If I use 'quasipoisson' as family to GLM on non-integer data, can it be treated as poisson? I'm trying to build a model based a data from package GLMsData
library(GLMsData)
data(lime)

my model is,
m <- glm(Foliage ~ DBH + Age + Origin, data = lime, 
            family = poisson (link = "log"))

here, Foliage is non integer, so it gives me inf AIC.
when I use 'quasipoisson' as family, it gives AIC 'NA' and overdispersion parameter around 0.8.

*

*Can I treat this as a Poisson?

*If not, what would the best family link to use?


The GLMsData package contains the datasets used as examples in the book by Peter Dunn and myself (Dunn and Smyth, 2018).
The lime dataset is used in the book as an example of gamma regression. The main model used a log-link with Foliage as the reponse and a linear model that included a linear regression on log(DPH) for each level of Origin. The book included a careful analysis to show that the gamma mean-variance relationship is appropriate for this data.
Certainly the data would not be suitable for Poisson or quasi-Poisson regression.
Reference
Dunn PK, Smyth GK (2018). Generalized linear models with examples in R. Springer, New York, NY.
https://www.amazon.com/Generalized-Linear-Examples-Springer-Statistics/dp/1441901175
