computeMetrics | R Documentation |
Compute method performance metrics
computeMetrics(
logRr,
seLogRr = NULL,
ci95Lb = NULL,
ci95Ub = NULL,
p = NULL,
trueLogRr
)
logRr |
A numeric vector of effect estimates on the log scale. |
seLogRr |
The standard error of the log of the effect estimates. Hint: often the standard error = (log(<lower bound 95 percent confidence interval>) - log(<effect estimate>))/qnorm(0.025). If not provided the standard error will be inferred from the 95 percent confidence interval. |
ci95Lb |
The lower bound of the 95 percent confidence interval. IF not provided it will be inferred from the standard error. |
ci95Ub |
The upper bound of the 95 percent confidence interval. IF not provided it will be inferred from the standard error. |
p |
The two-sided p-value corresponding to the null hypothesis of no effect. IF not provided it will be inferred from the standard error. |
trueLogRr |
A vector of the true effect sizes |
Compute the AUC, coverage, mean precision, MSE, type 1 error, type 2 error, and the fraction non- estimable.
library(EmpiricalCalibration)
data <- simulateControls(n = 50 * 3, trueLogRr = log(c(1, 2, 4)))
computeMetrics(logRr = data$logRr, seLogRr = data$seLogRr, trueLogRr = data$trueLogRr)
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