Hi there,
I am trying to use a predicting function in brms.
I have trait data for a select amount of geographical points (DF1), and my new dataset (DF2) is those same trait data but with a vaste amount more geographical points as it forms up species assemblages.
My first Data set DF1:
head(DF1)
Scientific_Name latitude longitude LAI solar TotAreaSpots
B2
Acanthagenys rufogularis -37.83113 145.8160 2.888929 14.21700 3.31158
Ailuroedus crassirostris -27.9167 152.3333 2.544430 18.42407 0.19611
Menura novaehollandiae -29.6500 152.0833 2.674176 17.78050 2.96349
B2 Nest.type
54.67732 Cup
73.88418 Cup
27.40258 Dome
My second data set DF2:
head(DF2)
Scientific_Name latitude longitude LAI solar TotAreaSpots
Acanthagenys rufogularis -37.83113 145.8160 2.888929 14.21700 3.31158
Acanthagenys rufogularis -37.54446 149.1793 3.038562 14.80028 3.31158
Acanthagenys rufogularis -37.64789 148.0603 2.953508 14.96962 3.31158
B2 Nest.type
54.67732 Cup
54.67732 Cup
54.67732 Cup
> str()
$ Scientific_Name: Factor w/ 261 levels "Acanthagenys rufogularis",..
$ latitude : num
$ longitude : num
$ LAI : num
$ solar : num
$ TotAreaSpots : num
$ B2 : num
$ Nest.type : Factor w/ 2 levels "Cup","Dome"
My model:
Fit1 <- brm(mvbind(B2,TotAreaSpots) ~ LAI * solar + Nest.type ,
data= DF1, family = gaussian(), cov_ranef = list(phylo=AB)
,warmup = 1000, iter = 2000, chains = 4,
control = list(adapt_delta = 0.99))
And what I would like to do, is to run a predictive model, using DF2 (thus many more geographical points), to see the effects of LAI * solar and nest type on B2 and Spots.
Any suggestions would be highly appreciated!