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

## Description

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)}}

## Usage

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

## Arguments

 ..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

## Format

A selfStart model

## Value

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

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 # 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) 

drLumi documentation built on May 30, 2017, 5:47 a.m.