Open DeirdreLoughnan opened 11 months ago
@DeirdreLoughnan Sounds super promising!
@lizzieinvancouver here is my progress on including site as a continuous variable. For the trait only part of the model, the test data I am using is here and the stan model is here.
This model does a pretty good job of returning the main parameter estimates:
Parameter Test.data.values Estiamte X2.5 X97.5
1 mu.tran 2 2.185294 -0.62549202 9.847331
2 mu.grand 10 10.962462 9.11307772 13.382674
3 sigma.tran 1 1.513281 0.05128542 8.202570
4 sigma.species 5 5.986751 5.98675109 7.490044
5 trt.var 1 1.004396 1.00439634 1.022306
But you see a similar issue as we found with the traitor modeling, where the estimates of muSp are slightly off from simulated values:
The full model does a fairly good job of returning the model estimates, but with a few issues:
Parameter Test.data.values Estiamte X2.5 X97.5
1 mutran 2.0 2.5638443 0.51764220 4.07779746
2 mu_forcesp -10.0 -10.1794081 -10.96022586 -9.39852179
3 mu_chillsp -14.0 -13.4769342 -14.33031321 -12.57029303
4 mu_photosp -15.0 -15.1733402 -16.19189355 -14.20576673
5 sigma_tran 1.0 0.8203776 0.08279876 3.17578980
6 sigma_forcesp 1.0 0.9499618 0.75386986 1.20518018
7 sigma_chillsp 1.0 1.1246461 0.89149014 1.43157227
8 sigma_photosp 1.0 0.9938081 0.68721399 1.38303431
9 sigma_phenoy 3.0 3.0010979 2.96439322 3.03847445
10 betaF 0.3 0.3469834 0.27130091 0.41905600
11 betaC -0.4 -0.4331277 -0.51908645 -0.35185634
12 betaP -0.2 -0.1541842 -0.24529521 -0.05841521
I will continue to work on it and see if I can improve the estimates for mutran, muchillsp in particular.
@lizzieinvancouver Sadly, running the model with 60 spp instead of 40 did not improve the model estimates, some got a little worse, especially sigmaphotosp:
Parameter Test.data.values Estiamte X2.5 X25 X50 X75 X97.5
1 mutran 2.0 2.8045284 0.1082778 2.2635966 2.7997938 3.3583215 5.3464928
2 mu_forcesp -10.0 -10.0333392 -10.6280109 -10.2388581 -10.0354260 -9.8293535 -9.4330053
3 mu_chillsp -14.0 -14.1888138 -14.8266683 -14.4087388 -14.1896654 -13.9771447 -13.5173965
4 mu_photosp -15.0 -15.0333082 -15.5070913 -15.1951475 -15.0330016 -14.8717375 -14.5617723
5 sigma_tran 1.0 1.5042649 0.4327008 0.8226801 1.2625048 1.9629560 3.8137010
6 sigma_forcesp 1.0 0.9489035 0.7527134 0.8673541 0.9377218 1.0198850 1.2041155
7 sigma_chillsp 1.0 1.0280614 0.8159177 0.9360183 1.0192629 1.1073484 1.2927864
8 sigma_photosp 1.0 0.6972528 0.5504254 0.6370115 0.6926494 0.7493224 0.8729458
9 sigma_phenoy 3.0 2.9976858 2.9613572 2.9854449 2.9973486 3.0099108 3.0347593
10 betaF 0.3 0.3254536 0.2730196 0.3069642 0.3265998 0.3444778 0.3754525
11 betaC -0.4 -0.3909004 -0.4500457 -0.4092934 -0.3906983 -0.3721440 -0.3347560
12 betaP -0.2 -0.2031380 -0.2454293 -0.2176397 -0.2033093 -0.1889107 -0.1615634
But on a positive note, removing the grand mean does improve the species estimates!
@DeirdreLoughnan I would check what might be up with the sigma_photosp and sigma_tran -- since it's not getting those.I am actually not sure what sigma_tran is, given that there are only two transects and thus would not be good for partial pooling ... (and I don't see where latitude is, but it would not need a sigma either since it's continuous).
@lizzieinvancouver if you have time this afternoon, could we chat about this in more detail?
@lizzieinvancouver I continue to have issues with how to incorporate site into this model. After our last conversation, we talked about the model including transect as a dummy variable and an interaction between the transect dummy variable and latitude:
y_hat[i] = muSp[species[i]] + b_tranE * tranE[i] + b_tranlat * (tranE * latitude)
But I seem stuck at getting the test data for the interaction to return the true values.
I took several steps back and was able to return the test data values fairly well for model with muSp and the transect dummy variable:
Parameter Test.data.values Estiamte X2.5 X25 X50 X75 X97.5
1 bTranE 4 4.049068 4.0022755 4.032535 4.048875 4.065402 4.097475
2 sigma_sp 5 5.883881 0.3411229 2.837319 5.342898 8.338506 14.544073
3 sigma_traity 1 1.007117 0.9904040 1.001276 1.007029 1.013001 1.024129
The estimate for sigma_sp is a little high, but the estimates for individual species look good:
But when I try adding latitude, either as a simple slope or as an interactions, the estimates are really bad:
Parameter Test.data.values Estiamte X2.5 X25 X50 X75 X97.5
1 bTranE 4 -31.352190 -41.0354092 -34.667673 -31.318554 -28.003578 -21.34691
2 bTranLat 2 108.616257 108.3107336 108.511392 108.617082 108.720196 108.91374
3 sigma_sp 5 5.826951 0.3217277 2.884909 5.327186 8.299395 13.88553
4 sigma_traity 1 287.484793 283.6445453 286.149891 287.472565 288.839452 291.43939
I think part of the issue is the test data, but I don't have a rational reason for changing it and I think I could really benefit from talking about it more and a second set of eyes.
@lizzieinvancouver I figured out the issue was with the way I was coding the interaction parameter and then adding it to calculate yTraiti.
The model output for the subsets of the data looked much better, so I went ahead and ran the model for the full model. The output looks pretty good, but not perfect:
Parameter Test.data.values Estiamte X2.5 X25 X50 X75 X97.5
1 bTranE 4.0 3.4784021 2.4083588 3.1270973 3.4829672 3.8416681 4.5031540
2 bTranLat 2.0 2.0106743 1.9900568 2.0034633 2.0105538 2.0178052 2.0320489
3 mu_forcesp -10.0 -9.4105091 -10.4492824 -9.7543079 -9.4324426 -9.0706732 -8.3310481
4 mu_chillsp -14.0 -14.3780959 -15.4860732 -14.7371280 -14.3678980 -14.0156398 -13.3100039
5 mu_photosp -15.0 -15.4811907 -16.3143809 -15.7422628 -15.4818317 -15.2066908 -14.6500921
6 sigma_sp 5.0 5.0071388 3.7836999 4.4637372 4.9248207 5.4657316 6.6598418
7 sigma_traity 1.0 1.0059972 0.9844312 0.9983096 1.0057236 1.0137590 1.0281675
8 sigma_forcesp 1.0 1.1932571 0.8867785 1.0577457 1.1702944 1.2988199 1.6320695
9 sigma_chillsp 1.0 1.1726527 0.8765847 1.0428704 1.1516221 1.2725267 1.5931743
10 sigma_photosp 1.0 0.9030784 0.6800047 0.8063770 0.8859213 0.9861722 1.2188055
11 sigma_phenoy 3.0 2.9732855 2.9258574 2.9570208 2.9733017 2.9896074 3.0191808
12 betaF 0.3 0.2706549 0.1699054 0.2398130 0.2712550 0.3010979 0.3683886
13 betaC -0.4 -0.3536558 -0.4505924 -0.3890603 -0.3544528 -0.3202395 -0.2520712
14 betaP -0.2 -0.1511724 -0.2292865 -0.1757392 -0.1509959 -0.1263453 -0.0758689
But I am definitely happy with the progress for today. Thanks for you help!
I ran the model with twice as many spp and reps (40 vs 20 above) and the output does look better, except for bTranE and sigma_sp:
Parameter Test.data.values Estiamte X2.5 X25 X50 X75 X97.5
1 bTranE 4.0 3.6153540 3.0777352 3.4202407 3.6155720 3.8080656 4.1759040
2 bTranLat 2.0 2.0069510 1.9965469 2.0033246 2.0069558 2.0105099 2.0169421
3 mu_forcesp -10.0 -9.9170983 -10.8544477 -10.2529963 -9.9139155 -9.5949105 -8.9577225
4 mu_chillsp -14.0 -13.9974622 -14.9387317 -14.3091020 -13.9919772 -13.6831298 -13.0615258
5 mu_photosp -15.0 -15.3347722 -16.2662611 -15.6430644 -15.3370279 -15.0083532 -14.4325579
6 sigma_sp 5.0 4.4006001 3.5620069 4.0456382 4.3571033 4.7024305 5.4796514
7 sigma_traity 1.0 1.0052632 0.9932787 1.0011191 1.0052708 1.0094949 1.0171449
8 sigma_forcesp 1.0 1.1222524 0.9009913 1.0288660 1.1090117 1.2011217 1.4146107
9 sigma_chillsp 1.0 1.1562696 0.9307797 1.0576007 1.1433923 1.2394430 1.4611381
10 sigma_photosp 1.0 1.1187904 0.8856199 1.0167166 1.1084341 1.2036920 1.4181994
11 sigma_phenoy 3.0 2.9930129 2.9667543 2.9839269 2.9928577 3.0018097 3.0196973
12 betaF 0.3 0.3147594 0.2258027 0.2851414 0.3153203 0.3449079 0.3984852
13 betaC -0.4 -0.3960073 -0.4812834 -0.4234247 -0.3966884 -0.3681156 -0.3099671
14 betaP -0.2 -0.1962686 -0.2823979 -0.2251928 -0.1964218 -0.1683275 -0.1099325
But the species level estimates are awesome!
@DeirdreLoughnan Nice work! These results make me think the model is good for our purposes. If you want, you could run the full code again (including the R code so you get slightly different species) and check it's not systematically off, but I would be happy with the results myself and move on.
@lizzieinvancouver I have been thinking about how to include site in the joint model. In our last meeting we discussed including latitude in the model as a continuous variable instead of having sites as dummy variables.
I rewrote the model to include latitude as a continuous variable, and now the model generates a slope estimate (b) for each species' traits for every degree latitude, the model code is:
Most species have very small changes in traits with latitude and the UI cross zero: