Asymptotic regression model
Providing the mean function and the corresponding self starter function for the asymptotic regression model.
AR.2(fixed = c(NA, NA), names = c("d", "e"), ...)
AR.3(fixed = c(NA, NA, NA), names = c("c", "d", "e"), ...)fixed |
numeric vector. Specifies which parameters are fixed and at what value they are fixed. NAs for parameter that are not fixed. |
names |
vector of character strings giving the names of the parameters (should not contain ":"). |
... |
additional arguments to be passed from the convenience functions. |
The asymptotic regression model is a three-parameter model with mean function:
f(x) = c + (d-c)(1-\exp(-x/e))
The parameter c is the lower limit (at x=0), the parameter d is the upper limit and the parameter e>0 is determining the steepness of the increase as x.
A list of class drcMean, containing the mean function, the self starter function,
the parameter names and other components such as derivatives and a function for calculating ED values.
The functions are for use with the function drm.
Christian Ritz
## First model met.as.m1<-drm(gain ~ dose, product, data = methionine, fct = AR.3(), pmodels = list(~1, ~factor(product), ~factor(product))) plot(met.as.m1, log = "", ylim = c(1450, 1800)) summary(met.as.m1) ## Calculating bioefficacy: approach 1 coef(met.as.m1)[5] / coef(met.as.m1)[4] * 100 ## Calculating bioefficacy: approach 2 EDcomp(met.as.m1, c(50,50)) ## Simplified models met.as.m2<-drm(gain ~ dose, product, data = methionine, fct = AR.3(), pmodels = list(~1, ~1, ~factor(product))) anova(met.as.m2, met.as.m1) # simplification not possible met.as.m3 <- drm(gain ~ dose, product, data = methionine, fct = AR.3(), pmodels = list(~1, ~factor(product), ~1)) anova(met.as.m3, met.as.m1) # simplification not possible
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