Question

[shahlaebrahimi]

@osofr Hi
I would greatly appreciate if you could let me know whether your code is suitable for my data set which is as follows. In fact, it is an unbalanced longitudinal data set with time varying features. I want to predict RE to CS using x1 to x5 features.

ID	year	RE to CS	  x1       x2	             x3	              x4               x5
1	1	0.06039	   1.28102412	0.022933584	0.87453816	1.216366609	0.06094049
1	2	0.01064	   1.270012471	0.00645422	0.820672937	1.004861122	-0.014079609
1	3	-0.45597   1.052890304	-0.059378881	0.922421512	0.729264145	0.020475912
1	4	-0.32539   1.113115232	-0.01522879	0.858878436	0.809737564	0.07603735
1	5	-0.56657   1.219644234	-0.058675441	0.887087711	0.484342194	0.009777888
2	1	1.25097	   1.06226374	0.107020836	0.814602294	0.835928139	0.19996023
2	2	1.35725	   1.055785531	0.081916221	0.879486383	0.686727862	0.142627013
2	3	0.00719	   0.970588058	0.076063501	0.906774596	0.809795658	0.165915285
2	4	1.20019	   1.058995743	0.130202682	0.818111675	0.875989179	0.23445163
2	5	2.23481	   1.12452475	0.147841049	0.758709609	1.079924775	0.276444488
2	6	1.34048	  1.599780804	0.262461269	0.546150712	1.312740749	0.369478637
2	7	2.04740	  1.575608388	0.262096474	0.564481097	1.156476191	0.3486243
2	8	2.34589	  1.544272968	0.240910847	0.590728825	1.076969981	0.325612011
2	9	2.24994	  1.721707641	0.215246493	0.552290866	0.841010871	0.293499528
2	10	2.28261	  1.723163256	0.208630134	0.533981319	0.786512171	0.293033271
2	11	1.79821	   1.630677468	0.186234679	0.547718673	0.728193067	0.273576931
2	12	2.82772	   2.17231306	0.319454809	0.441392998	0.94698478	0.427395498			
3	1	-0.30317  0.874395008	-0.034676249	0.79350188	0.609515013	-0.002631637
3	2	-1.65989  0.825239215	-0.14194334	0.952212806	0.572879612	-0.019154984

``
`Best regards,

[osofr]

Hi, gridisl relies on machine learning algorithms implemented in xbgboost and h2o R packages. The only thing gridisl does is making it easier to use those two packages with a unified syntax. In short, the answer is yes, you can use gridisl to do prediction for your longitudinal data. I recommend looking into the aforementioned two packages for learning about specific prediction algorithms to try on your data. Also please see examples in tests folder of gridisl for examples of various algorithms that can be used. Best, Oleg ~~ sent from mobile device ~~ On Mar 6, 2017 02:10, "shahlaebrahimi" <notifications@github.com> wrote: Hi I would greatly appreciate if you could let me know whether your code is suitable for my data set which is as follows. In fact, it is an unbalanced longitudinal data set with time varying features. I want to predict RE to CS using x1 to x5 features. ID year RE to CS x1 x2 x3 x4 x5 1 1 0.06039 1.28102412 0.022933584 0.87453816 1.216366609 0.06094049 1 2 0.01064 1.270012471 0.00645422 0.820672937 1.004861122 -0.014079609 1 3 -0.45597 1.052890304 -0.059378881 0.922421512 0.729264145 0.020475912 1 4 -0.32539 1.113115232 -0.01522879 0.858878436 0.809737564 0.07603735 1 5 -0.56657 1.219644234 -0.058675441 0.887087711 0.484342194 0.009777888 2 1 1.25097 1.06226374 0.107020836 0.814602294 0.835928139 0.19996023 2 2 1.35725 1.055785531 0.081916221 0.879486383 0.686727862 0.142627013 2 3 0.00719 0.970588058 0.076063501 0.906774596 0.809795658 0.165915285 2 4 1.20019 1.058995743 0.130202682 0.818111675 0.875989179 0.23445163 2 5 2.23481 1.12452475 0.147841049 0.758709609 1.079924775 0.276444488 2 6 1.34048 1.599780804 0.262461269 0.546150712 1.312740749 0.369478637 2 7 2.04740 1.575608388 0.262096474 0.564481097 1.156476191 0.3486243 2 8 2.34589 1.544272968 0.240910847 0.590728825 1.076969981 0.325612011 2 9 2.24994 1.721707641 0.215246493 0.552290866 0.841010871 0.293499528 2 10 2.28261 1.723163256 0.208630134 0.533981319 0.786512171 0.293033271 2 11 1.79821 1.630677468 0.186234679 0.547718673 0.728193067 0.273576931 2 12 2.82772 2.17231306 0.319454809 0.441392998 0.94698478 0.427395498 3 1 -0.30317 0.874395008 -0.034676249 0.79350188 0.609515013 -0.002631637 3 2 -1.65989 0.825239215 -0.14194334 0.952212806 0.572879612 -0.019154984 `` `Best regards, — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#1>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AGaPTo8Wj-2--hpCAA1Vwkey66GBL2A6ks5ri9ufgaJpZM4MT8xv> .

[shahlaebrahimi]

@osofr Hi
I thank you very much for your time and consideration. In fact, since you ignored the time varying features like "wtkg", "bmi", and "haz", I wans not sure if it could deal with time varying features among different subjects or not.
Best regards,

[osofr]

These time varying features are heavily correlated with the outcomes and that's why they were excluded. Time variable is a time varying feature, for example, and it was used for modeling. Nothing prevents you from including any time varying feature you like in you model. ~~ sent from mobile device ~~

…

On Mar 7, 2017 08:40, "shahlaebrahimi" ***@***.***> wrote: @osofr <https://github.com/osofr> Hi I thank you very much for your time and consideration. In fact, since you ignored the time varying features like "wtkg", "bmi", and "haz", I wans not sure if it could deal with time varying features among different subjects or not. Best regards, — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#1 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AGaPTtjWmwGB19f06Itt3wHhL8H2aVWCks5rjYhogaJpZM4MT8xv> .