ROC {Epi} | R Documentation |
Computes sensitivity, specificity and positive and negative predictive
values for a test based on dichotomizing along the variable
test
, for prediction of stat
. Alternatively 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.
Plots curves of these and a ROC-curve.
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(), ... )
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 |
A list with two components:
res |
dataframe with variables sn, sp, pvp, pvn and fv. The latter is the unique values of test (for PS==FALSE ) or linear predictor from the logistic regression |
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
.
Bendix Carstensen, Steno Diabetes Center & University of Copenhagen, http://www.biostat.ku.dk/~bxc
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" )