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inst/dpcReport/prob_distr/prob_distr4.md
In dpcR: Digital PCR Analysis
There are several methods of calculating confidence intervals for binomial data (e.g., a status of partitions):
Agresti-Coull.
Asymptotic.
Bayesian inference.
Cloglog parameterization.
Logit parameterization.
Pearson-Klopper.
Probit parameterization.
Proportion test.
* Wilson.
The Wilson confidence intervals are the most beneficial for dPCR experiment analysis, because they do not oscillate when the λ is close to 0. For a deeper analysis, please refer to Brown, 2001 (full references list in the About panel).
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dpcR documentation built on May 2, 2019, 7:04 a.m.
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There are several methods of calculating confidence intervals for binomial data (e.g., a status of partitions): Agresti-Coull. Asymptotic. Bayesian inference. Cloglog parameterization. Logit parameterization. Pearson-Klopper. Probit parameterization. Proportion test. * Wilson.
The Wilson confidence intervals are the most beneficial for dPCR experiment analysis, because they do not oscillate when the λ is close to 0. For a deeper analysis, please refer to Brown, 2001 (full references list in the About panel).
Try the dpcR package in your browser
Any scripts or data that you put into this service are public.
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.
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