mlComb {dtComb} | R Documentation |
Combine two diagnostic tests with Machine Learning Algorithms.
Description
The mlComb
function calculates the combination
scores of two diagnostic tests selected among several Machine Learning
Algorithms
Usage
mlComb(
markers = NULL,
status = NULL,
event = NULL,
method = NULL,
resample = NULL,
niters = 5,
nfolds = 5,
nrepeats = 3,
preProcess = NULL,
show.plot = TRUE,
B = 25,
direction = c("auto", "<", ">"),
conf.level = 0.95,
cutoff.method = c("CB", "MCT", "MinValueSp", "MinValueSe", "ValueSp", "ValueSe",
"MinValueSpSe", "MaxSp", "MaxSe", "MaxSpSe", "MaxProdSpSe", "ROC01", "SpEqualSe",
"Youden", "MaxEfficiency", "Minimax", "MaxDOR", "MaxKappa", "MinValueNPV",
"MinValuePPV", "ValueNPV", "ValuePPV", "MinValueNPVPPV", "PROC01", "NPVEqualPPV",
"MaxNPVPPV", "MaxSumNPVPPV", "MaxProdNPVPPV", "ValueDLR.Negative",
"ValueDLR.Positive", "MinPvalue", "ObservedPrev", "MeanPrev", "PrevalenceMatching"),
...
)
Arguments
markers |
a |
status |
a |
event |
a |
method |
a IMPORTANT: See https://topepo.github.io/caret/available-models.html for further information about the methods used in this function. |
resample |
a |
niters |
a |
nfolds |
a |
nrepeats |
a |
preProcess |
a |
show.plot |
a |
B |
a |
direction |
a |
conf.level |
a |
cutoff.method |
a |
... |
optional arguments passed to selected classifiers. |
Value
A list
of AUC values, diagnostic statistics,
coordinates of the ROC curve for the combination score obtained using
Machine Learning Algorithms as well as the given biomarkers individually, a
comparison table for the AUC values of individual biomarkers and combination
score obtained and the fitted model.
Author(s)
Serra Ilayda Yerlitas, Serra Bersan Gengec, Necla Kochan, Gozde Erturk Zararsiz, Selcuk Korkmaz, Gokmen Zararsiz
Examples
# call data
data(exampleData1)
# define the function parameters
markers <- exampleData1[, -1]
status <- factor(exampleData1$group, levels = c("not_needed", "needed"))
event <- "needed"
model <- mlComb(
markers = markers, status = status, event = event,
method = "knn", resample = "repeatedcv", nfolds = 10, nrepeats = 5,
preProcess = c("center", "scale"), direction = "<", cutoff.method = "Youden"
)