I'm trying to find out what distribution my empirical count data fits. Scores can run from 0-8 and I have ~360 observations. I've been using the
fitdistrplus package and have tested a poisson distribution. The gof stats I got were:
Fitting of the distribution ' pois ' by maximum likelihood Parameters : estimate Std. Error lambda 4.490489 0.07811023 Loglikelihood: -1571.578 AIC: 3145.155 BIC: 3149.756
I tried testing a negative binomial also but got this error message:
> fitnbinom <-fitdist(survival,"nbinom") Warning messages: 1: In dnbinom(c(4L, 3L, 1L, 1L, 6L, 5L, 4L, 3L, 4L, 1L, 0L, 0L, 6L, : NaNs produced 2: In dnbinom(c(4L, 3L, 1L, 1L, 6L, 5L, 4L, 3L, 4L, 1L, 0L, 0L, 6L, : NaNs produced
I'm not sure the fit of the Poisson is that good so would like to test an alternative. Any suggestions? Also any help with the code for testing an alternative in
fitdistrplus would be great - The examples provided in the vignette for count data test a Poisson and negative binomial but no others. I'm aware that you can test distributions provided in other packages but I haven't been able to work out how to do this.