Simulating ED values under various scenarios
Simulating ED values for a given model and given dose values.
simDR(mpar, sigma, fct, noSim = 1000, conc, edVec = c(10, 50), seedVal = 20070723)
mpar |
numeric vector of model parameters |
sigma |
numeric specifying the residual standard deviation |
fct |
list supplying the chosen mean function |
conc |
numeric vector of concentration/dose values |
edVec |
numeric vector of ED values to estimate in each simulation |
noSim |
numeric giving the number of simulations |
seedVal |
numeric giving the seed used to initiate the random number generator |
The arguments mpar and sigma are typically obtained from a previous model fit.
Only dose-response models assuming normally distributed errors can be used.
A list of matrices with as many components as there are chosen ED values. The entries in the matrices are empirical standard deviations of the estimated ED values. Row-wise from top to bottom more and more concentration/dose values are included in the simulations; top row starting with 5 concentrations. The number of replicates increases column by column from left to right.
The list is returned invisbly as the matrices also are displayed.
Christian Ritz
ryegrass.m1 <- drm(ryegrass, fct=LL.4()) simDR(coef(ryegrass.m1), sqrt(summary(ryegrass.m1)$resVar), LL.4(), 2, c(1.88, 3.75, 7.50, 0.94, 15, 0.47, 30, 0.23, 60), seedVal = 200710291)
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