ucl: Calculate upper control limit (UCL)
In jmeydam/countcheck: Detecting Count Anomalies Using Bayesian Hierarchical Models
ucl R Documentation
Calculate upper control limit (UCL)
Description
Basic formula for UCL:
estimated mean + 3 * estimated standard deviation
Usage
ucl(theta_hat, n_new, factor_sd = 3)
Arguments
theta_hat
Estimate (unless known) of rate parameter theta
n_new
New value for exposure (reference counts), must at least be 1
factor_sd
Factor multiplying standard deviation (default: 3)
Details
In this case, assuming a Poisson distribution, both the estimated mean
and the estimated variance are
lambda_hat = theta_hat * n_new.
Rounds the result to the nearest integer.
Adds 0.5, so that count values are either above or below the UCL
(as in Wheeler & Chambers [1992]).
Value
UCLs
jmeydam/countcheck documentation built on Aug. 16, 2024, 11:34 a.m.
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| ucl | R Documentation |
Calculate upper control limit (UCL)
Description
Basic formula for UCL: estimated mean + 3 * estimated standard deviation
Usage
ucl(theta_hat, n_new, factor_sd = 3)
Arguments
theta_hat |
Estimate (unless known) of rate parameter theta |
n_new |
New value for exposure (reference counts), must at least be 1 |
factor_sd |
Factor multiplying standard deviation (default: 3) |
Details
In this case, assuming a Poisson distribution, both the estimated mean and the estimated variance are lambda_hat = theta_hat * n_new.
Rounds the result to the nearest integer.
Adds 0.5, so that count values are either above or below the UCL (as in Wheeler & Chambers [1992]).
Value
UCLs
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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