plot.thres2: Threshold and density plot (two-state setting)

Description

The function provides a graphic including the sample densities (diseased and non-diseased populations), the threshold and its confidence interval.

Usage

## S3 method for class 'thres2':
plot(x, bw = c("nrd0", "nrd0"), ci = TRUE,
  which.boot = c("norm", "perc"), col = c(1, 2, 1),
  lty = c(1, 1, 1, 2), lwd = c(1, 1, 1),
  main = paste0("Threshold estimate ", ifelse(ci, "and CI ", ""),
  "(method ", x$T$method, ")"), xlab = "", legend = TRUE,
  leg.pos = "topleft", leg.cex = 1, ...)

Arguments

an object of class thres2.

vector containing the bandwith for the non-diseased sample in the first position and the bandwith for the diseased sample in the second position (to be passed to density()). Default, c("nrd0", "nrd0").

should the confidence interval be plotted? Default, TRUE.

which.boot

in case x contains confidence intervals computed by bootstrapping, which one should be printed? The user can choose between "norm" (based on normal distribution) or "perc" (based on percentiles). Default, "norm

col

a 3-dimensional vector containing: col[1]: color for the density of the non-diseased sample col[2]: color for the density of the diseased sample col[3]: color for the threshold and its co

lty

a 4-dimensional vector containing: lty[1]: line type for the density of the non-diseased sample lty[2]: line type for the density of the diseased sample lty[3]: line type for the threshold

lwd

a 3-dimensional vector containing: lwd[1]: line width for the density of the non-diseased sample lwd[2]: line width for the density of the diseased sample lwd[3]: line width for the threshold and i

legend

logical asking if an automatic legend should be added to the graph. Default, TRUE.

leg.pos

position of the legend. Default, "topleft". Ignored if legend=FALSE.

leg.cex

number that reescales the size of the legend. Ignored if legend=FALSE. Default, 1.

main, xlab, ...

further arguments to be passed to plot().

Value

Estimates of the density functions for both samples and vertical lines representing the threshold and its confidence limits are drawn.

References

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.

Examples

Run this code

n1 <- 100
n2 <- 100
set.seed(1234)
par1.1 <- 0
par1.2 <- 1
par2.1 <- 2
par2.2 <- 1
rho <- 0.2
k1 <- rnorm(n1, par1.1, par1.2) # non-diseased
k2 <- rnorm(n2, par2.1, par2.2) # diseased

thres <- thres2(k1, k2, rho, method="eq", ci.method="d")
plot(thres, col=c(1, 2, 4), lwd=c(2, 2, 1), leg.pos="topright")