simhetdr: Simulate nonlinear heteroscedastic dose-response data
In TJMurphy/nlfitr: Fitting models to nonlinear data

Description Usage Arguments Value Examples

A sandbox to simulate and visualize random normal heteroscedastic response data. Variances enlarge with the value of y predicted by the model using a constant coefficeint of variation (cv). The data generating formula is derived from the general hyperbolic model: y/ymax=x^h/(x^h+k^h). Failure errors in the plot fitting subfunction will occasionally happen due to the random data. These are more frequent with higher cv values. Just re-simulate with modified parameter values. The regression formula is 'y ~ ylo + (yhi - ylo)*x^h/(x^h + k^h)'

1	simhetdr(x, k, ylo, yhi, h, cv, reps, weight = F, log = F)

`x`	a vector of non-exponential linear scale values, usually representing dose or concentration, but can represent any stimulus.
`k`	the value of x that yields y/ymax = 0.5, usually EC50 or ED50.
`ylo`	the lowest expected y value, in response units.
`yhi`	the highest expected y value, in response units.
`h`	the Hill slope, a unitless slope factor; -1 > h > 1 is steeper, -1 < h < 1 is shallower. Use negative value for downward sloping response.
`cv`	the coefficient of variation for y replicates.
`reps`	an integer value for number of replicates
`weight`	logical value indicating y scale weighting. Default is FALSE. If TRUE, curve is fit using relative (1/y^2) weighting.
`log`	logical value. Default is FALSE. If TRUE, linear x values are transformed using a log10 function for plotting. Only for visual aesthetic.

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, ...)

dose <- c(1, 3, 10, 30, 100, 300) # eg, in nM units

set.seed(2345)

hetdat <- simhetdr(dose, k = 35, ylo = 100, yhi = 1000,
h = 1.0, cv = 0.10, reps = 5, weight=TRUE, log = TRUE ); hetdat

hetdat$data