Description Usage Arguments Details Value Author(s) References See Also Examples
The approach to SROC curve modeling is described in the paper of Moses, Shapiro and Littenberg (1993). It is considered outdated and is included in mada
so that users can reproduce older results and compare different SROC curves.
1 2 3 4 5 
data 
any object that can be converted to a data frame with integer variables for observed frequencies of true positives, false negatives, false positives and true negatives. The names of the variables are provided by the arguments 
TP 
character or integer: name for vector of integers that is a variable of 
FN 
character or integer: name for vector of integers that is a variable of 
FP 
character or integer: name for vector of integers that is a variable of 
TN 
character or integer: name for vector of integers that is a variable of 
subset 
the rows of 
fpr 
Points between 0 and 1 on which to draw the SROC curve. Should be tightly spaced. If set to 
extrapolate 
logical, should the SROC curve be extrapolated beyond the region where false positive rates are observed? 
correction 
numeric, continuity correction applied if zero cells 
correction.control 
character, if set to 
add 
logical, should the SROC curve be added to an existing plot? 
lty 
line type, see 
lwd 
line width, see 
col 
color of SROC, see 
... 
arguments to be passed on to plotting functions. 
Details are found in the paper of Moses, Shapiro and Littenberg (1993).
Besides plotting the SROC, an invisible
list is returned which contains the parameters of the SROC.
Philipp Doebler <philipp.doebler@googlemail.com>
Moses L.E., Shapiro D., & Littenberg B. (1993) “Combining independent studies of a diagnostic test into a summary ROC curve: dataanalytic approaches and some additional considerations.” Statistics in Medicine, 12, 1293–1316.
reitsmaclass
, talpha
, SummaryPts
1 2 3 4 5 6 7 8 9 10  ## First Example
data(Dementia)
ROCellipse(Dementia)
mslSROC(Dementia, add = TRUE) # Add the MSLSROC to this plot
## Second Example
# Make a fancy plot and look at the coefficients
msl_Dementia < mslSROC(Dementia, col = 3, lwd = 3, lty = 3)
msl_Dementia$A2 # intercept on logit SROC space
msl_Dementia$B2 # slope on logit SROC space

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