Creates a configuration object for computing dissimilarity based on
Mahalanobis distance in PLS score space. Requires Yr in
dissimilarity().
Usage
diss_pls(
ncomp = ncomp_by_opc(),
method = c("pls", "mpls"),
scale = FALSE,
return_projection = FALSE
)Arguments
- ncomp
Component selection method. Can be:
A positive integer for a fixed number of components
ncomp_fixed(n): explicit fixed selectionncomp_by_var(min_var): retain components explaining at leastmin_varvariance eachncomp_by_cumvar(min_cumvar): retain components until cumulative variance reachesmin_cumvarncomp_by_opc(): optimize using side information (default; recommended for PLS sinceYris already required)
- method
Character. PLS algorithm:
"pls"(default) or"mpls"(modified PLS, Shenk & Westerhaus 1991).- scale
Logical. Scale data? Default
FALSE. Note: PLS always centers internally.- return_projection
Logical. Return projection object? Default
FALSE.
See also
Component selection: ncomp_by_var, ncomp_by_cumvar,
ncomp_by_opc, ncomp_fixed
Other dissimilarity methods: diss_pca,
diss_correlation, diss_euclidean,
diss_cosine, diss_mahalanobis
Examples
# Default: OPC optimization (recommended)
diss_pls()
#> Dissimilarity: PLS
#> method : pls
#> ncomp :
#> scale : FALSE
#> return_projection : FALSE
# Fixed number of components
diss_pls(ncomp = 15)
#> Dissimilarity: PLS
#> method : pls
#> ncomp : fixed: 15
#> scale : FALSE
#> return_projection : FALSE
# Custom opc settings
diss_pls(ncomp = ncomp_by_opc(max_ncomp = 50))
#> Dissimilarity: PLS
#> method : pls
#> ncomp :
#> scale : FALSE
#> return_projection : FALSE
# Modified PLS
diss_pls(ncomp = 10, method = "mpls")
#> Dissimilarity: PLS
#> method : mpls
#> ncomp : fixed: 10
#> scale : FALSE
#> return_projection : FALSE
