SSl4cons: Self-Starting Nls 4 parameters logistic constraint regression...
In drLumi: Multiplex Immunoassays Data Analysis

Description Usage Arguments Format Value Examples

This selfStart model evaluates the 4 parameters logistic regression model and its gradient. It has an initial attribute that will evaluate initial estimates of the parameters hAsym, Slope and xMid for a given set of data. Instead of the standard exp function this implementation use the 10^ function.

f(x)=lAsym +\frac{hAsym-lAsym}{1+10^{Slope(x-xMid)}}

1	SSl4cons(..constraint.value, x, Slope, hAsym, xMid)

`..constraint.value`	a numeric value representing the lower asymptote when `x` trend to `-Inf`. In this function this value is not a parameter is just a numeric value to constraint `lAsym` parameter.
`x`	a numeric vector of values at which to evaluate the model
`Slope`	a numeric parameter representing the `-slope` of the function at the inflection point
`hAsym`	a numeric parameter representing the higher asymptote when `x` trend to `Inf`
`xMid`	is the x value corresponding to the inflection point

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)[[1]]

cons <- scluminex("plate_1",sdf$standard, sdf$background,
           lfct="SSl4",
           bkg="constraint",
           fmfi="mfi",
           verbose=FALSE)

summary(cons)

# Comparison constraint vs no constraint (same returning value but
# estimate 3 parameters).
lAsym <- 1
Slope <- 2
hAsym   <- 2
xMid <- 3
concentration <- 2
SSl4(concentration, Slope, lAsym, hAsym, xMid)
SSl4cons(lAsym, concentration, Slope, hAsym, xMid)