thin or get less iteration of only certain parameters (request)

Summary:

It would be nice to be able to get less samples of some parameters (e.g. predictions or less log-likelihoods from generated quantities section) to avoid models of more than 4GB.

Description:

I usually run a model with 2000 iterations and 4 chains. I have predicted values, and the log likelihood of every observation in my generated quantities, but I don't really need 4000 samples of each one.
And if I don't exclude them when I fit a model with stan(...) and I end up with a model of more than 4gb. (As you know R is not very friendly to huge files).

(Edited:Ok, I see now, that it's possible to save directly a huge csv file, without loading all the samples in memory. It still would be nice to be able to split the file between the main parameters, and those that clog R like likelihoods or predicted values, or to only save them)

Reproducible Steps:

Just using model <- stan(...) without excluding parameters

Current Output:

A model with all the samples of all the parameters > 1gb.

Expected Output:

A model with less the samples of some of the parameters.

RStan Version:

2.14.1

R Version:

3.3.2 (2016-10-31)

Operating System:

Ubuntu 16.04

Start from the stan(...) call described in the issue and trace how generated-quantity predictions and log-likelihood samples are retained in the fitted model. Clarify the intended selection and sampling behavior, then define tests showing that selected parameters can be stored with fewer samples while the remaining model output is preserved.