phm-class | R Documentation |
phm
objects.
Objects of the class phm
are output by the function with the same name. Apart from standard methods the function sroc
provides SROC curves and confidence bands for model fits.
## S3 method for class 'phm' print(x, ...) ## S3 method for class 'phm' summary(object, level = 0.95, ...) ## S3 method for class 'phm' sroc(fit, fpr = 1:99/100, ...) ## S3 method for class 'phm' plot(x, extrapolate = FALSE, confband = TRUE, level = 0.95, ylim = c(0,1), xlim = c(0,1), sroclty = 1, sroclwd = 1, confbandlty = 2, confbandlwd = 0.5, ...)
x |
a |
object |
a |
fit |
a |
level |
numeric, the confidence level for calculations of confidence intervals ( |
fpr |
numeric, the false positives rates for which to calculate the predicted sensitivities. |
extrapolate |
logical, should the sroc curve be plotted beyond the observed false positive rates? |
confband |
logical, should confidence bands be plotted? |
ylim |
numeric of length 2, which section of the sensitivities to plot? |
xlim |
numeric of length 2, which section of the false positive rates to plot? |
sroclty |
integer, line type of the SROC curve |
sroclwd |
integer, line width of the SROC curve |
confbandlty |
integer, line type of the SROC curve's confidence band |
confbandlwd |
integer, line width of the SROC curve's confidence band |
... |
arguments to be passed on to other functions |
The SROC curve is derived from the model formula. The confidence bands are calculated from the bounds of the confidence interval for the diagnostic accuracy parameter θ. The parameter and its confidence interval are then also used to calculate the AUC and partial AUC using the formulae
AUC(a,b) = \int_a^bu^θ\mathrm{d}u = \frac{1}{θ+1}[b^{θ+1}-a^{θ+1}],
AUC = AUC(0,1)
and
pAUC = \frac{1}{b-a}AUC(a,b),
where a is the lower bound of the observed false positive rates and b the upper.
The sroc
function returns a matrix ready for plotting. Each row corresponds to one point in ROC space.
Philipp Doebler <philipp.doebler@googlemail.com>
Holling, H., Boehning D., Boehning, W. (2012) “Meta-Analysis of Diagnostic Studies based upon SROC-Curves: a Mixed Model Approach using a Proportional Hazards Model.” Statistical Modelling, 12, 347–375.
phm
# load data data(AuditC) # fit model fit <- phm(AuditC) #calculate a SROC curve, but do not plot it sroc.AuditC <- sroc(fit) # plot the SROC curve in ROC space as a line plot(sroc.AuditC, type = "l") # Fancy version using plot plot(fit)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.