R help - Nov 2012 - Problems with nls

The problem is badly scaled -- parameters from very small to very big.

nlmrt package manages a bit better, but scaling would likely make it and 
nls both happier. nlxb insists on having its data in a data frame.

Here's my code

rm(list=ls())
Laptop_sale <- c(1405,
1863,2027,2669,2938,5275,6595,6943,8621,10905,12420,22400,32380,31600,34900,43163,47838,47592)
CuSale <- Laptop_sale
time <- seq_along(Laptop_sale)
Bass.Model <- Laptop_sale ~ m * ((p + q)^2/p) * (exp(-(p + q) * time)/((q /
p) * exp(-(p + q) * time) + 1)^2)
Bass.Fit <- nls(formula = Bass.Model, start = c(p = 0.03, q = 0.4, m 
=max(CuSale)), trace = TRUE)
Bass.Fit
library(nlmrt)
xdata<-data.frame(Laptop_sale, time)
Bass.Fitn <- nlxb(formula = Bass.Model, start = c(p = 0.03, q = 0.4, m = 
max(CuSale)), data=xdata,trace=TRUE)
Bass.Fitn


JN


> Message: 22
> Date: Tue, 27 Nov 2012 07:11:13 -0800 (PST)
> From: Yifen <yifen.chen at gmail.com>
> To: r-help at r-project.org
> Subject: Re: [R] Problems with nls
> Message-ID: <1354029073140-4650971.post at n4.nabble.com>
> Content-Type: text/plain; charset=us-ascii
>
> Hi all,
>
> I am encountering the same problem when I use nls to estimate the Bass
> diffusion model. I used similar code in the previous replies and it works
> with the data in this original post. However, I got an error when I use my
> own data.
>
> Error in nls(formula = Bass.Model, start = c(p = 0.03, q = 0.4, m >
max(CuSale)),  :
>   step factor 0.000488281 reduced below 'minFactor' of 0.000976563
>
>
> Here is my code:
>
> Laptop_sale <- c(1405,
>
1863,2027,2669,2938,5275,6595,6943,8621,10905,12420,22400,32380,31600,34900,43163,47838,47592)
> CuSale <- Laptop_sale
> time <- seq_along(Laptop_sale)
> Bass.Model <- Laptop_sale ~ m * ((p + q)^2/p) * (exp(-(p + q) *
time)/((q /
> p) * exp(-(p + q) * time) + 1)^2)
> Bass.Fit <- nls(formula = Bass.Model, start = c(p = 0.03, q = 0.4, m
> max(CuSale)), trace = TRUE)
>
>
> Can someone help me to figure out what the problem is? Thank you very much.

I'm trying to fit the Bass Diffusion Model using the nls function in R but 
I'm running into a strange problem. The model has either two or three 
parameters, depending on how it's parameterized, p (coefficient of 
innovation), q (coefficient of immitation), and sometimes m (maximum market 
share). Regardless of how I parameterize the model I get an error saying 
that the step factor has decreased below it's minimum. I have tried 
re-setting the minimum in nls.controls but that doesn't seem to fix the 
problem. Likewise, I have run through a variety of start values in the past 
few days, all to no avail. Looking at the trace output it appears that R 
believes I always have one more parameter than I actually have (i.e. when 
the model is parameterized with p and q R seems to be seeing three 
parameters, when m is also included R seems to be seeing four). My 
experience with nls is limited, can someone explain to me why it's doing 
this? I've included the data set I'm working with (published in
Michalakelis
et al. 2008) and some example code. 

## Assign relevant variables 
adoption <- 
c(167000,273000,531000,938000,2056452,3894103,5932090,7963742,9314687,10469060,11393302,11976340)
time <- seq(from = 1,to = 12, by = 1) 
## Models 
Bass.Model <- adoption ~ ((p + q)^2/p) * (exp(-(p + q) * time)/((q / p) * 
exp(-(p + q) * time) + 1)^2) 
## Starting Parameters 
Bass.Params <- list(p = 0.1, q = 0.1) 
## Model fitting 



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