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Gabriel LM
Gabriel LM
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I am currently working a some datasets with count data in R, in which the response is the number of activities of a given type that were performed in one day by a population.

For each type, I build a Poisson model and test for over/underdispersion using the function dispersiontest() from package AER. Depending on the result, I switch to quasi-Poisson model when there is evidence of over- or underdispersion.

In a next stepIstep, I would like to sample and generate simulated data using the results of my models. If I have a Poisson model, I can use rpois() with lambda being the fitted value of the model. However, I have no idea how to do it in the cases of over/underdispersion. Any ideas?

I am currently working a some datasets with count data in R, in which the response is the number of activities of a given type that were performed in one day by a population.

For each type, I build a Poisson model and test for over/underdispersion using the function dispersiontest() from package AER. Depending on the result, I switch to quasi-Poisson model when there is evidence of over- or underdispersion.

In a next stepI would like to sample and generate simulated data using the results of my models. If I have a Poisson model, I can use rpois() with lambda being the fitted value of the model. However, I have no idea how to do it in the cases of over/underdispersion. Any ideas?

I am currently working a some datasets with count data in R, in which the response is the number of activities of a given type that were performed in one day by a population.

For each type, I build a Poisson model and test for over/underdispersion using the function dispersiontest() from package AER. Depending on the result, I switch to quasi-Poisson model when there is evidence of over- or underdispersion.

In a next step, I would like to sample and generate simulated data using the results of my models. If I have a Poisson model, I can use rpois() with lambda being the fitted value of the model. However, I have no idea how to do it in the cases of over/underdispersion. Any ideas?

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edit approved Feb 10, 2017 at 11:49
Ferdi
edit approved Feb 10, 2017 at 11:49
Ferdi
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I am currently working a some datasets with count data in R, in which the response is the number of activities of a given type that were performed in one day by a population.

For each type, I build a Poisson model and test for over/underdispersion using the function dispersiontest()dispersiontest() from package AERAER. Depending on the result, I switch to quasi-Poisson model when there is evidence of over- or underdispersion.

IIn a next stepI would like to, in a next step, sample and generate simulated data using the results of my models. If I have a Poisson model, I can use rpois()rpois() with lambdalambda being the fitted value of the model. However, I have no idea how to do it in the cases of over/underdispersion. Any ideas?

I am currently working a some datasets with count data in R, in which the response is the number of activities of a given type that were performed in one day by a population.

For each type, I build a Poisson model and test for over/underdispersion using the function dispersiontest() from package AER. Depending on the result, I switch to quasi-Poisson model when there is evidence of over or underdispersion.

I would like to, in a next step, sample and generate simulated data using the results of my models. If I have a Poisson model, I can use rpois() with lambda being the fitted value of the model. However, I have no idea how to do it in the cases of over/underdispersion. Any ideas?

I am currently working a some datasets with count data in R, in which the response is the number of activities of a given type that were performed in one day by a population.

For each type, I build a Poisson model and test for over/underdispersion using the function dispersiontest() from package AER. Depending on the result, I switch to quasi-Poisson model when there is evidence of over- or underdispersion.

In a next stepI would like to sample and generate simulated data using the results of my models. If I have a Poisson model, I can use rpois() with lambda being the fitted value of the model. However, I have no idea how to do it in the cases of over/underdispersion. Any ideas?

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Gabriel LM
Gabriel LM
  • 51
  • 4

Sampling from under/over-dispersed count data in R

I am currently working a some datasets with count data in R, in which the response is the number of activities of a given type that were performed in one day by a population.

For each type, I build a Poisson model and test for over/underdispersion using the function dispersiontest() from package AER. Depending on the result, I switch to quasi-Poisson model when there is evidence of over or underdispersion.

I would like to, in a next step, sample and generate simulated data using the results of my models. If I have a Poisson model, I can use rpois() with lambda being the fitted value of the model. However, I have no idea how to do it in the cases of over/underdispersion. Any ideas?