View source: R/ThresholdROC-2states.R
plotCostROC | R Documentation |
This function plots the ROC curve and the cost function associated to the disease prevalence and costs.
plotCostROC(x, type = "l", which.plot, ...)
x |
an object of class |
type |
1-character string giving the type of plot desired. Default, |
which.plot |
which plot should be produced? The user can choose between |
... |
other graphical parameters to be passed to |
If the argument x
was constructed with method="empirical"
, the argument extra.info
should be switched to TRUE
(this only applies when x
is an object of class thres2
).
For parameter type
the following values are possible: "p"
for points, "l"
for lines, "b"
for both points and lines, "c"
for empty points joined by lines, "o"
for overplotted points and lines, "s"
and "S"
for stair steps and "h"
for histogram-like vertical lines. Finally, "n"
does not produce any points or lines.
When x
is an object of class thres2
, two plots are produced. The first one shows the cost function with the cost minimising threshold in red. The second one is the step ROC curve with the sensitivity and specificity achieved in red. If method = "empirical"
is used when building x
, empirical cost function and ROC curve are plotted. If method = "smooth"
is used when building x
, smooth cost function and ROC curve are plotted.
When x
is an object of class thres3
, two plots are produced. The first one shows the cost function C(T1) with the first cost minimising threshold in red. The second one shows the cost function C(T2) with the second cost minimising threshold in red.
This function uses the plot()
function and further arguments can be set to customise the resulting plot.
Skaltsa K, Jover L, Carrasco JL. (2010). Estimation of the diagnostic threshold accounting for decision costs and sampling uncertainty. Biometrical Journal 52(5):676-697.
Skaltsa K, Jover L, Fuster D, Carrasco JL. (2012). Optimum threshold estimation based on cost function in a multistate diagnostic setting. Statistics in Medicine, 31:1098-1109.
thres2
## Not run:
# example 1: x is an object of class 'thres2'
n1 <- 100
n2 <- 100
set.seed(19998)
par1.1 <- 0
par1.2 <- 1
par2.1 <- 2
par2.2 <- 1
rho <- 0.3
k1 <- rnorm(n1, par1.1, par1.2) # non-diseased
k2 <- rnorm(n2, par2.1, par2.2) # diseased
x <- thres2(k1, k2, rho, method="emp", ci.method="boot", extra=TRUE)
par(mfrow=c(1,2))
plotCostROC(x)
# example 2: x is an object of class 'thres3'
set.seed(2015)
n <- 100
k1 <- rlnorm(n)
k2 <- rnorm(n, 3, 1)
k3 <- rnorm(n, 5, 1)
rho <- c(1/3, 1/3, 1/3)
y <- thres3(k1, k2, k3, rho, B=1000, ci.method="boot", dist1="lnorm", dist2="norm", dist3="norm")
par(mfrow=c(1,2))
plotCostROC(y)
## End(Not run)
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