Data sets with negative association between biomarker values and probability of target condition can be used without data manipulation
Order of panels in plot.diagmeta() can be specified by the user
new argument 'direction' to specify whether the probability of the target condition (e.g., a disease) is increasing or decreasing with higher values of the biomarker
ipd2diag();
new argument 'data' to provide data set with information on arguments 'studlab', 'value', and 'status'
diagmeta():
input to first argument 'TP' can be an object created with ipd2diag()
plot.diagmeta():
New internal function plot.diagmeta-internal()
ipd2diag();
Change maintainer's email address
New branch 'release' on GitHub starting with diagmeta, version 0.5-1
Behaviour of print.diagmeta() and print.summary.diagmeta() switched (to be in line with other print and summary functions in R)
Do not stop with an error if optimal cut-off cannot be determined for logistic distribution
Calculate area under the curve for specificity given sensitivity
new list elements 'AUCSens' and 'AUCSpec' to calculate AUC for sensitivity given specificity or vice versa (existing list element 'AUC' is equal to 'AUCSens')
New internal function catch() to catch value for an argument
print correct confidence region for specificities in SROC curves
diagstats():
print a more informative error message in case of a negative correlation between increasing cutoffs and sensitivity
plot.diagmeta():
argument 'points' considered for plots of type "regression", "cdf", "survival", "Youden", "ROC" and "sensspec"
Help pages:
as.data.frame.diagmeta(), plot.diagmeta(), print.diagmeta(), print.diagstats(), print.summary.diagmeta(), summary.diagmeta()
plot.diagmeta():
plot.diagmeta(): new plot type to show sensitivity and specificity curves
New arguments 'sens' and 'spec' in diagstats()
print.summary.diagmeta():
print confidence interval for optimal cutoff (for normal distribution)
New function as.data.frame.diagmeta()
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