SSexpcons: Self-Starting Nls exponential constraint regression model
In drLumi: Multiplex Immunoassays Data Analysis

Description Usage Arguments Format Value Examples

This selfStart model evaluates the exponential growth regression model and its gradient. It has an initial attribute that will evaluate initial estimates of the parameters y0, and b for a given set of data. Instead of the standard exp function this implementation use the 10^ function.

f(x)=y_0 \times 10^b

1	SSexpcons(..constraint.value, x, b)

`..constraint.value`	a numeric value representing the value of the response when `x` is 0. In this function this value is not a parameter is just a numeric value to constraint `y_0` parameter.
`x`	a numeric vector of values at which to evaluate the model
`b`	a numeric parameter representing the growth rate

A selfStart model

The value returned is a list containing the nonlinear function, the self starter function and the parameter names.

# Load data
data(ecdata)
data(mfidata)

# Select analyte FGF for plate 1
dat <- mfidata[mfidata$plate=="plate_1" & mfidata$analyte=="FGF",]

sdf <- data_selection(dat, ecdata)$plate_1

cons <- scluminex("plate_1",sdf$standard, sdf$background,
            lfct="SSexp",
            bkg="constraint",
            fmfi="mfi",
            verbose=FALSE)

summary(cons)

# Comparison constraint vs no constraint (same returning value but estimate
# one parameter).
b <- 3
y0 <- 1
concentration <- 2
SSexp(concentration, b, y0)
SSexpcons(y0, concentration, b)