diagstats {diagmeta} | R Documentation |
diagmeta
The user can provide cutoffs, sensitivities, and / or specificities to calculate the respective quantities (with confidence intervals). Furthermore, positive predictive values (PPV), negative predictive values (NPV), and probabilities of disease (PD) are calculated if the prevalence is provided.
diagstats(x, cutoff = x$optcut, sens, spec, prevalence, level = 0.95)
x |
An object of class |
cutoff |
A numeric or vector with cutoff value(s) |
sens |
A numeric or vector with sensitivity value(s) |
spec |
A numeric or vector with specificity value(s) |
prevalence |
A numeric or vector with the prevalence(s) |
level |
The level used to calculate confidence intervals |
A data frame of class "diagstats" with the following variables:
cutoff |
Cutoffs provided in argument "cutoff" and / or model-based cutoff values for given sensitivities / specificities. |
Sens |
Sensitivities provided in argument "sens" and / or model-based estimates of the sensitivity for given cutoffs / specificities |
seSens |
Standard error of sensitivity |
lower.Sens, upper.Sens |
Lower and upper confidence limits of the sensitivity |
Spec |
Specificities provided in argument "spec" and / or model-based estimates of the specificity for given cutoffs / sensitivities |
seSpec |
Standard error of specificity |
lower.Spec, upper.Spec |
Lower and upper confidence limits of the specificity |
prevalence |
As defined above. |
PPV |
Positive predictive value (based on the cutoff) |
NPV |
Negative predictive value (based on the cutoff) |
PD |
Probability of disease if the given cutoff value was observed as the measurement for an individual |
dens.nondiseased |
Value of the model-based density function at the cutoff(s) for non-diseased individuals |
dens.diseased |
Value of the model-based density function at the cutoff(s) for diseased individuals |
Gerta RÃ¼cker ruecker@imbi.uni-freiburg.de, Srinath Kolampally kolampal@imbi.uni-freiburg.de, Guido Schwarzer sc@imbi.uni-freiburg.de
# FENO dataset # data(Schneider2017) diag1 <- diagmeta(tpos, fpos, tneg, fneg, cutpoint, studlab = paste(author, year, group), data = Schneider2017, log.cutoff = TRUE) # Results at the optimal cutoff # diagstats(diag1) # Results for cutoffs 25 and 50 (and a prevalence of 10%) # diagstats(diag1, c(25, 50), prevalence = 0.10) # Results for sensitivity and specificity of 0.95 # diagstats(diag1, sens = 0.95, spec = 0.95)