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")

Methods	PCA	BADA	PLSC	MFA
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