Principal Component Analysis (PCA) is a dimension reduction technique. We obtain a set of factors which summarize, as well as possible, the information available in the data. The factors (or components) are linear combinations of the original variables.

Choosing the right number of factors is a crucial problem in PCA. If we select too much factors, we include noise from the sampling fluctuations in the analysis. If we choose too few factors, we lose relevant information, the analysis is incomplete. Unfortunately, there is not an indisputable approach for the determination of the number of factors. As a rule of thumb, we must select only the interpretable factors, knowing that the choice depends heavily on the domain expertise. And yet, this last one is not always available. We intend precisely to build on the data analysis to get a better knowledge on the studied domain.

In this tutorial, we present various approaches for the determination of the right number of factors for PCA based on the correlation matrix. Some of them, such as the Kaiser-Gutman rule or the scree plot method, are very popular even if they are not really statistically sound; others seems more rigorous, but seldom if ever used because they are not available in the popular statistical software suite.

In a first time, we use Tanagra and the Excel spreadsheet for the implementation of some methods; in a second time, especially for the resampling based approaches, we write programs for R from the results of the princomp() procedure.

**Keywords**: principal component analysis, factor analysis, pca, princomp, R software, bartlett's test of sphericity, xlsx package, scree plot, kaiser-guttman rule, broken-stick method, parallel analysis, randomization, bootstrap, correlation, partial correlation

**Components**: PRINCIPAL COMPONENT ANALYSIS, LINEAR CORRELATION, PARTIAL CORRELATION

**Tutorial**:

en_Tanagra_Nb_Components_PCA.pdf
**Dataset**:

crime_dataset_pca.zip
**References **:

D. Jackson, “Stopping Rules in Principal Components Analysis: A Comparison of Heuristical and Statistical Approaches”, in Ecology, 74(8), pp. 2204-2214, 1993.

P. Neto, D. Jackson, K. Somers, “How Many Principal Components? Stopping Rules for Determining the Number of non-trivial Axes Revisited”, in Computational Statistics & Data Analysis, 49(2005), pp. 974-997, 2004.

Tanagra - "

Principal Component Analysis (PCA)"

Tanagra - "

VARIMAX rotation in Principal Component Analysis"

Tanagra - "

PCA using R - KMO index and Bartlett's test"