mcca (version 0.7.0)

ccp: Calculate CCP Value

Description

compute the Correct Classification Percentage (CCP) value of two or three or four categories classifiers with an option to define the specific model or user-defined model.

Usage

ccp(y, d, method="multinom", …)

Arguments

y

The multinomial response vector with two, three or four categories.

d

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

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; "label": d is a label vector resulted from any external classification algorithm obtained by the user, should be encoded from 1; "prob": d is a probability matrix resulted from any external classification algorithm obtained by the user.

…

Additional arguments in the chosen method's function.

Value

Returns an object of class "mcca.ccp". The CCP value of the classification using a particular learning method on a set of marker(s).

An object of class "mcca.ccp" is a list containing at least the following components:

call

the matched call.

measure

the value of measure.

table

the category-specific value of measure.

Details

The function returns the CCP 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. This function is general 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., Jiang, B. and Fine, J. P. (2013). Multicategory reclassification statistics for assessing Improvements in diagnostic accuracy. Biostatistics. 14(2): 382-394.

Li, J., Jiang, B., and Fine, J. P. (2013). Letter to Editor: Response. Biostatistics. 14(4): 809-810.

See Also

pdi

Examples

# NOT RUN {
str(iris)
data <- iris[, 1:4]
label <- iris[, 5]
ccp(y = label, d = data, method = "multinom",maxit = 1000,MaxNWts = 2000,trace=FALSE)

## Call:
## ccp(y = label, d = data, method = "multinom", maxit = 1000,
##     MaxNWts = 2000, trace = FALSE)

## Overall Correct Classification Probability:
##  0.9866667

## Category-specific Correct Classification Probability:
##   CATEGORIES VALUES PREVALENCE
## 1     setosa   1.00  0.3333333
## 2 versicolor   0.98  0.3333333
## 3  virginica   0.98  0.3333333

ccp(y = label, d = data, method = "multinom")
ccp(y = label, d = data, method = "svm")
ccp(y = label, d = data, method = "svm",kernel="sigmoid",cost=4,scale=TRUE,coef0=0.5)
ccp(y = label, d = data, method = "tree")

p = as.numeric(label)
ccp(y = label, d = p, method = "label")


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)])
mtcars$carb_new <- factor(mtcars$carb_new)
label <- mtcars$carb_new
str(mtcars)
ccp(y = as.numeric(label), d = data, method = "svm",kernel="radial",cost=1,scale=TRUE)

## Call:
## ccp(y = as.numeric(label), d = data, method = "svm", kernel = "radial", cost = 1, scale = TRUE)

## Overall Correct Classification Probability:
##  0.4375

## Category-specific Correct Classification Probability:
##   CATEGORIES    VALUES PREVALENCE
## 1          1 0.5714286    0.21875
## 2          2 0.2000000    0.31250
## 3          3 0.8000000    0.31250
## 4          4 0.0000000    0.15625

# }