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business-science
correlationfunnel:Speed Up Exploratory Data Analysis (EDA) with the Correlation Funnel
Speeds up exploratory data analysis (EDA) by providing a succinct workflow and interactive visualization tools for understanding which features have relationships to target (response). Uses binary correlation analysis to determine relationship. Default correlation method is the Pearson method. Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.
Maintained by Matt Dancho. Last updated 1 years ago.
correlationexploratory-analysisexploratory-data-analysisexploratory-data-visualizationstidyverse
137 stars 7.20 score 115 scripts
annennenne
PCADSC:Tools for Principal Component Analysis-Based Data Structure Comparisons
A suite of non-parametric, visual tools for assessing differences in data structures for two datasets that contain different observations of the same variables. These tools are all based on Principal Component Analysis (PCA) and thus effectively address differences in the structures of the covariance matrices of the two datasets. The PCADSC tools consist of easy-to-use, intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue (CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The angle plot presents the information loss when moving from the PCA decomposition of one dataset to the PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby presenting the relative weighting and importance of the variables from the original dataset.
Maintained by Anne Helby Petersen. Last updated 3 years ago.
data-structuresexploratory-data-visualizationsprincipal-component-analysis
1 stars 3.11 score 13 scripts