simdecay2: Simulate two phase exponential decay data
In TJMurphy/nlfitr: Fitting models to nonlinear data

Description Usage Arguments Value Examples

A sandbox to simulate and visualize random normal nonlinear response data for a system that decays in two phases. The data generating formula is derived from the general model: 'y = y0*e^-((k1+k2)*x)'. A two-phase model is used when the outcome you measure is the result of the sum of a fast and slow exponential decay. This is also called a double exponential decay. Failure errors in the plot fitting subfunction will happen with some freqeuncy due to the random data. These warnings are a useful sign. They are more frequent with fewer time points, higher sd, and lower replicates. J ust re-simulate with modified parameter values. The regression formula is: 'y ~ range1*exp(-k1*x) + range2*exp(-k2*x) + ylo'

1	simdecay2(x, k1, k2, range1, range2, ylo, sd, reps)

`x`	a vector of non-exponential linear scale values representing time.
`k1`	the first rate constant, expressed in reciprocal of the X axis time units. The first half-life is 0.6932/k1.
`k2`	the second rate constant, expressed in reciprocal of the X axis time units. The second half-life is 0.6932/k2.
`range1`	a single value for the range of y in the first phase of decay, in y units.
`range2`	a single value for the range of y in the second phase of decay, in y units..
`ylo`	the lowest expected y value, or the value at infinite times, expressed in the same units as Y.
`sd`	the coefficient of variation for y replicates.
`reps`	an integer value for number of replicates

ggplot, data

# Note: exponential or log-transformed x scale values will not work
# do not use x = c(1e-9, 3e-9, ...) or c(-9, -8.523, ...)

time <- c(1,2,3,4, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25) # eg, in mins

set.seed(2346)

decayTwodat <- simdecay2(time, k1=0.23, k2=0.05, range1=10, range2=85, ylo=1, sd=2, reps=5)

decayTwodat

decayTwodat$data