UBMS question- how to know if random effect is significant

[Emma Buckley]

Hi,

I'm a student using Royal-Nicole modelling and N-mixture modelling to estimate detection probability and abundance from a count data I've collected over 11 days. I am wanting to add a random effect, using ubms, to see if this influences my detections. I created an unmarked dataframe with the detections, site and observational variables. I created a model with and without the random effect and manged to get the output summary, but how do you know if the random effect is significant or not?

Another question, I am trying to generate a model selection table of models, similar to the dredge function. Is there a way you can create something similar using ubms incorporating the random effect?

Regards

Emma

Code:

######  Made unmarked data frame to be used by model

###############################################################

#make unmarked data frame

umf_stack <- unmarkedFrameOccu(y = y, 

                         obsCovs = list(mean_temp_air = mean_temp_air,

                                        mean_temp_surface = mean_temp_surface,

                                        mean_temp_subsurface = mean_temp_subsurface,

                                        mean_humidity = mean_humidity

                         ),

                         siteCovs = data.frame(colony_pres_abs = colony_pres_abs, 

                                               canopy_height = canopy_height, total_tree_height = total_tree_height, pair = pair)

)

Models:

#With pair 

fit_stack <- stan_occu(~ scale(mean_temp_surface)+

                       scale(mean_temp_subsurface)+

                       scale(mean_humidity)

                     ~ scale(colony_pres_abs)+

                       scale(canopy_height)+

                       scale(total_tree_height) +

                       (1|pair),

                     data = umf_stack , chains=3, iter=2000

)

 

fit_stack

 

#Without pair

fit_no_pair <- stan_occu(~ scale(mean_temp_surface)+

                         scale(mean_temp_subsurface)+

                         scale(mean_humidity)

                       ~ scale(colony_pres_abs)+

                         scale(canopy_height)+

                         scale(total_tree_height),

                       data = umf_stack , chains=3, iter=2000

)

 

fit_no_pair

#Fitted two models together and compared them:

fl <- fitList(fit_no_pair, fit_stack)

round(modSel(fl),3)

 

[Ken Kellner]

Hi Emma,

It doesn't really make sense to think of a random effect as being "significant". What you can say from your output, based on the model table, is that you have evidence that the model with the random effect performed better. That's because the elpd for fit_stack was greater than for fit_no_pair, and the SE of the difference in elpd between the two models was small relative to the value of the difference.

As far as I know, MuMIn::dredge does not support models from ubms. You could ask the MuMIn community about that, however my opinion is that it is better to compare a curated set of candidate models based specifically on your objectives and hypotheses rather than fitting models for all possible combinations of covariates as dredge does.

Ken

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> [image: Model output with random effect.JPG][image: Model output without
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[Jim Baldwin]

A reason to include "pairs" no matter what the size of the effect is if "pairs" is associated with repeated measurements on the same experimental unit.  (Now if there are only 6 or fewer observed levels of "pairs", then the common approach in mixed models is simply to treat that as a fixed effect as one would not get a very good estimate of the variance anyway.)  I recognize that this is a Bayesian model but if the experiment/survey is constructed with different experimental units, then each of those types of experimental units should probably have a random effect.

Jim

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[Emma Buckley]

Hi all, thanks for the helpful answers

In the study there are 24 trees that have been identified, 12 experimental trees and 12 control trees. Each tree is paired, one control and one experimental tree. I am trying to take into account that the trees are paired by creating them as a random effect called pairs. I am wanting to compare the pairs of trees with the detections to see if there is a difference between the detections based on that the trees are paired. Is there a way to do this using ubms and random effects? Or should I rather use GLM for this?

Regards

Emma

[Marc Kery]

Yes, you declare the pair a random-effects factor in ubms. Pair is a factor with levels 1-12.

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