Generate data-driven targets for fused ridge estimation

Generates a list of (data-driven) targets to use in fused ridge estimation. Simply a wrapper for default.target.

default.target.fused(Slist, ns, type = "DAIE", by, ...)

Arguments

Slist

A list of length \(K\) of numeric covariance matrices of the same size for \(K\) classes.

ns

A numeric vector of sample sizes corresponding to the entries of Slist.

type

A character giving the choice of target to construct. See default.target for the available options. Default is "DAIE".

by

A character vector with the same length as Slist specifying which groups should share target. For each unique entry of by a target is constructed. If omitted, the default is to assign a unique target to each class. If not given as a character coercion into one is attempted.

...

Arguments passed to default.target.

Value

A list of \(K\) covariance target matrices of the same size.

See also

default.target

Author

Anders E. Bilgrau, Carel F.W. Peeters <carel.peeters@wur.nl>, Wessel N. van Wieringen

Examples

# Make some toy data
ns <- c(3, 4)  # Two classes with sample size 3 and 4
Slist <- createS(ns, p = 3)  # Generate two 3-dimensional covariance matrices
Slist
#> $class1
#>            A          B          C
#> A  0.5350243  0.7195370 -0.5558835
#> B  0.7195370  1.5885096 -0.4801166
#> C -0.5558835 -0.4801166  0.6927923
#> 
#> $class2
#>            A          B         C
#> A 0.85764628 0.03205939 0.4239914
#> B 0.03205939 0.09458489 0.2146714
#> C 0.42399139 0.21467137 0.8111733
#> 

# Different choices:
default.target.fused(Slist, ns)
#> $class1
#>          A        B        C
#> A 1.114223 0.000000 0.000000
#> B 0.000000 1.114223 0.000000
#> C 0.000000 0.000000 1.114223
#> 
#> $class2
#>         A       B       C
#> A 14.6301  0.0000  0.0000
#> B  0.0000 14.6301  0.0000
#> C  0.0000  0.0000 14.6301
#> 
default.target.fused(Slist, ns, by = seq_along(Slist)) # The same as before
#> $class1
#>          A        B        C
#> A 1.114223 0.000000 0.000000
#> B 0.000000 1.114223 0.000000
#> C 0.000000 0.000000 1.114223
#> 
#> $class2
#>         A       B       C
#> A 14.6301  0.0000  0.0000
#> B  0.0000 14.6301  0.0000
#> C  0.0000  0.0000 14.6301
#> 
default.target.fused(Slist, ns, type = "Null")
#> $class1
#>   A B C
#> A 0 0 0
#> B 0 0 0
#> C 0 0 0
#> 
#> $class2
#>   A B C
#> A 0 0 0
#> B 0 0 0
#> C 0 0 0
#> 
default.target.fused(Slist, ns, type = "DAPV")
#> $class1
#>         A       B       C
#> A 1.31401 0.00000 0.00000
#> B 0.00000 1.31401 0.00000
#> C 0.00000 0.00000 1.31401
#> 
#> $class2
#>          A        B        C
#> A 4.323759 0.000000 0.000000
#> B 0.000000 4.323759 0.000000
#> C 0.000000 0.000000 4.323759
#> 
default.target.fused(Slist, ns, type = "DAPV", by = rep(1, length(Slist)))
#> $class1
#>         A       B       C
#> A 1.35532 0.00000 0.00000
#> B 0.00000 1.35532 0.00000
#> C 0.00000 0.00000 1.35532
#> 
#> $class2
#>         A       B       C
#> A 1.35532 0.00000 0.00000
#> B 0.00000 1.35532 0.00000
#> C 0.00000 0.00000 1.35532
#> 


# Make some (more) toy data
ns <- c(3, 4, 6, 7)  # Two classes with sample size 3 and 4
Slist <- createS(ns, p = 2)  # Generate four 2-dimensional covariance matrices

# Use the same target in class 1 and 2, but another in class 3 and 4:
default.target.fused(Slist, ns, by = c("A", "A", "B", "B"))
#> $class1
#>          A        B
#> A 2.935609 0.000000
#> B 0.000000 2.935609
#> 
#> $class2
#>          A        B
#> A 2.935609 0.000000
#> B 0.000000 2.935609
#> 
#> $class3
#>          A        B
#> A 1.029498 0.000000
#> B 0.000000 1.029498
#> 
#> $class4
#>          A        B
#> A 1.029498 0.000000
#> B 0.000000 1.029498
#>