And why is the first term of ifelse(x == 0, zero, 0) + dpois(x, lambda) / (1 -
zero)
ifelse(x == 0, zero, 0)
rather than something corresponding to
zero+(1-zero)e^{-lambda}
https://en.wikipedia.org/wiki/Zero-inflated_model#Zero-inflated_Poisson
> On 22 Mar 2016, at 14:25, Matti Viljamaa <mvilja...@kapsi.fi> wrote:
>
> Could you clarify what are the parameters and why it’s formulated that way?
>
> -Matti
>
>> On 22 Mar 2016, at 14:17, Thierry Onkelinx <thierry.onkel...@inbo.be
>> <mailto:thierry.onkel...@inbo.be>> wrote:
>>
>> Dear Matti,
>>
>> What about this?
>>
>> dzeroinflpois <- function(x, lambda, zero){
>> ifelse(x == 0, zero, 0) + dpois(x, lambda) / (1 - zero)
>> }
>> plot(x, dzeroinflpois(x, lambda = 10, zero = 0.2), type = "l")
>>
>>
>>
>> ir. Thierry Onkelinx
>> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
>> Forest
>> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
>> Kliniekstraat 25
>> 1070 Anderlecht
>> Belgium
>>
>> To call in the statistician after the experiment is done may be no more than
>> asking him to perform a post-mortem examination: he may be able to say what
>> the experiment died of. ~ Sir Ronald Aylmer Fisher
>> The plural of anecdote is not data. ~ Roger Brinner
>> The combination of some data and an aching desire for an answer does not
>> ensure that a reasonable answer can be extracted from a given body of data.
>> ~ John Tukey
>>
>> 2016-03-22 13:04 GMT+01:00 Matti Viljamaa <mvilja...@kapsi.fi
>> <mailto:mvilja...@kapsi.fi>>:
>> I’m doing some optimisation that I first did with normal Poisson (only
>> parameter theta was estimated), but now I’m doing the same with a
>> zero-inflated Poisson model which
>> gives me two estimated parameters theta and p (p is also pi in some
>> notation).
>>
>> My question is, is there something equivalent to dpois that would use both
>> of the parameters (or is the p parameter possibly unnecessary)?
>>
>> I’m calculating the “fit” of the Poisson model
>>
>> i.e. like
>>
>> x = c(0,1,2,3,4,5,6)
>> y = c(3062,587,284,103,33,4,2)
>> fit1 <- sum(y)*dpois(x, est_theta)
>>
>> and then comparing fit1 to the real observations.
>> [[alternative HTML version deleted]]
>>
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>
[[alternative HTML version deleted]]
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