MCMCglmm ordinal model in R

问题

I'm trying to explain changes in tree vitality from 1 to 3 (1=green, 2=damage, 3=dry) using climatic variables in an MCMCglmm model. Unfortunately, I am struggling with two questions:

1. How do I interpret the summary of the MCMCglmm model?

I see which variables are significant, but what does it mean? Does this mean that with increasing precipitation the vitality gets to 1 (green)? I plotted the posterior means of the model and looks like precipitation and diameter have no influence on the vitality of the tree, but species react differently.

2. How do I check if the model is correct?

Data structure: Species, diameter (DBH), vitality 2018 (vit_2018) and precipitation (pp18). Were made plots, 1 plot contains 10 trees measured, precipitation is one value per plot.

###MCMCglmm model, family= ordinal

    prior1<-list(R=list(V=diag(1),nu=0.002))
    m1 <-MCMCglmm(VIT_2018~ pp18 +DBH+Species, 
                                 family = "ordinal", data = comsp,prior=prior1,pr = TRUE, 
                                 nitt = 60000, burnin =30000, thin = 50)
    summary(m1)

    #Output:
    Iterations = 30001:59951
     Thinning interval  = 50
     Sample size  = 600 

     DIC: -146008.7 

     R-structure:  ~units

          post.mean l-95% CI u-95% CI eff.samp
    units      2697     1016     5304    6.075

     Location effects: VIT_2018 ~ pp18 + DBH + Species 

                            post.mean  l-95% CI  u-95% CI eff.samp   pMCMC   
    (Intercept)              60.64229  31.71644  92.78733    11.49 < 0.002 **
    pp18                     -0.07020  -0.11518  -0.03556    15.47 < 0.002 **
    DBH                       0.17357  -0.01797   0.37281    58.26 0.06000 . 
    SpeciesBetula pendula   -12.88510 -29.42191   1.87807    58.40 0.08667 . 
    SpeciesCarpinus betulus  15.57570   0.96439  30.89528    45.04 0.02000 * 
    SpeciesCorylus avellana  -3.81337 -18.31965  13.12771   600.00 0.59000   
    SpeciesCrataegus spec.  -14.90077 -36.86880   4.24729   286.00 0.10333   
    SpeciesFagus sylvatica  -15.03559 -29.04547  -2.07809    60.56 0.00667 **
    SpeciesFrangula alnus    20.10817  -0.10598  38.66354    73.88 0.01333 * 
    SpeciesQuercus spec.     -9.09458 -24.52595   6.42502   293.33 0.26000   
    SpeciesSambucus nigra    24.29894   2.58339  46.29901    49.66 0.02333 * 
    SpeciesSorbus aucuparia  39.56930  22.97282  63.15175    10.13 < 0.002 **
    ---
    Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Cutpoints: 
                             post.mean l-95% CI u-95% CI eff.samp
    cutpoint.traitVIT_2018.1     80.46    55.38    117.9    4.204
#### Estimating Credible Intervals

    HPDinterval(mcmc(randomprior1$Sol[,"(Intercept)"]))
    #        lower    upper
    #var1 31.71644 92.78733
    #attr(,"Probability")
    #[1] 0.95

Data and R script can be found here

https://drive.google.com/drive/folders/1LmgEAssR5FfFw1CkYjygsaawg84dDAwk?usp=sharing Here are attached a part of the data, when I run the model with all the data, the density plots look better.

P.S. I also tried the clm function from the ordinal package in R, but the McFadden pseudo r ^ 2 was 0.06 = not a good fit. So I checked the precipitation data and the location of the plots with Moran's I test and there was spatial autocorrelation. I am using MCMCglmm because I read that it can deal with spatial autocorrelation. So far I followed several tutorials or questions that I found online

Any suggestions would be really helpful.

Thank you for your time and help!

来源：https://stackoverflow.com/questions/60029274/mcmcglmm-ordinal-model-in-r