I am dealing with a very hard-to-work data set: fish larval density. It is a semicontinuous data, with 90% of zeros and a right-skewed distribution, with few very large values.
One problem is that my zeros have two sources: true and false, or structural and sampling.
I saw models that can deal with these two types of zeros, called Zero inflated models, but only for count data.
I didn't see models for semicontinuous data that can deal with false zeros. These models are two-part models with log-normal or gamma distribution, and all of them assume true zeros. I am searching for some model that can deal with 2 sources of zero.
Here is a part of my data set: My response is fish larval density (Dprochilodus). And I have a lot of zeros, but I can't assume that all of them are trully zeros. What I want to tell is: when I have zero, it doesn't mean there were no larvae. Can be because I could't sample/detect them. Here there are some explanations about the different models for true and false zeros
ponto Dprochilodus periodo Dif_his.y temp
Length:574 Min. : 0.0000 I :278 Min. :0.01571 Min. :21.00
Class :character 1st Qu.: 0.0000 II:296 1st Qu.:0.09738 1st Qu.:24.00
Mode :character Median : 0.0000 Median :0.22505 Median :27.00
Mean : 0.5949 Mean :0.25405 Mean :26.87
3rd Qu.: 0.0000 3rd Qu.:0.40296 3rd Qu.:29.01
Max. :134.7469 Max. :0.89746 Max. :33.00
