HUMC_NPCI: R function that calculates non-parametric bootstrap...

In CompClassMetrics: Classification Measures when Subclasses are Involved

R function that calculates non-parametric bootstrap percentile confidence interval

Description

This function provides non-parametric bootstrap percentile confidence interval of HUMcm

Usage

HUMC_NPCI(dat, num_sub, B)

Arguments

dat	test values in list, each element represents biomarker values for a disease group
num_sub	a vector of number of subclasses in each subclass
B	the number of iteration

Value

The non-parametric bootstrap percentile confidence interval of HUMcm

Examples

# Create a list of example data
Y1 <- c(0.9316, 0.9670, 1.3856, 1.3505, 1.0316, 1.1764, 0.7435, 0.5813, 0.4695, 0.3249)
Y2 <- c(1.63950, 1.36535, 1.79859, 0.47961, 1.50978, 1.36525,0.13515, 2.11275, 0.45659)
Y3 <- c(1.89856, 1.30920, 2.38615, 2.34785, 2.92493, 2.71615, 2.75243, 0.95060, 0.38964)
Y4 <- c(2.580,2.570,2.143,3.079,1.765,3.081,2.175,2.306,2.918,2.507,4.261,3.033,1.836,2.321)
Y5 <- c(3.969,3.044,3.318,2.862,3.655,1.523,3.722,4.074,3.662,3.571,5.177,6.321,4.932,4.129)
Y.dat <- list(Y1,Y2,Y3,Y4,Y5)
num_sub <- c(1,3,1)
# calculate the non-parametric bootstrap percentile confidence interval
HUMC_NPCI(Y.dat,num_sub,50)

CompClassMetrics documentation built on Sept. 9, 2025, 5:45 p.m.