Type:	Package
Title:	ROC Analysis in Three-Class Classification Problems for Clustered Data
Date:	2025-10-01
Version:	1.0.3
Maintainer:	Duc-Khanh To <tdkhanh@hcmus.edu.vn>
Description:	Statistical methods for ROC surface analysis in three-class classification problems for clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for: (i) true class fractions (TCFs) at fixed pairs of thresholds; (ii) the ROC surface; (iii) the volume under ROC surface (VUS); (iv) the optimal pairs of thresholds. Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022) <doi:10.1177/09622802221089029>. Referring to point (iv), three different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018) <doi:10.1177/0962280217742539>. Visualization tools are also provided. We refer readers to the articles cited above for all details.
License:	GPL-3
Depends:	R (≥ 3.5.0), stats, utils, graphics, nlme, Rcpp (≥ 0.12.3)
Imports:	rgl, ellipse, numDeriv, ggplot2, ggpubr, foreach, iterators, parallel, doParallel
LinkingTo:	Rcpp, RcppArmadillo
Encoding:	UTF-8
LazyData:	true
LazyLoad:	yes
ByteCompile:	yes
RoxygenNote:	7.3.3
NeedsCompilation:	yes
URL:	https://github.com/toduckhanh/ClusROC
BugReports:	https://github.com/toduckhanh/ClusROC/issues
Suggests:	testthat (≥ 3.0.0)
Config/testthat/edition:	3
Packaged:	2025-10-01 15:29:07 UTC; duckh
Author:	Duc-Khanh To [aut, cre], Gianfranco Adimari [ctb], Monica Chiogna [ctb]
Repository:	CRAN
Date/Publication:	2025-10-01 17:00:10 UTC

ROC Analysis in Three-Class Classification Problems for Clustered Data

Description

This package implements the techniques for ROC surface analysis, in cases of clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for: (i) true class fractions (TCFs) at fixed pairs of thresholds; (ii) the ROC surface; (iii) the volume under ROC surface (VUS); (iv) the optimal pairs of thresholds. Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022). Referring to point (iv), three different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018). Visualization tools are also provided. We refer readers to the articles cited above for all details.

Details

Major functions are clus_lme, clus_roc_surface, clus_opt_thres3, clus_vus and clus_tcfs.

Author(s)

Duc-Khanh To, with contributions from Gianfranco Adimari and Monica Chiogna

Maintainer: Duc-Khanh To <toduc@stat.unipd.it>

References

Bantis, L. E., Nakas, C. T., Reiser, B., Myall, D., and Dalrymple-Alford, J. C. (2017). Construction of joint confidence regions for the optimal true class fractions of Receiver Operating Characteristic (ROC) surfaces and manifolds. Statistical methods in medical research, 26, 3, 1429-1442.

Gurka, M. J., Edwards, L. J. , Muller, K. E., and Kupper, L. L. (2006). Extending the Box-Cox transformation to the linear mixed model. Journal of the Royal Statistical Society: Series A (Statistics in Society), 169, 2, 273-288.

Gurka, M. J. and Edwards, L. J. (2011). Estimating variance components and random effects using the box-cox transformation in the linear mixed model. Communications in Statistics - Theory and Methods, 40, 3, 515-531.

Kauermann, G. and Carroll, R. J. (2001). A note on the efficiency of sandwich covariance matrix estimation. Journal of the American Statistical Association, 96, 456, 1387-1396.

Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 1, 13-22.

Mancl, L. A. and DeRouen, T. A. (2001). A covariance estimator for GEE with improved small-sample properties. Biometrics, 57, 1, 126-134.

To, D-K., Adimari, G., Chiogna, M. and Risso, D. (2022). Receiver operating characteristic estimation and threshold selection criteria in three-class classification problems for clustered data. Statistical Methods in Medical Research, 7, 31, 1325-1341.

Xiong, C., Luo, J., Chen L., Gao, F., Liu, J., Wang, G., Bateman, R. and Morris, J. C. (2018). Estimating diagnostic accuracy for clustered ordinal diagnostic groups in the three-class case – Application to the early diagnosis of Alzheimer disease. Statistical Methods in Medical Research, 27, 3, 701-714.

A subset of energy choice data in 4 cities of Ethiopia

Description

A subset of energy choice data used in Alem et al. (2016). The authors are used the full dataset to investigate the determinants of household cooking fuel choice and energy transition in urban Ethiopia. A full data is publicly available at doi:10.1016/j.eneco.2016.06.025.

Usage

data(EnergyEthiopia)

Format

A data frame with 2088 observations from 1123 households (or clusters) in the capital Addis Ababa and 9 variables:

uqid

the id of household (which yield 1123 clusters).

energy2

a factor with 3 levels (types) of cooking energy state at each time (2000, 2004, 2009), i.e., 1 (clean fuel only - electricity, gas and kerosene), 2 (a mix of clean and biomass), 3 (biomass fuel only - firewood, charcoal, dung and crop residues).

hhs

household size.

hhs_ft

a factor with 4 levels of household size: small (1 \le hhs \le 4); medium (5 \le hhs \le 8); large ((9 \le hhs \le 12)); very large (hhs \ge 13).

lrconsaeu

log of real consumption per adult equivalent units.

lfirewood_pr

Firewood log price.

lcharcol_pr

Charcoal log price.

lkerosene_pr

Kernosene log price.

lelectric_pr

Electricity log price.

References

Alem, Y., Beyene, A. D., Köhlin, G., & Mekonnen, A. (2016). "Modeling household cooking fuel choice: A panel multinomial logit approach". Energy Economics, 59, 129-137.

A subset of mouse brain cells data

Description

A subset of mouse brain cells data used in To el al. (2022). This is used to evaluate the ability of Lamp5 gene to discriminate three types of glutamatergic neurons. A full data is publicly available at https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-v1-and-alm-smart-seq.

Usage

data(MouseNeurons)

Format

A data frame with 860 observations from 23 clusters and 7 variables:

sample_name

name of each observation.

subclass_label

a factor with 3 levels (types) of glutamatergic neurons, i.e., L2/3 IT (Layer 2/3 Intratelencephalic), L4 (Layer 4) and L5 PT (Layer 5 Pyramidal Tract) neurons.

genotype_id

the mouse genotype (which yield 23 clusters).

sex

the gender of mouse.

age_days

the age of mouse, in days.

Slc17a7_cpm

count per million of Slc17a7 (Solute Carrier Family 17 Member 7) gene expression.

Lamp5_cpm

count per million of Lamp5 (Lysosomal Associated Membrane Protein Family Member 5) gene expression.

References

To, D-K., Adimari, G., Chiogna, M. and Risso, D. (2022) “Receiver operating characteristic estimation and threshold selection criteria in three-class classification problems for clustered data”. Statistical Methods in Medical Research, 7, 31, 1325-1341.

Confidence Intervals for Covariate-specific VUS

Description

Computes confidence intervals for covariate-specific VUS.

Usage

ci_clus_vus(x, ci_level = 0.95)

Arguments

Details

A confidence interval for covariate-specific VUS is given based on normal approximation. If the lower bound (or the upper bound) of the confidence interval is smaller than 0 (or greater than 1), it will be set as 0 (or 1). Also, logit and probit transformations are available if one wants guarantees that confidence limits are inside (0, 1).

Value

ci_clus_vus returns an object of class inheriting from "ci_VUS" class. An object of class "ci_VUS" is a list, containing at least the following components:

See Also

clus_vus

Linear Mixed-Effects Models for a continuous diagnostic test or a biomarker (or a classifier).

Description

clus_lme fits the cluster-effect model for a continuous diagnostic test in a three-class setting as described in Xiong et al. (2018) and To et al. (2022).

Usage

clus_lme(
  fixed_formula,
  name_class,
  name_clust,
  data = sys.frame(sys.parent()),
  subset,
  na_action = na.fail,
  levl_class = NULL,
  ap_var = TRUE,
  boxcox = FALSE,
  interval_lambda = c(-2, 2),
  trace = TRUE,
  ...
)

Arguments

Details

This function fits a linear mixed-effect model for a continuous diagnostic test in a three-class setting in order to account for the cluster and covariates effects on the test result. See Xiong et al. (2018) and To et al. (2022) for more details.

• Estimation is done by using lme with the restricted maximum log-likelihood (REML) method.

• Box-Cox transformation for the model can be used when the distributions of test results are skewed (Gurka et al. 2006). The estimation procedure is described in To et al. (2022). The Box-Cox parameter \lambda is estimated by a grid search on the interval (-2, 2), as discussed in Gurka and Edwards (2011).

• The estimated variance-covariance matrix for the estimated parameters are obtained by sandwich formula (see, Liang and Zeger, 1986; Kauermann and Carroll, 2001; Mancl and DeRouen, 2001) as discussed in To et al. (2022).

Value

clus_lme returns an object of class "clus_lme" class, i.e., a list containing at least the following components:

Generic functions such as print and plot are also used to show results of the fit.

References

Gurka, M. J., Edwards, L. J. , Muller, K. E., and Kupper, L. L. (2006) “Extending the Box-Cox transformation to the linear mixed model”. Journal of the Royal Statistical Society: Series A (Statistics in Society), 169, 2, 273-288.

Gurka, M. J. and Edwards, L. J. (2011) “Estimating variance components and random effects using the box-cox transformation in the linear mixed model”. Communications in Statistics - Theory and Methods, 40, 3, 515-531.

Kauermann, G. and Carroll, R. J. (2001) “A note on the efficiency of sandwich covariance matrix estimation”. Journal of the American Statistical Association, 96, 456, 1387-1396.

Liang, K. Y. and Zeger, S. L. (1986) “Longitudinal data analysis using generalized linear models”. Biometrika, 73, 1, 13-22.

Mancl, L. A. and DeRouen, T. A. (2001) “A covariance estimator for GEE with improved small-sample properties”. Biometrics, 57, 1, 126-134.

To, D-K., Adimari, G., Chiogna, M. and Risso, D. (2022) “Receiver operating characteristic estimation and threshold selection criteria in three-class classification problems for clustered data”. Statistical Methods in Medical Research, 7, 31, 1325-1341.

Xiong, C., Luo, J., Chen L., Gao, F., Liu, J., Wang, G., Bateman, R. and Morris, J. C. (2018) “Estimating diagnostic accuracy for clustered ordinal diagnostic groups in the three-class case – Application to the early diagnosis of Alzheimer disease”. Statistical Methods in Medical Research, 27, 3, 701-714.

Examples

## Example 1:
data(data_3class)
head(data_3class)
## A model with two covariate
out1 <- clus_lme(fixed_formula = Y ~ X1 + X2, name_class = "D",
                 name_clust = "id_Clus", data = data_3class)
print(out1)
plot(out1)


## Example 2: Box-Cox transformation
data(data_3class_bcx)
out2 <- clus_lme(fixed_formula = Y ~ X, name_class = "D",
                 name_clust = "id_Clus", data = data_3class_bcx,
                 boxcox = TRUE)
print(out2)
plot(out2)

Estimation of the covariate-specific optimal pair of thresholds for clustered data.

Description

clus_opt_thres3 estimates covariate-specific optimal pair of thresholds of a continuous diagnostic test in a clustered design, with three classes of diseases.

Usage

clus_opt_thres3(
  method = c("GYI", "CtP", "MV"),
  out_clus_lme,
  newdata,
  ap_var = TRUE,
  control = list()
)

Arguments

Details

This function implements estimation methods discussed in To et al. (2022) for covariate-specific optimal pair of thresholds in a clustered design with three ordinal groups. The estimators are based on the results from clus_lme function, which fits the linear mixed-effect model by using REML approach.

Before performing estimation, a check for the monotone ordering assumption is performed. This means that, for the fixed values of covariates, three predicted mean values for test results in three diagnostic groups are compared. If the assumption is not meet, the covariate-specific optimal pair of thresholds at the values of covariates are not estimated.

The estimation procedure uses three criteria. Method "GYI" is Generalized Youden Index, which maximizes the sum of three covariate-specific True Class Fractions - TCFs. Method "CtP" is based on Closest to Pefection approach. By using this method, the optimal pair of thresholds is obtained by minimizing the distance, in the unit cube, between a generic point on the covariate-specific ROC surface and the top corner (1, 1, 1). Method "MV" is based on Maximum Volume approach, which searches for thresholds that maximize the volume of a box under the covariate-specific ROC surface. The user can select more than one method. This function allows to estimate covariate-specific optimal pair of thresholds at multiple points for covariates.

The asymptotic variance-covariance matrix of the (estimated) covariate-specific optimal thresholds is estimated by using the Delta method under the normal assumption. If the Box-Cox transformation is applied to the linear mixed-effect model, a nonparametric bootstrap procedure for clustered data will be used to obtain the estimated asymptotic covariance matrix (see To et al. 2022, for more details).

The control argument is a list that can supply any of the following components:

method_optim

Optimization method to be used. There are three options: "L-BFGS-B", "BFGS" and "Nelder-Mead". Default is "L-BFGS-B".

start

Starting values in the optimization procedure. If it is NULL, a starting point will be automatically obtained.

maxit

The maximum number of iterations. Default is 200.

lower, upper

Possible bounds on the threshold range, for the optimization based on "L-BFGS-B" method. Defaults are -Inf and Inf.

n_boot

Number of bootstrap replicates for estimating the covariance matrix (when Box-Cox transformation is applied). Default is 250.

parallel

A logical value. If set to TRUE, a parallel computing is employed in the bootstrap resampling process.

ncpus

Number of processes to be used in parallel computing. Default is 2.

Value

clus_opt_thres3 returns an object of "clus_opt_thres3" class, which is a list containing at least the following components:

Generic functions such as print and plot are also used to show the results.

References

To, D-K., Adimari, G., Chiogna, M. and Risso, D. (2022) “Receiver operating characteristic estimation and threshold selection criteria in three-class classification problems for clustered data”. Statistical Methods in Medical Research, 7, 31, 1325-1341.

Examples

data(data_3class)
## One covariate
out1 <- clus_lme(fixed_formula = Y ~ X1, name_class = "D",
                 name_clust = "id_Clus", data = data_3class)

### Estimate covariate-specific optimal thresholds at multiple values of one covariate,
### with 3 methods
out_thres_1 <- clus_opt_thres3(method = c("GYI", "MV", "CtP"),
                               out_clus_lme = out1,
                               newdata = data.frame(X1 = 1), ap_var = TRUE)
print(out_thres_1)
plot(out_thres_1)

## Two covariates
out2 <- clus_lme(fixed_formula = Y ~ X1 + X2, name_class = "D",
                 name_clust = "id_Clus", data = data_3class)

### Estimate covariate-specific optimal thresholds at one point, with 3 methods
out_thres_2 <- clus_opt_thres3(method = c("GYI", "MV", "CtP"),
                               out_clus_lme = out2,
                               newdata = data.frame(X1 = 1, X2 = 0),
                               ap_var = TRUE)
print(out_thres_2)
plot(out_thres_2)

### Estimate covariate-specific optimal thresholds at three points, with 3 methods
out_thres_3 <- clus_opt_thres3(method = c("GYI", "MV", "CtP"),
                               out_clus_lme = out2,
                               newdata = data.frame(X1 = c(-0.5, 0.5, 0.5),
                                                    X2 = c(0, 0, 1)),
                               ap_var = TRUE)
print(out_thres_3)
plot(out_thres_3, colors = c("forestgreen", "blue"))

Plot an estimated covariate-specific ROC surface for clustered data.

Description

clus_roc_surface estimates and makes a 3D plot of a covariate-specific ROC surface for a continuous diagnostic test, in a clustered design, with three ordinal groups.

Usage

clus_roc_surface(
  out_clus_lme,
  newdata,
  step_tcf = 0.01,
  main = NULL,
  file_name = NULL,
  ellips = FALSE,
  thresholds = NULL,
  ci_level = ifelse(ellips, 0.95, NULL)
)

Arguments

Details

This function implements a method in To et al. (2022) for estimating covariate-specific ROC surface of a continuous diagnostic test in a clustered design, with three ordinal groups. The estimator is based on the results from clus_lme with REML approach.

Before performing estimation, a check for the monotone ordering assumption is performed. This means that, for the fixed values of covariates, three predicted mean values for test results in three diagnostic groups are compared. If the assumption is not meet, the covariate-specific ROC surface at the values of covariates is not estimated.

The ellipsoidal confidence region for TCFs at a given pair of thresholds, if required, is constructed by using normal approximation and is plotted in the ROC surface space. The confidence level (default) is 0.95. Note that, if the Box-Cox transformation is applied for the linear mixed-effect model, the pair of thresholds must be input in the original scale. If the constructed confidence region for TCFs is outside the unit cube, a probit transformation will be automatically applied to obtain an appropriate confidence region, which is inside the unit cube (see Bantis et. al., 2017).

Value

clus_roc_surface returns a 3D rgl plot of the estimated covariate-specific ROC surface.

References

Bantis, L. E., Nakas, C. T., Reiser, B., Myall, D., and Dalrymple-Alford, J. C. (2017). “Construction of joint confidence regions for the optimal true class fractions of Receiver Operating Characteristic (ROC) surfaces and manifolds”. Statistical methods in medical research, 26, 3, 1429-1442.

To, D-K., Adimari, G., Chiogna, M. and Risso, D. (2022) “Receiver operating characteristic estimation and threshold selection criteria in three-class classification problems for clustered data”. Statistical Methods in Medical Research, 7, 31, 1325-1341.

Examples

data(data_3class)
## One covariate
out1 <- clus_lme(fixed_formula = Y ~ X1, name_class = "D",
                 name_clust = "id_Clus", data = data_3class)

### plot only covariate-specific ROC surface
clus_roc_surface(out_clus_lme = out1, newdata = data.frame(X1 = 1))

### plot covariate-specific ROC surface and a 95% ellipsoidal confidence region for TCFs
clus_roc_surface(out_clus_lme = out1, newdata = data.frame(X1 = 1),
                 ellips = TRUE, thresholds = c(0.9, 3.95))

## Two covariates
out2 <- clus_lme(fixed_formula = Y ~ X1 + X2, name_class = "D",
                 name_clust = "id_Clus", data = data_3class)

### plot only covariate-specific ROC surface
clus_roc_surface(out_clus_lme = out2, newdata = data.frame(X1 = 1, X2 = 1))

### plot covariate-specific ROC surface and a 95% ellipsoidal confidence region for TCFs
clus_roc_surface(out_clus_lme = out2, newdata = data.frame(X1 = 1, X2 = 1),
                 ellips = TRUE, thresholds = c(0.9, 3.95))

Estimation of the covariate-specific TCFs for clustered data.

Description

clus_tcfs estimates covariate-specific True Class Fractions (TCFs), at a specified pair of thresholds, of a continuous diagnostic test in a clustered design with three ordinal groups. This function allows to estimate covariate-specific TCFs at multiple points for covariates.

Usage

clus_tcfs(out_clus_lme, newdata, thresholds, ap_var = FALSE)

Arguments

Details

This function implements a method in To et al. (2022) for estimating covariate-specific TCFs at a specified pair of thresholds of a continuous diagnostic test in a clustered design with three ordinal groups. The estimator is based on results from clus_lme, which uses the REML approach. The asymptotic variance-covariance matrix of the estimated covariate-specific TCFs is estimated through the Delta method. Note that, if the Box-Cox transformation is applied for the linear mixed-effect model, the pair of thresholds must be input in the original scale.

Before performing estimation, a check for the monotone ordering assumption is performed. This means that, for the fixed values of covariates, three predicted mean values for test results in three diagnostic groups are compared. If the assumption is not meet, the covariate-specific TCFs at the values of covariates are not estimated.

Value

TCFs returns an object of class "TCFs", which is a list containing at least the following components:

Generic functions such as print is also used to show the results.

References

To, D-K., Adimari, G., Chiogna, M. and Risso, D. (2022) “Receiver operating characteristic estimation and threshold selection criteria in three-class classification problems for clustered data”. Statistical Methods in Medical Research, 7, 31, 1325-1341.

Examples

data(data_3class)
## One covariate
out1 <- clus_lme(fixed_formula = Y ~ X1, name_class = "D",
                 name_clust = "id_Clus", data = data_3class)

### Estimate TCFs at one single value of X1, (t1, t2) = (1, 4)
out_tcfs_1 <- clus_tcfs(out_clus_lme = out1, newdata = data.frame(X1 = 1),
                        thresholds = c(1, 4), ap_var = TRUE)
print(out_tcfs_1)

## Two covariates
out2 <- clus_lme(fixed_formula = Y ~ X1 + X2, name_class = "D",
                 name_clust = "id_Clus", data = data_3class)

### Estimate covariate-specific TCFs at point (X1, X2) = (1, 0), and (t1, t2) = (1, 4)
out_tcfs_2 <- clus_tcfs(out_clus_lme = out2,
                        newdata = data.frame(X1 = 1, X2 = 0),
                        thresholds = c(1, 4), ap_var = TRUE)
print(out_tcfs_2)

### Estimate covariate-specific TCFs at three points and (t1, t2) = (1, 4)
out_tcfs_3 <- clus_tcfs(out_clus_lme = out2,
                        newdata = data.frame(X1 = c(-0.5, 0.5, 0.5),
                                             X2 = c(0, 0, 1)),
                        thresholds = c(1, 4), ap_var = TRUE)
print(out_tcfs_3)

Estimation of the covariate-specific VUS for clustered data.

Description

This function estimates the covariate-specific VUS of a continuous diagnostic test in the setting of clustered data as described in Xiong et al. (2018). This function allows to estimate covariate-specific VUS at multiple points for covariates.

Usage

clus_vus(out_clus_lme, newdata, ap_var = TRUE, subdivisions = 1000, ...)

Arguments

Details

This function implements a method in Xiong et al. (2018) for estimating covariate-specific VUS of a continuous diagnostic test in a clustered design with three ordinal groups. The estimator is based on results from clus_lme, which uses the REML approach. The standard error of the estimated covariate-specific VUS is approximated through the Delta method.

Before performing estimation, a check for the monotone ordering assumption is performed. This means that, for the fixed values of covariates, three predicted mean values for test results in three diagnostic groups are compared. If the assumption is not meet, the covariate-specific VUS at the values of covariates are not estimated. In addition, this function also performs the statistical test, H_0: VUS = 1/6 versus an alternative of interest.

Value

clus_vus returns an object of class "VUS" which is a list containing at least the following components:

Generic functions such as print is also used to show the results.

References

Xiong, C., Luo, J., Chen L., Gao, F., Liu, J., Wang, G., Bateman, R. and Morris, J. C. (2018) “Estimating diagnostic accuracy for clustered ordinal diagnostic groups in the three-class case – Application to the early diagnosis of Alzheimer disease”. Statistical Methods in Medical Research, 27, 3, 701-714.

Examples

data(data_3class)
## One covariate
out1 <- clus_lme(fixed_formula = Y ~ X1, name_class = "D",
                 name_clust = "id_Clus", data = data_3class)

### Estimate covariate-specific VUS at one value of one covariate
out_vus1 <- clus_vus(out1, newdata = data.frame(X1 = 0.5))
ci_clus_vus(out_vus1, ci_level = 0.95)

### Estimate covariate-specific VUS at multiple values of one covariate
out_vus2 <- clus_vus(out1, newdata = data.frame(X1 = c(-0.5, 0, 0.5)))
ci_clus_vus(out_vus2, ci_level = 0.95)

## Two covariates
out2 <- clus_lme(fixed_formula = Y ~ X1 + X2, name_class = "D",
                 name_clust = "id_Clus", data = data_3class)

### Estimate covariate-specific VUS at one point
out_vus3 <- clus_vus(out2, newdata = data.frame(X1 = 1.5, X2 = 1))
ci_clus_vus(out_vus3, ci_level = 0.95)

### Estimate covariate-specific VUS at three points
out_vus4 <- clus_vus(out2, newdata = data.frame(X1 = c(-0.5, 0.5, 0.5),
                                                X2 = c(0, 0, 1)))
ci_clus_vus(out_vus4, ci_level = 0.95)

A simulated data

Description

A simulated data example with 30 clusters.

Usage

data(data_3class)

Format

A data frame with 225 observations (from 30 clusters).

id_Clus

the id number of cluster.

Y

a vector containing test results.

D

a factor with 3 levels for the disease status, 1, 2, 3. The levels correspond to benign disease, early stage and late stage.

X1

a continuous covariate.

X2

a binary covariate.

A simulated data

Description

A simulated data example with 60 clusters. This dataset is used in a example of analysis with Box-Cox transformation.

Usage

data(data_3class_bcx)

Format

A data frame with 582 observations (from 60 clusters).

id_Clus

the id number of cluster.

Y

a vector containing test results.

D

a factor with 3 levels for disease status, 1, 2, 3. The levels correspond to benign disease, early stage and late stage.

X

a continuous covariate.

Plot an clus_lme object.

Description

Diagnostic plots for the linear mixed-effect model, fitted by clus_lme.

Usage

## S3 method for class 'clus_lme'
plot(x, file_name = NULL, ...)

Arguments

Details

plot.clus_lme provides three diagnostic plots: Q-Q plots for residuals, Fitted vs. Residuals values, and Q-Q plot for cluster effects, based on ggplot().

Value

plot.clus_lme returns the diagnostic plots for the linear mixed-effect model, fitted by clus_lme.

See Also

clus_lme

Plot of confidence regions for covariate-specific optimal pair of thresholds.

Description

This function plots confidence regions for covariate-specific optimal pair of thresholds.

Usage

## S3 method for class 'clus_opt_thres3'
plot(
  x,
  ci_level = 0.95,
  colors = NULL,
  xlims,
  ylims,
  size_point = 0.5,
  size_path = 0.5,
  names_labels,
  nrow_legend = 1,
  file_name = NULL,
  ...
)

Arguments

Details

plot.clus_opt_thres3 provides plots of confidence regions (and point estimates) of covariate-specific optimal pair of thresholds. The plots are based on ggplot().

Value

plot.clus_opt_thres3 returns plots of confidence regions of covariate-specific optimal pair of thresholds.

See Also

clus_opt_thres3

Print summary results from ci_clus_vus

Description

print.ci_vus displays the results of the output from ci_clus_vus.

Usage

## S3 method for class 'ci_clus_vus'
print(x, digits = 3, ...)

Arguments

Details

print.ci_clus_vus shows a summary table for confidence interval limits for covariate-specific VUS.

Value

print.ci_clus_vus shows a summary table for confidence intervals for covariate-specific VUS.

See Also

clus_vus

Print summary results of an clus_lme object

Description

print.clus_lme displays results of the output from clus_lme.

Usage

## S3 method for class 'clus_lme'
print(x, digits = max(3L, getOption("digits") - 3L), call = TRUE, ...)

Arguments

Details

print.clus_lme shows a summary table for the estimated parameters in the cluster-effect model (continuous diagnostic test in three-class setting).

Value

print.clus_lme returns a summary table for the estimated parameters in the cluster-effect model.

See Also

clus_lme

Print summary results from clus_opt_thres3

Description

print.clus_opt_thres3 displays the results of the output from clus_opt_thres3.

Usage

## S3 method for class 'clus_opt_thres3'
print(x, digits = 3, call = TRUE, ...)

Arguments

Details

print.clus_opt_thres3 shows a summary table for covariate-specific optimal pair of thresholds estimates.

Value

print.clus_opt_thres3 returns a summary table for results of covariate-specific optimal pair of thresholds estimation.

See Also

clus_opt_thres3

Print summary results from clus_tcfs

Description

print.clus_tcfs displays the results of the output from clus_tcfs.

Usage

## S3 method for class 'clus_tcfs'
print(x, digits = 3, call = TRUE, ...)

Arguments

Details

print.clus_tcfs shows a summary table for covariate-specific TCFs estimates.

Value

print.clus_tcfs returns a summary table for covariate-specific TCFs estimates.

See Also

clus_tcfs

Print summary results from clus_vus

Description

print.clus_vus displays the results of the output from clus_vus.

Usage

## S3 method for class 'clus_vus'
print(x, digits = 3, call = TRUE, ...)

Arguments

Details

print.clus_vus shows a summary table for covariate-specific VUS estimates, containing estimates, standard errors, z-values and p-values for the hypothesis testing H_0: VUS = 1/6 versus an alternative H_A: VUS > 1/6.

Value

print.clus_vus returns a summary table for covariate-specific VUS estimates.

See Also

clus_vus