rules of thumb for minimum sample size in relation to number of variables or correlation structure. Gorsuch (1983) recommended at least 100 samples. Hatcher (1994) recommended that the sample size should be larger than five times the number of variables (p).

In which analysis only one component of sample is determined?

In Factor Analysis only one component was extracted.

Is it important to standardize before PCA?

Yes, it is necessary to normalize data before performing PCA. The PCA calculates a new projection of your data set. If you normalize your data, all variables have the same standard deviation, thus all variables have the same weight and your PCA calculates relevant axis.

Why is scaling important before PCA?

The rule of thumb is that if your data is already on a different scale (e.g. every feature is XX per 100 inhabitants), scaling it will remove the information contained in the fact that your features have unequal variances. If the data is on different scales, then you should normalize it before running PCA.