Epi: ROC – R documentation

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ROC

Function to compute and draw ROC-curves.

Description

Computes sensitivity, specificity and positive and negative predictive values for a test based on dichotomizing along the variable test, for prediction of stat. Plots curves of these and a ROC-curve.

Usage

ROC( test = NULL,
     stat = NULL,
     form = NULL,
     plot = c("sp", "ROC"),
       PS = is.null(test),
       PV = TRUE,
       MX = TRUE,
       MI = TRUE,
      AUC = TRUE,
     grid = seq(0,100,10),
 col.grid = gray( 0.9 ),
     cuts = NULL,
      lwd = 2,
     data = parent.frame(),
      ... )

Arguments

`test`	Numerical variable used for prediction.
`stat`	Logical variable of true status.
`form`	Formula used in a logistic regression. If this is given, `test` and `stat` are ignored. If not given then both `test` and `stat` must be supplied.
`plot`	Character variable. If "sp", the a plot of sensitivity, specificity and predictive values against test is produced, if "ROC" a ROC-curve is plotted. Both may be given.
`PS`	logical, if TRUE the x-axis in the plot "ps"-plot is the the predicted probability for `stat`==TRUE, otherwise it is the scale of `test` if this is given otherwise the scale of the linear predictor from the logistic regression.
`PV`	Should sensitivity, specificity and predictive values at the optimal cutpoint be given on the ROC plot?
`MX`	Should the “optimal cutpoint” (i.e. where sens+spec is maximal) be indicated on the ROC curve?
`MI`	Should model summary from the logistic regression model be printed in the plot?
`AUC`	Should the area under the curve (AUC) be printed in the ROC plot?
`grid`	Numeric or logical. If FALSE no background grid is drawn. Otherwise a grid is drawn on both axes at `grid` percent.
`col.grid`	Colour of the grid lines drawn.
`cuts`	Points on the test-scale to be annotated on the ROC-curve.
`lwd`	Thickness of the curves
`data`	Data frame in which to interpret the variables.
`...`	Additional arguments for the plotting of the ROC-curve. Passed on to `plot`

Details

As an alternative to a test and a status variable, a model formula may given, in which case the the linear predictor is the test variable and the response is taken as the true status variable. The test used to derive sensitivity, specificity, PV+ and PV- as a function of x is test>=x as a predictor of stat=TRUE.

Value

A list with two components:

res

dataframe with variables sens, spec, pvp, pvn and name of the test variable. The latter is the unique values of test or linear predictor from the logistic regression in ascending order with -Inf prepended. Since the sensitivity is defined as P(test>x)|status=TRUE, the first row has sens equal to 1 and spec equal to 0, corresponding to drawing the ROC curve from the upper right to the lower left corner.

lr

glm object with the logistic regression result used for construction of the ROC curve

0, 1 or 2 plots are produced according to the setting of plot.

Author(s)

Bendix Carstensen, Steno Diabetes Center \& University of Copenhagen, http://bendixcarstensen.com

Examples

x <- rnorm( 100 )
z <- rnorm( 100 )
w <- rnorm( 100 )
tigol <- function( x ) 1 - ( 1 + exp( x ) )^(-1)
y <- rbinom( 100, 1, tigol( 0.3 + 3*x + 5*z + 7*w ) )
ROC( form = y ~ x + z, plot="ROC" )

Epi

Statistical Analysis in Epidemiology

v2.44

GPL-2

Authors

Bendix Carstensen [aut, cre], Martyn Plummer [aut], Esa Laara [ctb], Michael Hills [ctb]

Initial release

2021-02-28

ROC