mcca (version 0.7.0)

rsq: Calculate RSQ Value

Description

compute the R-squared (RSQ) value of two or three or four categories classifiers with an option to define the specific model or user-defined model.

Usage

rsq(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

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

An object of class "mcca.rsq" 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 RSQ 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

ccp

Examples

# NOT RUN {
str(iris)
data <- iris[, 1:4]
label <- iris[, 5]
rsq(y = label, d = data, method="multinom")

## Call:
## rsq(y = label, d = data, method = "multinom")

## Overall R-squared value:
##  0.9637932

## Category-specific R-squared value:
##   CATEGORIES    VALUES
## 1     setosa 0.9999824
## 2 versicolor 0.9456770
## 3  virginica 0.9457203

rsq(y = label, d = data, method = "tree")


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)
rsq(y = label, d = pp, method = "prob")


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)

rsq(y = label, d = data, method="tree")
rsq(y = label, d = data, method="lda")
rsq(y = label, d = data, method="lda",prior = c(100,1,1,1)/103)

# }