SSbiexp

0th

Percentile

Self-Starting Nls Biexponential model

This selfStart model evaluates the biexponential model function and its gradient. It has an initial attribute that creates initial estimates of the parameters A1, lrc1, A2, and lrc2.

Keywords
models
Usage
SSbiexp(input, A1, lrc1, A2, lrc2)
Arguments
input

a numeric vector of values at which to evaluate the model.

A1

a numeric parameter representing the multiplier of the first exponential.

lrc1

a numeric parameter representing the natural logarithm of the rate constant of the first exponential.

A2

a numeric parameter representing the multiplier of the second exponential.

lrc2

a numeric parameter representing the natural logarithm of the rate constant of the second exponential.

Value

a numeric vector of the same length as input. It is the value of the expression A1*exp(-exp(lrc1)*input)+A2*exp(-exp(lrc2)*input). If all of the arguments A1, lrc1, A2, and lrc2 are names of objects, the gradient matrix with respect to these names is attached as an attribute named gradient.

See Also

nls, selfStart

Aliases
  • SSbiexp
Examples
library(stats) # NOT RUN { Indo.1 <- Indometh[Indometh$Subject == 1, ] SSbiexp( Indo.1$time, 3, 1, 0.6, -1.3 ) # response only A1 <- 3; lrc1 <- 1; A2 <- 0.6; lrc2 <- -1.3 SSbiexp( Indo.1$time, A1, lrc1, A2, lrc2 ) # response and gradient print(getInitial(conc ~ SSbiexp(time, A1, lrc1, A2, lrc2), data = Indo.1), digits = 5) ## Initial values are in fact the converged values fm1 <- nls(conc ~ SSbiexp(time, A1, lrc1, A2, lrc2), data = Indo.1) summary(fm1) ## Show the model components visually require(graphics) xx <- seq(0, 5, len = 101) y1 <- 3.5 * exp(-4*xx) y2 <- 1.5 * exp(-xx) plot(xx, y1 + y2, type = "l", lwd=2, ylim = c(-0.2,6), xlim = c(0, 5), main = "Components of the SSbiexp model") lines(xx, y1, lty = 2, col="tomato"); abline(v=0, h=0, col="gray40") lines(xx, y2, lty = 3, col="blue2" ) legend("topright", c("y1+y2", "y1 = 3.5 * exp(-4*x)", "y2 = 1.5 * exp(-x)"), lty=1:3, col=c("black","tomato","blue2"), bty="n") axis(2, pos=0, at = c(3.5, 1.5), labels = c("A1","A2"), las=2) ## and how you could have got their sum via SSbiexp(): ySS <- SSbiexp(xx, 3.5, log(4), 1.5, log(1)) ## --- --- stopifnot(all.equal(y1+y2, ySS, tolerance = 1e-15)) # }
Documentation reproduced from package stats, version 3.5.0, License: Part of R 3.5.0

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