cppcor: Probabilistic Composition of Correlated Preference

In cppcor: Probabilistic Composition of Correlated Preference

Description

cppcor function that returns the confusion matrix and parameters of classification analysis

Usage

cppcor(dataset, ID = FALSE, cores = 1)

Arguments

dataset	Data frame object
ID	Logical argument, TRUE or FALSE. The default is FALSE
cores	The number of cores to use for parallel execution. The default is 1.

Details

The dataset argument must be a data frame object, and the last column must be the classes of the evaluated elements. The ID argument must be FALSE if the data are correlated and TRUE if the data are independents.

Value

cppcor return the confusion matriz and parameters of classification analysis

Examples

# Seed
set.seed(10)
c1 <- matrix(rnorm(30, mean = c(70,80,90), sd = 30), 10, 3, byrow = TRUE)
c2 <- matrix(rnorm(45, mean = c(30,40,50), sd = 10), 15, 3, byrow = TRUE)
# Data set
dataset <- as.data.frame(cbind(rbind(c1,c2), c(rep(1, 10), rep(2, 15))))
colnames(dataset) <- c("Var1", "Var2", "Var3", "Class")
# Loading package
library(cppcor)
cppcor(dataset, ID = FALSE)

cppcor documentation built on April 1, 2020, 1:08 a.m.