principal component analysis

This app presents linguistic data over several tabs. It combines the R Markdown-based user interface of Flexdashboard with a Shiny back-end that lets users download the data they select as CSV files and the plots as PNG images. One of the hardest nuts to crack was changing the orientation of rows and columns without breaking the reactable tables. Flexdashboard also made it possible to use quite different formats in different tabs.

Part of the toolkit of language researchers is formed of stimuli that have been rated on various dimensions. The current study presents modality exclusivity norms for 336 properties and 411 concepts in Dutch. Forty-two respondents rated the auditory, …

Principal Component Analysis (PCA) is a technique used to find the core components that underlie different variables. It comes in very useful whenever doubts arise about the true origin of three or more variables. There are two main methods for performing a PCA: naive or less naive. In the naive method, you first check some conditions in your data which will determine the essentials of the analysis. In the less-naive method, you set those yourself based on whatever prior information or purposes you had. The ‘naive’ approach is characterized by a first stage that checks whether the PCA should actually be performed with your current variables, or if some should be removed. The variables that are accepted are taken to a second stage which identifies the number of principal components that seem to underlie your set of variables.