core_cpp: Maximization of Cross-Validateed Accuracy Methods
In KODAMA: Knowledge Discovery by Accuracy Maximization

core_cpp

R Documentation

Maximization of Cross-Validateed Accuracy Methods

Description

This function performs the maximization of cross-validated accuracy by an iterative process

Usage

core_cpp(x,
         xTdata = NULL, 
         clbest, 
         Tcycle = 20, 
         FUN = c("fastpls","simpls"), 
         f.par.pls = 5, 
         constrain = NULL, 
         fix = NULL)

Arguments

`x`	a matrix.
`xTdata`	a matrix for projections. This matrix contains samples that are not used for the maximization of the cross-validated accuracy. Their classification is obtained by predicting samples on the basis of the final classification vector.
`clbest`	a vector to optimize.
`Tcycle`	number of iterative cycles that leads to the maximization of cross-validated accuracy.
`FUN`	classifier to be consider. Choices are "`fastpls`" and "`simpls`".
`f.par.pls`	parameters of the classifier. If the classifier is `KNN`, `fpar` represents the number of neighbours. If the classifier is `PLS-DA`, `fpar` represents the number of components.
`constrain`	a vector of `nrow(data)` elements. Supervised constraints can be imposed by linking some samples in such a way that if one of them is changed, all other linked samples change in the same way (i.e., they are forced to belong to the same class) during the maximization of the cross-validation accuracy procedure. Samples with the same identifying constrain will be forced to stay together.
`fix`	a vector of `nrow(data)` elements. The values of this vector must be `TRUE` or `FALSE`. By default all elements are `FALSE`. Samples with the `TRUE` fix value will not change the class label defined in `W` during the maximization of the cross-validation accuracy procedure. For more information refer to Cacciatore, et al. (2014).

Value

The function returns a list with 3 items:

`clbest`	a classification vector with a maximized cross-validated accuracy.
`accbest`	the maximum cross-validated accuracy achieved.
`vect_acc`	a vector of all cross-validated accuracies obtained.
`vect_proj`	a prediction of samples in `xTdata` matrix using the vector clbest. This output is present only if `xTdata` is not `NULL`.

Author(s)

Stefano Cacciatore and Leonardo Tenori

References

Abdel-Shafy EA, Kassim M, Vignol A, et al.
KODAMA enables self-guided weakly supervised learning in spatial transcriptomics.
bioRxiv 2025. doi: 10.1101/2025.05.28.656544. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1101/2025.05.28.656544")}

Cacciatore S, Luchinat C, Tenori L
Knowledge discovery by accuracy maximization.
Proc Natl Acad Sci U S A 2014;111(14):5117-5122. doi: 10.1073/pnas.1220873111. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1073/pnas.1220873111")}

Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA
KODAMA: an updated R package for knowledge discovery and data mining.
Bioinformatics 2017;33(4):621-623. doi: 10.1093/bioinformatics/btw705. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bioinformatics/btw705")}

Examples

# Here, the famous (Fisher's or Anderson's) iris data set was loaded
data(iris)
u=as.matrix(iris[,-5])
s=sample(1:150,150,TRUE)

# The maximization of the accuracy of the vector s is performed
results=core_cpp(u, clbest=s,f.par.pls  = 4)


print(as.numeric(results$clbest))

KODAMA documentation built on June 8, 2025, 11:53 a.m.