Stefan, This is a good question. For the specific example, you are quite correct. However, the motivation for the question came from using the MonetDB-rmath extension and embedded R: often it is more efficient to calculate a number of values (e.g. the confidence intervals and p-values) together and then return all of the values as a table. This probably also reflects that I am a statistician and an old R user. The recent article by Raasveldt suggested that analysis objects from Python could be stored natively rather than being serialised BLOBs. This could be further extended to storing R objects natively. Members of the objects could then be extracted as they are needed. Admittedly, this would lose many relational advantages. My suggestion is halfway, where R and Python can be used for analytics, with simple relational storage of the results. Actually, is there any way to optimise a lateral call using BATs? -- Mark On 13 Jun 2018 17:43, Stefan Manegold wrote: Hi Mark, just out of curiosity, (in the given case/example) what's wrong with a simple select *, mean-1.96*sd as lci, mean+1.96*sd as uci from test; or create or replace function ci(se double) returns double begin return 1.96*se; end; select *, mean-ci(sd) as lci, mean+ci(sd) as uci from test; in case you really want/need to avoid the "redundant" occurrence or "1.96" ... Thanks! Best, Stefan ----- On Jun 13, 2018, at 5:30 PM, Mark Clements mark.clements@ki.se wrote:

Jennie,

Thank you for replying to this inquiry - it's appreciated.

Your solution gives the right result - but it requires changing the function call and will not generalise easily to other column types or other number of covariates. If we had another table such as:

drop table test; create table test as select 1 as id, 2 as sex, cast(3 as double) as mean, cast(4.0 as double) as sd; -- and repeating the function create or replace function cis(mu double, se double) returns table(lci double, uci double) begin return select mu-1.96*se, mu+1.96*se; end;

we can get all of the test table combined with the confidence interval by:

-- row_number() select * from (select *, row_number() over() as rid from test) as t1 natural join (select *, row_number() over() as rid from cis((select mean,sd from test))) as t2; -- or using lateral select * from test, lateral cis(test.mean,test.sd);

The row_number solution is fast, and the lateral solution is simple. I was hoping for:

select *, cis(mean,test).* from test;

Again, thank you for your help.

-- Mark

On 06/08/2018 01:53 PM, Ying Zhang wrote:

...

Hai Mark,

I’m just wonder if this gives you wat you want:

create function cis(mu double, se double) returns table(mu_out double, lci double, uci double) begin return select mu, mu-1.96*se, mu+1.96*se; end;

Regards, Jennie

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