core_cpp | R Documentation |
This function performs the maximization of cross-validated accuracy by an iterative process
core_cpp(x, xTdata=NULL, clbest, Tcycle=20, FUN=c("PLS-DA","KNN"), fpar=2, constrain=NULL, fix=NULL, shake=FALSE)
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 " |
fpar |
parameters of the classifier. If the classifier is |
constrain |
a vector of |
fix |
a vector of |
shake |
if |
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 |
Stefano Cacciatore and Leonardo Tenori
Cacciatore S, Luchinat C, Tenori L
Knowledge discovery by accuracy maximization.
Proc Natl Acad Sci U S A 2014;111(14):5117-22. doi: 10.1073/pnas.1220873111. Link
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. Link
KODAMA
# 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,fpar = 10) print(as.numeric(results$clbest))
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