Nothing
# gamma/shifted Gompertz (G/SG) function ----------------------------------------------------
#
# References
# Bemmaor, A.C. 1994. Modeling the Diffusion of New Durable Goods: Word-of-Mouth
# Effect versus Consumer Heterogeneity. In G. Laurent, G.L. Lilien, and B. Pras
# (Eds.). Research Traditions in Marketing. Boston: Kluwer. pp. 201-223.
#
# Bemmaor, A.C. and Lee, J. 2002. The Impact of Heterogeinity and
# Ill-Conditioning on Diffusion Model Paremeter Estimates. Marketing Science,
# 21(2), 209-220.
#
# author Oliver Schaer, info@oliverschaer.ch
gsgCurve <- function(n, w){
# Generate Gompertz curve
# n, sample size
# w, vector of parameters
t <- 1:n
# Cumulative
# At <- w[4] * ((1 - exp(-w[2] * t)) * (1 + w[1] * exp(-w[2] * t))^-w[3])
At <- w[1] * ((1 - exp(-w[3] * t)) * (1 + w[2] * exp(-w[3] * t))^-w[4])
# Adoption
at <- diff(c(0, At))
x <- cbind(At, at)
colnames(x) <- c("Cumulative Adoption", "Adoption")
return(x)
}
gsgInit <- function(y, loss, method, multisol, initpar, mscal){
# Internal function: get initial values
# We use Bass model paramters assuming c = 1 (see Bemmaor 1994)
# y in adoption per period
# calling bass estimates
what <- diffusionEstim(y, loss, pvalreps = 0, type = "bass", method = method,
multisol = multisol, initpar = initpar, mscal = mscal)$w
# Bemmaor shows that if a = 1, Beta = p/q and b = p + q
a <- what[2] / what[3] # the shape parameter beta
b <- what[2] + what[3] # the scale parameter b
m <- what[1] # the market size
c <- 1 # this is the shifting parameter alpha
w <- c(m, a, b, c)
names(w) <- c("m", "a", "b", "c")
return(w)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.