biROC {movieROC} | R Documentation |
This is one of the main functions of the movieROC package.
It builds a bivariate ROC curve by considering one of these methods:
i) fitting a binary logistic regression model with a particular combination
(fixed by the user) of the two components on the right-hand side,
ii) linear combinations with fixed parameters,
iii) linear combinations with dynamic parameters, or
iv) quadratic combinations with fixed parameters.
It returns a ‘biroc’ object, a list of class ‘biroc’.
This object can be print
ed or plot
ted. It may be also passed to
plot.buildROC
and movieROC
function.
biROC(X, D, ...)
## Default S3 method:
biROC(X, D, method = c("lrm", "fixedLinear", "fixedQuadratic",
"dynamicEmpirical", "dynamicMeisner", "kernelOptimal"),
formula.lrm = "D ~ X.1 + I(X.1^2) + X.2 + I(X.2^2) + I(X.1*X.2)",
stepModel = TRUE, methodLinear = c("coefLinear", "SuLiu", "PepeThompson",
"logistic", "minmax"), coefLinear = c(1, 1), coefQuadratic = c(1, 1, 0, 1, 1),
K = 201, alpha = 0.5, approxh = 0.5, multiplier = 2,
kernelOptimal.H = c("Hbcv", "Hscv", "Hpi", "Hns", "Hlscv", "Hbcv.diag",
"Hscv.diag", "Hpi.diag", "Hlscv.diag"), eps = sqrt(.Machine$double.eps),
verbose = FALSE, ...)
X |
Matrix (dimension |
D |
Vector of response values. Two levels; if more, the two first ones are used. |
method |
Method used to build the classification regions. One of |
formula.lrm |
If |
stepModel |
If TRUE and |
methodLinear |
If |
coefLinear |
If |
coefQuadratic |
If |
K |
If |
alpha , approxh , multiplier |
If |
kernelOptimal.H |
If |
eps |
Epsilon value to consider. Default: |
verbose |
If TRUE, a progress bar is displayed for computationally intensive methods. Default: FALSE. |
... |
Other parameters to be passed. Not used. |
A list of class ‘biroc’ with the following fields:
controls , cases |
Marker values of negative and positive subjects, respectively. |
levels |
Levels of response values. |
t |
Vector of false-positive rates. |
roc |
Vector of values of the ROC curve for |
auc |
Area under the curve estimate. |
Z |
If |
c |
If |
CoefTable |
If |
J. Q. Su and J. S. Liu. (1993) “Linear combinations of multiple diagnostic markers”. Journal of the American Statistical Association, 88(424): 1350–1355. DOI: doi:10.1080/01621459.1993.10476417.
M. S. Pepe and M. L. Thompson (2000) “Combining diagnostic test results to increase accuracy”. Biostatistics, 1 (2):123–140. DOI: doi:10.1093/biostatistics/1.2.123.
C. Liu, A. Liu, and S. Halabi (2011) “A min–max combination of biomarkers to improve diagnostic accuracy”. Statistics in Medicine, 30(16): 2005–2014. DOI: doi:10.1002/sim.4238.
P. Martínez-Camblor, S. Pérez-Fernández, and S. Díaz-Coto (2021) “Optimal classification scores based on multivariate marker transformations”. AStA Advances in Statistical Analysis, 105(4): 581–599. DOI: doi:10.1007/s10182-020-00388-z.
A. Meisner, M. Carone, M. S. Pepe, and K. F. Kerr (2021) “Combining biomarkers by maximizing the true positive rate for a fixed false positive rate”. Biometrical Journal, 63(6): 1223–1240. DOI: doi:10.1002/bimj.202000210.
data(HCC)
# ROC curve for genes 20202438 and 18384097 to identify tumor by 4 different methods:
X <- cbind(HCC$cg20202438, HCC$cg18384097); D <- HCC$tumor
## 1. Linear combinations with fixed parameters by Pepe and Thompson (2000)
biROC(X, D, method = "fixedLinear", methodLinear = "PepeThompson")
## 2.Linear combinations with dynamic parameters by Meisner et al. (2021)
### Time consuming
biROC(X, D, method = "dynamicMeisner")
## 3. Logistic regression model with quadratic formula by default
biROC(X, D)
## 4. Optimal transformation with multivariate KDE by Martínez-Camblor et al. (2021)
biROC(X, D, method = "kernelOptimal")