I have a model with two variables, crop and pop, both have scaled values.
I am using GLMMtmb to run a model on it, but I'm unsure how to manually plot predictions from this model, backtransforming the scaled variable values.
A chunk of data is at the end of the post for a reproducible example.
so for my data frame columns: Animal is the presence or absence of the animal, crop and pop the variables that may affect presence or absence.
So I run the model
model <- glmmTMB(animal~crop+pop,family="poisson",data=dummy)
I received some code from someone to manually plot predictions but it's not working. This is the code, for for example, the 'pop' variable.
range <- range(dummy$pop)
nd <- expand.grid(pop = seq(range[1], range[2], by=0.5))
dummy <- as.data.frame(predict(model, newdata=nd, se.fit=TRUE))
dummy$lower <- dummy$fit - 1.96*dummy$se.fit
dummy$upper <- dummy$fit + 1.96*dummy$se.fit
dummy$fit_trans <- plogis(dummy$fit)
dummy$low_trans <- plogis(dummy$lower)
dummy$up_trans <- plogis(dummy$upper)
dummy <- cbind(dummy, nd)
this should then give me a dataframe I can feed to ggplot, but it goes wrong where I create the object 'nd' (newdata). Something needs to be added there but I'm unsure what. Additionally, I don't see in this code where my variable data is unscaled before creating the new data frame?
structure(list(animal = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), crop = structure(c(-0.911367192804466, -0.656468577561402,
0.937306298620515, -0.748068186743915, -0.950289715380232, -0.950289715380232,
-0.311140903098454, -0.950289715380232, -0.420299555068266, -0.204030814471523,
0.50447619733435, -0.950289715380232, -0.777918539996587, -0.950289715380232,
-0.950289715380232, -0.840253112662893, -0.839667809844811, -0.949704414301948,
-0.950289715380232, -0.332797072571516, -0.950289715380232, 0.28586616195498,
-0.950289715380232, -0.949704414301948, -0.950289715380232, -0.217785406339271,
-0.950289715380232, -0.949704414301948, -0.950289715380232, -0.950289715380232,
-0.950289715380232, -0.917220203587303, -0.950289715380232, -0.950289715380232,
-0.950289715380232, -0.950289715380232, -0.683099761835028, -0.950289715380232,
-0.950289715380232, -0.950289715380232, -0.950289715380232, -0.950289715380232,
-0.950289715380232, -0.420006934975588, -0.950289715380232, -0.86512840935985,
-0.950289715380232, -0.880053585986186, -0.950289715380232, -0.947070559449672,
0.886092437742609, 1.83281709105803, 1.11640850164685, -0.950289715380232,
-0.946485258371388, 1.37335566631441, -0.126771093015647, -0.949704414301948,
-0.896734664107575, -0.123551937954986, -0.826498527754337, -0.950289715380232,
-0.307921748037792, 1.68180931195255, 0.606903807743094, -0.190568898369987,
-0.669052549874602, 1.74970429966619, -0.941217548666834, 1.81701382450034,
0.217678665495733, -0.235637069219248, 0.166464804617827, -0.949411763762807,
-0.945022005675679, 0.0827666616935611, 0.362833263318178, 1.5957700969398,
0.778982349115678, 1.85652176820044, 1.54836074265497, -0.950289715380232,
0.153588184375181, 1.1480148120681, -0.950289715380232, -0.860153345844944,
-0.551699672370031, -0.916927552178262, -0.791673117945951, -0.949704414301948,
-0.580672067915984, 1.70053900213117, 0.212703567184868, 0.465553605166667,
-0.490535698380696, 0.512963070798555, -0.40449645553117, -0.949704414301948,
0.437459181245815, 1.78921202067216, -0.939168994892841), dim = c(101L,
1L)), pop = structure(c(-0.197547924591922, -0.194351971156629,
-0.188274258566538, -0.196448483438051, -0.197547924591922, -0.197534996338939,
-0.197544742856045, -0.19666972080707, -0.184138726259079, -0.187096628792688,
-0.181474291574343, -0.197126836606957, -0.191909922110918, -0.197541385064299,
-0.197547924591922, -0.180014490953617, -0.196345541198624, -0.194080008065368,
-0.197547924591922, -0.169718619816262, -0.1973883809182, -0.19069419228899,
-0.19750465288443, -0.197547924591922, -0.197547924591922, -0.196222722433591,
-0.197547924591922, -0.197007381120755, -0.197547924591922, -0.197547924591922,
-0.197547924591922, -0.196084866482173, -0.196199779840005, -0.197547924591922,
-0.197547924591922, -0.196775288017525, -0.195128528488411, -0.197547924591922,
-0.197547924591922, -0.196661275901739, -0.197547924591922, -0.197547924591922,
-0.197547924591922, -0.195309309220705, -0.197547924591922, -0.196083127378007,
-0.197547924591922, -0.197047712962236, -0.197547924591922, -0.19661973821873,
0.624878194166003, -0.194073305851747, -0.165976797458507, -0.197547924591922,
-0.189654680869701, -0.138488532034927, -0.182502054986232, -0.151862484978888,
-0.146757177620867, -0.180411925114601, -0.196752195942981, -0.197416680215279,
-0.183794718471439, -0.173372247384172, -0.196556998242149, -0.174186298469269,
-0.178206794332005, -0.197547924591922, -0.196558496041342, -0.182985603845477,
0.0359655292697121, 0.14216237743388, -0.170265068895674, -0.197547924591922,
-0.19328285943275, -0.148427554030811, -0.115746893472626, -0.189714944074946,
-0.185416997205207, -0.1959619567473, -0.0975791931695816, -0.197326675563388,
-0.17579755900438, -0.0809452064393561, -0.197246026550915, 0.888153565621317,
-0.189168753981969, -0.193326520514112, -0.191939980937669, -0.197547923770278,
-0.193648598847386, -0.140792688570448, -0.162820943186907, -0.0971587686665226,
-0.184731765563276, -0.166566876527242, -0.0244512966150544,
-0.19624995376397, -0.147791915216302, -0.167901037976233, 16.3965287744725
), dim = c(101L, 1L))), class = "data.frame", row.names = c(2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L,
43L, 45L, 46L, 47L, 49L, 50L, 51L, 52L, 53L, 3786L, 3787L, 3788L,
3789L, 3790L, 3791L, 3792L, 3793L, 3794L, 3795L, 3796L, 3797L,
3798L, 3799L, 3800L, 3801L, 3802L, 3803L, 3804L, 3805L, 3806L,
3807L, 3808L, 3809L, 3810L, 3811L, 3812L, 3813L, 3814L, 3815L,
3816L, 3817L, 3818L, 3819L, 3820L, 3821L, 3822L, 3823L, 3824L,
3825L, 3826L, 3827L, 3828L, 3829L, 3830L, 3831L, 3832L, 3833L,
3834L, 3835L, 3836L))
I found out the best way is to not rescale variables before running a model, but supplying scale(variables) to the model formula. This way, when you create a new data frame for predictions, the values are automatically unscaled.