| Title: | Classification Measures when Subclasses are Involved |
| Version: | 0.1.0 |
| Description: | Accuracy metrics are commonly used to assess the discriminating ability of diagnostic tests or biomarkers. Among them, metrics based on the ROC framework are particularly popular. When classification involves subclasses, the package 'CompClassMetrics' includes functions that can provide the point estimate, confidence interval as well as true values if a parametric setting is known. For more details see Nan and Tian (2025) <doi:10.1177/09622802251343600> and Nan and Tian (2023) <doi:10.1002/sim.9908> and Feng and Tian (2020) <doi:10.1177/0962280220938077>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | plot3D, pracma, cubature, stats |
| NeedsCompilation: | no |
| Packaged: | 2025-08-29 16:40:34 UTC; nannan |
| Author: | Nan Nan [aut, cre] |
| Maintainer: | Nan Nan <nannan@buffalo.edu> |
| Depends: | R (≥ 3.5.0) |
| Repository: | CRAN |
| Date/Publication: | 2025-09-03 21:00:20 UTC |
R function that calculates the true values of AUCo when distribution is known
Description
R function that calculates the true values of AUCo when distribution is known
Usage
AUCofunc(k1, k2, distribution, arg1, arg2)
Arguments
k1 |
number of subclasses in main class-1 |
k2 |
number of subclasses in main class-2 |
distribution |
the distribution of marker value follows Normal or Gamma |
arg1 |
if distribution is normal input mean parameters of all subclasses in a vector, if gamma input shape parameters |
arg2 |
if distribution is gamma input variance parameter, if gamma input rate parameters |
Value
The true value of AUCo under given distribution and parameters
R function that calculates percentile confidence interval given an array of estimates
Description
This function provides percentile confidence interval
Usage
CI.func(x)
Arguments
x |
an array of calculated estimates |
Value
The percentile confidence interval of given values
R function that calculates the true values of VUSC when distribution is known
Description
R function that calculates the true values of VUSC when distribution is known
Usage
CVUS.calc.func(k1, k2, k3, distribution, arg1, arg2)
Arguments
k1 |
number of subclasses in main class-1 |
k2 |
number of subclasses in main class-2 |
k3 |
number of subclasses in main class-3 |
distribution |
the distribution of marker value follows Normal or Gamma |
arg1 |
if distribution is normal input mean parameters of all subclasses in a vector, if gamma input shape parameters |
arg2 |
if distribution is gamma input variance parameter, if gamma input rate parameters |
Value
The true value of VUSc under given distribution and parameters
R function that calculates the conditional probability of minimum greater than y_min given maximum equals to y_max of gamma random variables (upper tail of conditional probability of minimum given maximum)
Description
R function that calculates the conditional probability of minimum greater than y_min given maximum equals to y_max of gamma random variables (upper tail of conditional probability of minimum given maximum)
Usage
F_min_given_max_partial_gamma_upper(y_min, y_max, shape, rate)
Arguments
y_min |
the value of y_min |
y_max |
the value of y_max |
shape |
the vector of shape parameters of gamma random variables |
rate |
the vector of rate parameters of gamma random variables |
Value
The conditional probability of minimum given maximum of gamma random variables
R function that calculates the conditional probability of minimum greater than y_min given maximum equals to y_max of normal random variables (upper tail probability of minimum given maximum)
Description
R function that calculates the conditional probability of minimum greater than y_min given maximum equals to y_max of normal random variables (upper tail probability of minimum given maximum)
Usage
F_min_given_max_partial_normal_upper(y_min, y_max, mu, sd)
Arguments
y_min |
the value of y_min |
y_max |
the value of y_max |
mu |
the vector of mean parameters of normal random variables |
sd |
the vector of variance parameters of normal random variables |
Value
The conditional probability of minimum given maximum of normal random variables
R function that calculates the partial of joint probability of min and max over max of NIND gamma random variables
Description
R function that calculates the partial of joint probability of min and max over max of NIND gamma random variables
Usage
F_min_max_partial_gamma(y_min, y_max, shape, rate)
Arguments
y_min |
the value of y_min |
y_max |
the value of y_max |
shape |
the vector of shape parameters of gamma random variables |
rate |
the vector of rate parameters of gamma random variables |
Value
The partial of joint probablity of min and max over max
R function that calculates the partial of joint probability of min and max over max of NIND normal random variables
Description
R function that calculates the partial of joint probability of min and max over max of NIND normal random variables
Usage
F_min_max_partial_normal(y_min, y_max, mu, sd)
Arguments
y_min |
the value of y_min |
y_max |
the value of y_max |
mu |
the vector of mean parameters of normal random variables |
sd |
the vector of variance parameters of normal random variables |
Value
The partial of joint probablity of min and max over max
R function that calculates the probability of r-th order statistics of gamma random variables (CDF of r-th order statistics)
Description
R function that calculates the probability of r-th order statistics of gamma random variables (CDF of r-th order statistics)
Usage
F_order_r_gamma(x, shape, rate, r)
Arguments
x |
the value of x |
shape |
the vector of shape parameters of gamma random variables |
rate |
the vector of rate parameters of gamma random variables |
r |
r-th order statistics |
Value
The probability of r-th order statistics of gamma random variables smaller or equal to x
R function that calculates the probability of r-th order statistics of normal random variables (CDF of r-th order statistics)
Description
R function that calculates the probability of r-th order statistics of normal random variables (CDF of r-th order statistics)
Usage
F_order_r_normal(x, mu, sd, r)
Arguments
x |
the value of x |
mu |
the vector of mean parameters of normal random variables |
sd |
the vector of variance parameters of normal random variables |
r |
r-th order statistics |
Value
The probability of r-th order statistics of normal random variables smaller or equal to x
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)
R function that calculates the true values of HUMcm when distribution is known
Description
R function that calculates the true values of HUMcm when distribution is known
Usage
HUMC_fourclass(distribution, arg1, arg2, num_sub)
Arguments
distribution |
the distribution of marker value follows Normal or Gamma |
arg1 |
if distribution is normal input mean parameters of all subclasses in a vector, if gamma input shape parameters |
arg2 |
if distribution is gamma input variance parameter, if gamma input rate parameters |
num_sub |
the vector of number of subclasses in each main class |
Value
The true value of HUMcm under given distribution and parameters
R function that calculates the minimum of HUMcm under given structure
Description
R function that calculates the minimum of HUMcm under given structure
Usage
HUM_min(num_sub)
Arguments
num_sub |
the vector of number of subclasses in each main class |
Value
The minimum of HUMcm
R function to calculate the standardized HUMcm under given structure
Description
R function to calculate the standardized HUMcm under given structure
Usage
HUM_standard(value, num_sub)
Arguments
value |
the value of HUMcm |
num_sub |
the vector of number of subclasses in each main class |
Value
The standardized HUMcm
PLCO
Description
Description of PLCO.
Format
A data frame with 239 rows and 7 columns:
- ID
Participant ID
- Group
The disease class label
- CA125
Numeric, value of CA125
- CA153
Numeric, value of CA153
- CA199
Numeric, value of CA199
- KLK6
Numeric, value of KLK6
- CA724
Numeric, value of CA724
Source
This is a subset of PLCO dataset, available at https://edrn.nci.nih.gov.
R function for plotting the compound ROC surface and chance surface
Description
R function for plotting the compound ROC surface and chance surface
Usage
ROCC_Surface(k1, k2, k3, distribution, arg1, arg2)
Arguments
k1 |
number of subclasses in main class-1 |
k2 |
number of subclasses in main class-2 |
k3 |
number of subclasses in main class-3 |
distribution |
the distribution of marker value follows Normal or Gamma |
arg1 |
if distribution is normal input mean parameters of all subclasses in a vector, if gamma input shape parameters |
arg2 |
if distribution is gamma input variance parameter, if gamma input rate parameters |
Value
The compound ROC surface and chance surface
R function for plotting the overall ROC curve and chance curve
Description
R function for plotting the overall ROC curve and chance curve
Usage
ROCC_curve(k1, k2, distribution, arg1, arg2)
Arguments
k1 |
number of subclasses in main class-1 |
k2 |
number of subclasses in main class-2 |
distribution |
the distribution of marker value follows Normal or Gamma |
arg1 |
if distribution is normal input mean parameters of all subclasses in a vector, if gamma input shape parameters |
arg2 |
if distribution is gamma input variance parameter, if gamma input rate parameters |
Value
The overall ROC curve and chance curve
adni2
Description
Description of adni2.
Format
A data frame with 317 rows and 7 columns:
- RID
Participant ID
- DX.bl
The disease class label
- FDG
Numeric, value of FDG
- AV45
Numeric, value of AV45
- ABETA
Numeric, value of ABETA
- TAU.x
Numeric, value of TAU from CSF
- PTAU
Numeric, value of PTAU from CSF
Source
This is a subset of ADNI2 dataset, available at https://adni.loni.usc.edu
R function that calculates the probability density of maximum of gamma random variables (PDF)
Description
R function that calculates the probability density of maximum of gamma random variables (PDF)
Usage
f_order_max_gamma(y_max, shape, rate)
Arguments
y_max |
the value of y_max |
shape |
the vector of shape parameters of gamma random variables |
rate |
the vector of rate parameters of gamma random variables |
Value
The probability density of maximum of gamma random variables
R function that calculates the probability density of maximum of NIND normal random variables (PDF)
Description
R function that calculates the probability density of maximum of NIND normal random variables (PDF)
Usage
f_order_max_normal(y_max, mu, sd)
Arguments
y_max |
the value of y_max |
mu |
the vector of mean parameters of normal random variables |
sd |
the vector of variance parameters of normal random variables |
Value
The probability density of maximum of normal random variables
R function that calculates the probability density of minimum of gamma random variables (PDF)
Description
R function that calculates the probability density of minimum of gamma random variables (PDF)
Usage
f_order_min_gamma(y_min, shape, rate)
Arguments
y_min |
the value of y_min |
shape |
the vector of shape parameters of gamma random variables |
rate |
the vector of rate parameters of gamma random variables |
Value
The probability density of minimum of gamma random variables
R function that calculates the probability density of minimum of NIND normal random variables (PDF)
Description
R function that calculates the probability density of minimum of NIND normal random variables (PDF)
Usage
f_order_min_normal(y_min, mu, sd)
Arguments
y_min |
the value of y_min |
mu |
the vector of mean parameters of normal random variables |
sd |
the vector of variance parameters of normal random variables |
Value
The probability density of minimum of normal random variables
R function for obtaining all combinations of maximum and minimum of a given dataset
Description
R function for obtaining all combinations of maximum and minimum of a given dataset
Usage
get_max_min_permutations(df)
Arguments
df |
Given dataset, in list form |
Value
A list of all combinations of maximum and minimum of df
R function that calculates empirical estimates of HUMcm
Description
This function provides empirical estimates of HUMcm
Usage
hum.dynamic(dat, num_sub)
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 |
Value
The empirical estimate of HUMcm based on given data and num_sub
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 HUMcm of Y.dat and num_sub
hum.dynamic(Y.dat,num_sub)