mcca (version 0.7.0)

hum: Calculate HUM Value

Description

compute the Hypervolume Under Manifold (HUM) value of two or three or four categories classifiers with an option to define the specific model or user-defined model.

Usage

hum(y, d, method="multinom", …)

Arguments

y

The multinomial response vector with two, three or four categories. It can be factor or integer-valued.

d

The set of candidate markers, including one or more columns. Can be a data frame or a matrix; if the method is "prob", then d should be the probablity matrix.

method

Specifies what method is used to construct the classifier based on the marker set in d. Available option includes the following methods:"multinom": Multinomial Logistic Regression which is the default method, requiring R package nnet;"tree": Classification Tree method, requiring R package rpart;"svm": Support Vector Machine (C-classification and radial basis as default), requiring R package e1071;"lda": Linear Discriminant Analysis, requiring R package lda;"prob": d is a risk matrix resulted from any external classification algorithm obtained by the user.

…

Additional arguments in the chosen method's function.

Value

The HUM value of the classification using a particular learning method on a set of marker(s).

Details

The function returns the HUM value for predictive markers based on a user-chosen machine learning method. Currently available methods include logistic regression (default), tree, lda, svm and user-computed risk values. For binary outcome, one can use AUC value (HUM reduces to AUC in such case). This function is more general than the package HUM, since we can evaluate the accuracy for marker combinations resulted from complicated classification algorithms.

References

Li, J., Gao, M., D<U+2019>Agostino, R. (2019). Evaluating Classification Accuracy for Modern Learning Approaches. Statistics in Medicine (Tutorials in Biostatistics). 38(13): 2477-2503.

Li, J. and Fine, J. P. (2008): ROC analysis with multiple tests and multiple classes: methodology and applications in microarray studies. Biostatistics. 9 (3): 566-576.

Li, J., Chow, Y., Wong, W.K., and Wong, T.Y. (2014). Sorting Multiple Classes in Multi-dimensional ROC Analysis: Parametric and Nonparametric Approaches. Biomarkers. 19(1): 1-8.

See Also

pdi

Examples

# NOT RUN {
str(iris)
data <- iris[, 1:4]
label <- iris[, 5]
hum(y = label, d = data,method = "multinom")
## [1] 0.9972
hum(y = label, d = data,method = "svm")
## [1] 0.9964
hum(y = label, d = data,method = "svm",type="C",kernel="linear",cost=4,scale=TRUE)
## [1] 0.9972
hum(y = label, d = data, method = "tree")
## [1] 0.998

data <- data.matrix(iris[, 1:4])
label <- as.numeric(iris[, 5])
# multinomial
require(nnet)
# model
fit <- multinom(label ~ data, maxit = 1000, MaxNWts = 2000)
predict.probs <- predict(fit, type = "probs")
pp<- data.frame(predict.probs)
# extract the probablity assessment vector
head(pp)
hum(y = label, d = pp, method = "prob")
## [1] 0.9972

table(mtcars$carb)
for (i in (1:length(mtcars$carb))) {
  if (mtcars$carb[i] == 3 | mtcars$carb[i] == 6 | mtcars$carb[i] == 8) {
    mtcars$carb_new[i] = 9
  }else{
    mtcars$carb_new[i] = mtcars$carb[i]
  }
}
data <- data.matrix(mtcars[, c(1:10)])
mtcars$carb_new <- factor(mtcars$carb_new)
label <- mtcars$carb_new
str(mtcars)

hum(y = label, d = data, method = "tree",control = rpart::rpart.control(minsplit = 5))
## [1] 1
hum(y = label, d = data, method = "svm",kernel="linear",cost=0.7,scale=TRUE)
## [1] 1
hum(y = label, d = data, method = "svm", kernel ="radial",cost=0.7,scale=TRUE)
## [1] 0.53
# }