SSl5cons: Self-Starting Nls 5 parameters logistic constraint regression...
In drLumi: Multiplex Immunoassays Data Analysis

Description Usage Arguments Format Value Examples

This selfStart model evaluates the 5 parameters logistic regression model and its gradient for the lower asymptote constraint method. It has an initial attribute that will evaluate initial estimates of the parameters hAsym, Slope, xMid and Asymetry 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)})^{Asymetry}}

1	SSl5cons(..constraint.value,x, Slope, hAsym, xMid, Asymetry)

`..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`	is 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
`Asymetry`	is a numeric parameter representing the asymetry around 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]]

# SSl5
cons <- scluminex("plate_1",sdf$standard, sdf$background,
            lfct="SSl5",
            bkg="constraint",
            fmfi="mfi",
            verbose=FALSE)

summary(cons)

# Comparison constraint vs no constraint (same returning value but estimate
# 4 parameters).
lAsym <- 1
Slope <- 2
hAsym   <- 2
xMid <- 3
Asymetry <- 1.5

concentration <- 2
SSl5(concentration, Slope, lAsym, hAsym, xMid, Asymetry)
SSl5cons(lAsym, concentration, Slope, hAsym, xMid, Asymetry)

  analyte          b        c        d        e obs   rsquare convergence  fct
1     FGF -0.8960132 3.680366 1.440341 1.629947  17 0.9987929 convergence SSl5
[1] 1.985185
attr(,"gradient")
          Slope      lAsym     hAsym       xMid    Asymetry
[1,] 0.03369019 0.01481466 0.9851853 0.06738039 -0.00980292
[1] 1.985185
attr(,"gradient")
          Slope     hAsym       xMid    Asymetry
[1,] 0.03369019 0.9851853 0.06738039 -0.00980292