This is a wrapper for the DISCOsca function by Zhengguo Gu for computing DISCO.
Value
multiblock object including relevant scores and loadings. Relevant plotting functions: multiblock_plots
and result functions: multiblock_results.
Details
DISCO is a restriction of SCA where Alternating Least Squares is used for estimation of loadings and scores. The SCA solution is rotated towards loadings (in sample linked mode) which are filled with zeros in a pattern resembling distinct, local and common components. When used in sample linked mode and only selecting distinct components, it shares a resemblance to SO-PLS, only in an unsupervised setting. Explained variances are computed as proportion of block variation explained by scores*loadings'.
References
Schouteden, M., Van Deun, K., Wilderjans, T. F., & Van Mechelen, I. (2014). Performing DISCO-SCA to search for distinctive and common information in linked data. Behavior research methods, 46(2), 576-587.
See also
Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.
Examples
data(potato)
potList <- as.list(potato[c(1,2,9)])
pot.disco <- disco(potList)
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 0
#> [2,] 1 0
#> [3,] 0 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 0
#> [2,] 0 1
#> [3,] 1 0
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 0
#> [2,] 0 1
#> [3,] 0 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 0 1
#> [2,] 1 0
#> [3,] 1 0
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 0 1
#> [2,] 1 0
#> [3,] 0 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 0 1
#> [2,] 0 1
#> [3,] 1 0
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 1 0
#> [3,] 1 0
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 0
#> [2,] 1 1
#> [3,] 1 0
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 0
#> [2,] 1 0
#> [3,] 1 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 1 0
#> [3,] 0 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 0
#> [2,] 1 1
#> [3,] 0 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 0 1
#> [3,] 1 0
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 0
#> [2,] 0 1
#> [3,] 1 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 0 1
#> [3,] 0 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 0 1
#> [2,] 1 1
#> [3,] 1 0
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 0 1
#> [2,] 1 0
#> [3,] 1 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 0 1
#> [2,] 1 1
#> [3,] 0 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 0 1
#> [2,] 0 1
#> [3,] 1 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 1 1
#> [3,] 1 0
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 1 0
#> [3,] 1 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 0
#> [2,] 1 1
#> [3,] 1 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 1 1
#> [3,] 0 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 0 1
#> [3,] 1 1
#> Now checking the following component structure:
#> [,1] [,2]
#> [1,] 0 1
#> [2,] 1 1
#> [3,] 1 1
plot(scores(pot.disco), labels="names")
