Description Usage Arguments Details Value Warning Note Author(s) References See Also Examples
once identified the optimal cut-points from VUS or Youden3Grp analyses, use the function to obtain the three empirical correct classification probabilities associated with each diagnosis group.
1 | Sp.Sm.Se(x, y, z, t.minus, t.plus)
|
x |
A numeric vector, a diagnostic test's measurements in the D- (usually healthy subjects). |
y |
A numeric vector, a diagnostic test's measurements in the D0 (usually mildly diseased subjects). |
z |
A numeric vector, a diagnostic test's measurements in the D+ (usually severely diseased subjects). |
t.minus |
A numeric value, the lower optimal cut-point identified from VUS or Youden3Grp analyses. |
t.plus |
A numeric value, the upper optimal cut-point identified from VUS or Youden3Grp analyses. |
Specificity: Sp=Pr(x≤ t_-) for D^- group; Sensitivity: Se=Pr(z≥ t_+) for D^+ group and the probability of the diagnostic test for the D^0 group fall between the two cut points: Sm=Pr(t_- ≤ y ≤ t_+). These three probabilities will be estimated empirically.
Return a numeric vector with three components Sp, Sm and Se, the three correct classification probabilities.
The bootstrapping to obtain the variance on the nonparametric VUS estimate may take a while.
Bug reports, malfunctioning, or suggestions for further improvements or contributions can be sent to Jingqin Luo <rosy@wubios.wustl.edu>.
Jingqin Luo
Xiong, C. and van Belle, G. and Miller, J.P. and Morris, J.C. (2006) Measuring and Estimating Diagnostic Accuracy When There Are Three Ordinal Diagnostic Groups. Statistics In Medicine 25 7 1251–1273.
Ferri, C. and Hernandez-Orallo, J. and Salido, M.A. (2003) Volume under the ROC Surface for Multi-class Problems LECTURE NOTES IN COMPUTER SCIENCE 108–120.
VUS
Normal.VUS
NonParametric.VUS.var
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