FitLogistic: Fit logistic functions

In ModelDataComp: Model-Data Comparison

Description

Fits logistic functions between one or several predictor variables and a response variable. In case of multi-variate fits, logistic functions can be combined either additatively or multiplicatively:
Additive: y = Logistic(x1) + Logistic(x2) + Logistic(xN)
Multiplicative: y = Logistic(x1) * Logistic(x2) * Logistic(xN)

Usage

FitLogistic(x, y, additive = FALSE, method = c("genoud"), ninit = 50, 
    ...)

Arguments

x	predictor variables
y	response variables
additive	Make an additive or multiplicative fit? The default is a multiplicative fit.
method	Method to be used for optimization of fit parameters. "genoud" uses genetic optimization (see genoud). Can be also one of "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", "Brent" (see optim). If two methods are provided, the second method is applied on the best result of the first method.
ninit	number of inital parameter sets for the optimization. Inital parameter sets will be included in genoud as part of the first generation. Each initial parameter set will be used as starting value within optim methods. A higher number of ninit will likely avoid solutions close to local optima but is computationally more expensive.
...	further arguments for genoud or optim

Details

No details.

Value

An object of class 'FitLogistic' which is actually a list.

Author(s)

Matthias Forkel <[e-mail]> [aut, cre]

References

No reference.

See Also

Logistic

Examples

# 1D example
x <- 1:1000
y <- Logistic(c(1, 0.01, 500), x) + rnorm(1000, 0, 0.01)
plot(x, y)
fit <- FitLogistic(x, y)
lines(x, fit$predicted, col="red")
fit$par # fitted parameter

# performance of fit
ScatterPlot(fit$predicted, y, objfct=TRUE)

## 2D example - takes more time
#n <- 1000
#x1 <- runif(n, 0, 100)
#x2 <- runif(n, 0, 100)
#f1 <- Logistic(c(1, 0.1, 50), x1)
#f2 <- Logistic(c(1, -1, 20), x2)
#plot(x1, f1)
#plot(x2, f2)
#y <- f1 * f2 * rnorm(n, 1, 0.1)
#plot(x1, y) 
#plot(x2, y)
#fit <- FitLogistic(x=cbind(x1, x2), y)
#ScatterPlot(fit$predicted, y, objfct=TRUE)

ModelDataComp documentation built on Nov. 22, 2020, 3 a.m.