drLumi (version 0.1.2)

loq_cv: Limits of quantifications estimation using coefficient of variation

Description

Estimates the limits of quantification based on an approximation of the coefficient of variation.

Usage

loq_cv(x, subset.list = NULL, max.cv = 0.2, n.cuts = 100)

Arguments

x

a scluminex object

subset.list

list of analytes to estimate. Default NULL (all analytes of the scluminex object).

max.cv

is the target coeficient of variation by default 0.2

n.cuts

is the number of cuts to search the coefficent of variation. Default 100.

Value

• Object of class loq.

Details

For each value of the response, the estimated concentration value and the approximated standard deviation is estimated. The function invest with the delta method approach is used. The coefficient of variation of the log10 concentration is calculated as the $$\sqrt{e^{ (SE \times log(10))^2} - 1 }$$

References

Gottschalk PG, and Dunn JR. (2005). Determining the error of dose estimates and minimum and maximum acceptable concentrations from assays with nonlinear dose-response curves. Comput Methods Programs Biomed 80, 204-215.

Defawe OD, Fong Y, Vasilyeva E, Pickett M, Carter DK, Gabriel E, Rerks-Ngarm S, Nityaphan S, Frahm N, McElrath MJ and De Rosa SC.(2012). Optimization and qualification of a multiplex bead array to assess cytokine and chemokine production by vaccine-specific cells. J Immunol Methods 382, 117-128.

Examples

# Load data and estimate models
data(ecdata)
data(mfidata)

dat <- mfidata[mfidata$plate=="plate_1" & mfidata$analyte=="FGF",]

sdf <- data_selection(dat, ecdata)$plate_1

igmodels <- scluminex("plate_1",sdf$standard, sdf$background,
lfct=c("SSl4", "SSl5"), bkg="ignore", fmfi="mfi", verbose=FALSE)

loq_cv(igmodels, max.cv=0.25, n.cuts=100)