7.5 Comparison Table Between PCA, BADA, PLSC, MFA

#use readxl to read from excel files
compare.t <- read_excel("Comparision_table_of_most notable_methods.xlsx") #don't hard code location, 
                                               #just direct to data file by name as long as it is in the same folder
a <- kable(compare.t)                        #taking a peak at our data
scroll_box(a, width = "100%", height = "650")
Find relationships between… Correlation between Variables Correlation between Group Means Covariance between 2 or more Group of Variables Correlation between Multiple Tables (Judges)
How easy to process? (I-IV) I (easiest) III II IV (hardest)
Overestimate Eigenvalues? NO YES NO NO
Can assess Panelist? YES (w/ coloring only) NO YES (w/ coloring only) YES (in high details)
Notable Finding: Row: Pavo VS Viena Relatively good Prediction Basic Tastes can describe other Attributes Panelists mostly agree
Final Notes: Provides good overview but cannot detect non-linear Is the PCA of group means, can predict Explains attributes groups very well Provides a relatively full picture and balance Panelists