gROC_param {movieROC} | R Documentation |
Build a binormal ROC curve for a univariate marker
Description
This function builds a univariate ROC curve (standard or general) assuming the binormal scenario with parameters being the sample estimates. It returns a ‘groc’ object, a list of class ‘groc’.
Usage
gROC_param(X, D, side = c("right", "left", "both", "both2"), N = NULL, ...)
Arguments
X |
Vector of marker values. |
D |
Vector of response values. Two levels; if more, the two first ones are used. |
side |
Type of ROC curve. One of |
N |
Number indicating the length of the vector of FPR considered to build the ROC curve:
|
... |
Other parameters to be passed. Not used. |
Details
This function's main job is to estimate an ROC curve for a univariate marker
under one of these considerations: larger values of the marker are associated
with a higher probability of being positive (resulting in the right-sided
ROC curve, \mathcal{R}_r (\cdot)
), the opposite (left-sided ROC curve,
\mathcal{R}_l (\cdot)
), when both smaller and larger values of the marker are
associated with having more probability of being positive (gROC curve,
\mathcal{R}_g(\cdot)
), the opposite (opposite gROC curve, \mathcal{R}_{g'}(\cdot)
).
Value
A list of class ‘groc’ with the following fields:
controls , cases |
Marker values of negative and positive subjects, respectively. |
levels |
Levels of response values. |
side |
Type of ROC curve. |
t |
Vector of false-positive rates. |
roc |
Vector of values of the ROC curve for |
c |
Vector of marker thresholds resulting in ( |
xl , xu |
Vectors of marker thresholds resulting in ( |
auc |
Area under the curve estimate. |
a , b |
Estimates for parameters |
p0 |
Estimate of the "central value", |
References
P. Martínez-Camblor and J. C. Pardo-Fernández (2019) “Parametric estimates for the receiver operating characteristic curve generalization for non-monotone relationships”. Statistical Methods in Medical Research, 28(7): 2032–2048. DOI: doi:10.1177/0962280217747009.
Examples
data(HCC)
# ROC curve estimates for gene 03515901 and response tumor assuming the binormal scenario
gROC_param(X = HCC[,"cg03515901"], D = HCC$tumor) # Standard right-sided ROC curve
gROC_param(X = HCC[,"cg03515901"], D = HCC$tumor, side = "left") # Left-sided ROC curve
gROC_param(X = HCC[,"cg03515901"], D = HCC$tumor, side = "both") # gROC curve