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
Bug fixes
diagmeta():
fix for erratic confidence limits of AUC which could be outside the
admissible range from 0 to 1 or exclude the AUC estimate
User-visible changes
More concise printout for summary.diagmeta()
Internal changes
diagmeta():
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
diagmeta, version 0.4-1
(2021-05-11)
Bug fixes
plot.diagmeta():
print correct confidence region for specificities in SROC
curves
diagstats():
print results for requested specificity if only argument ‘spec’ is
provided
User-visible changes
Use Markdown for NEWS
Internal changes
diagmeta():
new list element ‘Cov.fixed’ with covariance matrix from fixed
effects model
diagmeta, version 0.4-0
(2020-04-02)
Major changes
New default model (argument ‘model’) in diagmeta(), i.e., common
random intercept and common slope (“CICS”), due to estimation problems
with the previous default (“DIDS”) after changes in R package
lme4
Bug fixes
plot.diagmeta():
correct line types for survival functions
User-visible changes
diagmeta():
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”
argument ‘col.points’ can be any color defined in colours()
new argument ‘col.ci’ to specify color of curves with confidence
limits
Internal changes
diagmeta():
check for numerical values in arguments ‘TP’, ‘FP’, ‘TN’, ‘FN’, and
‘cutoff’
diagmeta, version 0.3-0
(2018-12-11)
User-visible changes
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()
Bug fixes
plot.diagmeta():
correct ROC curves for datasets with decreasing cutoff values for
individual studies (points (0, 0) and (1, 1) were connected with the
wrong values on the ROC curve)
Internal changes
diagmeta():
calculate and return lower and upper confidence limit for optimal
cutoff (for normal distribution)