I’ve been trying to model a time series (weeks) of cumulative values using GP and a negbinomial likelihood as this:

`EA_cumsum ~ 1 + gp(log2(week))`

and

`S_cumsum ~ 1 + gp(log2(week))`

with this result:

Rplot-GP.pdf (13.6 KB)

compared to using non-GP as, e.g., `S_cumsum ~ log2(week)`

with this result:

gamma-poisson.pdf (18.5 KB)

For some reason, in the GP case the `EA`

curve goes down even though I’m dealing with cumulative data. I presume it is that at the end of the dataset `EA`

plateaus:

```
> tail(d$EA_cumsum)
[1] 40945 40968 40968 40968 40968 40968
```

Is there a way to tell the GP model somehow that it is cumulative values and that it never can decrease?