mcca (version 0.7.0)

pdi: Calculate PDI Value

Description

compute the Polytomous Discrimination Index (PDI) value of two or three or four categories classifiers with an option to define the specific model or user-defined model.

Usage

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

An object of class "mcca.pdi" 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 PDI 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.

Van Calster B, Vergouwe Y, Looman CWN, Van Belle V, Timmerman D and Steyerberg EW. Assessing the discriminative ability of risk models for more than two outcome categories. European Journal of Epidemiology 2012; 27: 761 C 770.

Li, J., Feng, Q., Fine, J.P., Pencina, M.J., Van Calster, B. (2018). Nonparametric estimation and inference for polytomous discrimination index. Statistical Methods in Medical Research. 27(10): 3092<U+2014>3103.

See Also

hum

Examples

# NOT RUN {
str(iris)
data <- iris[, 3]
label <- iris[, 5]
pdi(y = label, d = data,method = "multinom")

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

## Overall Polytomous Discrimination Index:
##  0.9845333

## Category-specific Polytomous Discrimination Index:
##   CATEGORIES VALUES
## 1          1 1.0000
## 2          2 0.9768
## 3          3 0.9768

pdi(y = label, d = data,method = "tree")
pdi(y = label, d = data,method = "tree",control = rpart::rpart.control(minsplit = 200))

data <- data.matrix(iris[, 3])
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)
pdi(y = label, d = pp, method = "prob")
# }