NPV: computes the Negative Predictive Value.
In evidence: Analysis of Scientific Evidence Using Bayesian and Likelihood Methods
Description
Usage
Arguments
Value
Author(s)
References
See Also
Examples
Description
The negative predictive value (NPV) of a diagnostic test is the probability
that someone with a negative diagnostic test for a condition does not
have the condition. The NPV can easily be calculated from the
prevalence, the sensitivity, and the specificity, but this function
automates the procedure.
Usage
1
Arguments
sens
the sensitivity of the test
spec
the specificity of the test
prev
the prevalence of the disease
Value
the negative predictive value
Author(s)
Robert van Hulst
References
van Hulst, R. 2018. Evaluating Scientific Evidence. ms.
See Also
Examples
1
NPV(0.9, 0.8, 0.003)
evidence documentation built on May 2, 2019, 2:14 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Author(s) References See Also Examples
Description
The negative predictive value (NPV) of a diagnostic test is the probability that someone with a negative diagnostic test for a condition does not have the condition. The NPV can easily be calculated from the prevalence, the sensitivity, and the specificity, but this function automates the procedure.
Usage
1 |
Arguments
sens |
the sensitivity of the test |
spec |
the specificity of the test |
prev |
the prevalence of the disease |
Value
the negative predictive value
Author(s)
Robert van Hulst
References
van Hulst, R. 2018. Evaluating Scientific Evidence. ms.
See Also
Examples
1 | NPV(0.9, 0.8, 0.003)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.