Extract information for plotLoadings splsda

Hi,

I have conducted splsda test and subsequent predictions, where I can see after plotting the loadings plotLoadings there are certain features driving discrimination between multiple outcomes.

I am wondering how I can extract this into a table, where every feature will be connected to outcome it contributes to.

ps. if it was only two outcomes, then it is easier to get this info, as it is usually discriminated along x axis as positive or negative. However, I have here multiple outcomes, and few features are positive for several outcomes, while few negative along x-axis, therefore making in more difficult to discriminate exactly which feature belongs to which outcome. Hope this makes sense.

Thanks

If you look at the documentation for plotLoadings() (via ?plotLoadings), you will see the contrib and method parameters. When this are non-NULL, the returned object is a dataframe. Observe the GroupContrib column to find what you're looking for

data(srbct)
X <- srbct$gene[,1:10]
Y <- srbct$class

model <- splsda(X, Y)
obj <- plotLoadings(model, contrib = "max", method="mean")

obj
#>             EWS          BL          NB         RMS Contrib.EWS Contrib.BL
#> g1   0.45191775 -1.41860883 -0.53145775  0.36661277        TRUE      FALSE
#> g2  -0.42043056 -0.56012905 -0.41195530  0.95471994       FALSE      FALSE
#> g3   0.08367123  0.23803114  0.78169159 -0.66044932       FALSE      FALSE
#> g9  -0.45584880  0.91373648  0.59266341 -0.19686652       FALSE       TRUE
#> g4  -0.31926138  0.19024186  0.78559795 -0.18030493       FALSE      FALSE
#> g8   0.16173375  0.20372641 -0.76198922  0.18970915       FALSE       TRUE
#> g6   0.14843647 -0.38690277 -0.34439310  0.19069503       FALSE      FALSE
#> g7  -0.14465770 -0.51111911 -0.07106862  0.41344517       FALSE      FALSE
#> g5   0.18874901  0.07687763 -0.59904443  0.11161424        TRUE      FALSE
#> g10 -0.26216968  0.87078260 -0.22078014  0.08565018       FALSE       TRUE
#>     Contrib.NB Contrib.RMS Contrib GroupContrib   color importance
#> g1       FALSE       FALSE   FALSE          EWS #388ECC -0.4946755
#> g2       FALSE        TRUE   FALSE          RMS #009E73 -0.4246271
#> g3        TRUE       FALSE   FALSE           NB #C2C2C2  0.4079229
#> g9       FALSE       FALSE   FALSE           BL #F68B33  0.3819278
#> g4        TRUE       FALSE   FALSE           NB #C2C2C2  0.2984552
#> g8       FALSE       FALSE   FALSE           BL #F68B33 -0.2200930
#> g6       FALSE        TRUE   FALSE          RMS #009E73 -0.2111764
#> g7       FALSE        TRUE   FALSE          RMS #009E73 -0.2033517
#> g5       FALSE       FALSE   FALSE          EWS #388ECC -0.1806413
#> g10      FALSE       FALSE   FALSE           BL #F68B33  0.0813818

^{Created on 2022-08-29 by the reprex package (v2.0.1)}

mixOmicsTeam / mixOmics

Extract information for plotLoadings splsda #222