dtComb (version 1.0.7)

mlComb: 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"),
  show.result = FALSE,
  ...
)

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.

Arguments

markers

a numeric data frame that includes two diagnostic tests results

status

a factor vector that includes the actual disease status of the patients

event

a character string that indicates the event in the status to be considered as positive event

method

a character string specifying the method used for combining the markers. For the available methods see availableMethods()

IMPORTANT: See https://topepo.github.io/caret/available-models.html for further information about the methods used in this function.

resample

a character string that indicates the resampling method used while training the model. The available methods are "boot", "boot632", "optimism_boot", "boot_all", "cv", "repeatedcv", "LOOCV", "LGOCV", "none", "oob", "adaptive_cv", "adaptive_boot" and "adaptive_LGOCV". for details of these resampling methods see ?caret::trainControl

niters

a numeric value that indicates the number of bootstrapped resampling iterations (10, default)

nfolds

a numeric value that indicates the number of folds for cross validation based resampling methods (5, default)

nrepeats

a numeric value that indicates the number of repeats for "repeatedcv" option of resampling methods (3, default)

preProcess

a character string that indicates the pre-processing options to be applied in the data before training the model. Available pre-processing methods are: "BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "medianImpute", "pca", "ica", "spatialSign", "corr", "zv", "nzv", and "conditionalX". For detailed information about the methods see ?caret::preProcess

show.plot

a logical. If TRUE, a ROC curve is plotted. Default is TRUE

B

a numeric value that is the number of bootstrap samples for bagging classifiers, "bagFDA", "bagFDAGCV", "bagEarth" and "bagEarthGCV". (25, default)

direction

a character string determines in which direction the comparison will be made. ">": if the predictor values for the control group are higher than the values of the case group (controls > cases). "<": if the predictor values for the control group are lower or equal than the values of the case group (controls < cases).

conf.level

a numeric value to determine the confidence interval for the ROC curve(0.95, default).

cutoff.method

a character string determines the cutoff method for the ROC curve.

show.result

a logical string indicating whether the results should be printed to the console.

...

optional arguments passed to selected classifiers.

Author

Serra Ilayda Yerlitas, Serra Bersan Gengec, Necla Kochan, Gozde Erturk Zararsiz, Selcuk Korkmaz, Gokmen Zararsiz

Examples

# call data
data(laparotomy)

# define the function parameters
markers <- laparotomy[, -1]
status <- factor(laparotomy$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"
)