predict.assaytest: Construct effective standard deviation intervals for observed...

In testassay: A Hypothesis Testing Framework for Validating an Assay for Precision

Description

Computes effective standard deviation intervals for observed assay results. These intervals have at least 68.27 percent coverage.

Usage

## S3 method for class 'assaytest'
predict(object, newdata, ...)

Arguments

object	An object of class "assaytest"
newdata	A vector of observed values. If missing, uses object$x.
...	additional arguments

Details

Takes the Umax element from the assaytest object and treats it as the known precision parameter. For the constant SD model, the effective standard deviation interval for observed data value y is (y-Umax, y+Umax). For the constant CV models the effective SD interval uses either normConstCVCI (for the "normal" model) or lognormConstCVCI (for the "lognormal" model).

Although Umax is an upper bound (not an estimate) of the precision parameter, simulations have shown that treating Umax as the true precision parameter gives effective SD intervals with coverage of at least 68.27 percent (see Fay, Sachs, and Miura, 2016).

Value

A data frame with the observed values, lower, and upper confidence limits

References

Fay, MP, Sachs, MC, and Miura, K (2016). A Hypothesis Testing Framework for Validating and Assay for Precision (unpublished manuscript).

testassay documentation built on July 1, 2020, 10:25 p.m.