[R-sig-ME] mcmcglmm Priors: Auto correlation and extreme post mean values
sree datta
@reedt@8 @end|ng |rom gm@||@com
Fri May 15 18:32:49 CEST 2020
I forgot to copy the group on my original reply: here is the text of my
notes:
Thanks for sharing the relevant data and model details and output. The
first and biggest concern I have is the use of Site variable for a Random
Intercept. There are only 175 observations, but you have 86 different
sites. On an average, without seeing the actual data, that means you have
~2 observations per site. There is not enough data to use Site for a Random
intercept as it exists.
You should use site only after you can reduce 86 sites into some meaningful
groupings based on criteria you use in Ecology (reduce to 3 or 4 groups).
Additionally, the remaining factors create 2 * 5 * 4 = 40 cells and you
have only 175 observations ~ 4.4 observations per cell - this number is
also too low for you to use all of these factors in your analysis.
All of the problems you are facing mostly likely are arising from the wrong
application of the techniques to your data. I would take the following
steps:
1. Run a bivariate means comparison using Anova after you declare your
Landuse, Human_presence etc as factors using ( aov( trappings ~ landuse)
2. In doing so you are going to change your assumptions of trappings from
count to continuous, but I think for exploration, it is okay.
3. Make sure you examine the means of the trappings for each level and
combine levels within a factor, when the sub-groups are not different from
each other.
4. This will reduce the number of groups you are working with since your
records are limited.
Only after undertaking the steps above should you consider, running any
models in lme4 or nlme. Ideally, if you could reduce those factors that
have 4 and 5 groups to having 2 each (if the differences are not
significant - see # 3) then you could do meaningful analyses. Similarly,
with Site, the 86 levels should be reduced to either 3 or 4 for you to use
them even for a descriptive analysis. Without adding in more data or
reducing your number of levels, you should not undertake any further
modeling.
I also wanted to add that for Site variable, you should first find a
theoretical way to reduce the number of sites to 3 or 4 broad groups. You
cannot use Means comparison to do that.
On Fri, May 15, 2020 at 12:08 PM sree datta <sreedta8 using gmail.com> wrote:
>
> Thanks for sharing the relevant data and model details and output. The
> first and biggest concern I have is the use of Site variable for a Random
> Intercept. There are only 175 observations, but you have 86 different
> sites. On an average, without seeing the actual data, that means you have
> ~2 observations per site. There is not enough data to use Site for a Random
> intercept as it exists.
>
> You should use site only after you can reduce 86 sites into some
> meaningful groupings based on criteria you use in Ecology (reduce to 3 or 4
> groups). Additionally, the remaining factors create 2 * 5 * 4 = 40 cells
> and you have only 175 observations ~ 4.4 observations per cell - this
> number is also too low for you to use all of these factors in your
> analysis.
>
> All of the problems you are facing mostly likely are arising from the
> wrong application of the techniques to your data. I would take the
> following steps:
> 1. Run a bivariate means comparison using Anova after you declare your
> Landuse, Human_presence etc as factors using ( aov( trappings ~ landuse)
> 2. In doing so you are going to change your assumptions of trappings from
> count to continuous, but I think for exploration, it is okay.
> 3. Make sure you examine the means of the trappings for each level and
> combine levels within a factor, when the sub-groups are not different from
> each other.
> 4. This will reduce the number of groups you are working with since your
> records are limited.
>
> Only after undertaking the steps above should you consider, running any
> models in lme4 or nlme. Ideally, if you could reduce those factors that
> have 4 and 5 groups to having 2 each (if the differences are not
> significant - see # 3) then you could do meaningful analyses. Similarly,
> with Site, the 86 levels should be reduced to either 3 or 4 for you to use
> them even for a descriptive analysis. Without adding in more data or
> reducing your number of levels, you should not undertake any further
> modeling.
>
> Sree
>
> On Fri, May 15, 2020 at 11:33 AM Alexander Botha <alexbotha555 using gmail.com>
> wrote:
>
>> Hi Sree.
>> I have run multiple models with lmer and glmer using a combination of
>> predictor variables as either random or fixed effects. I was worried about
>> the collinearity in fixed effects (singular fit) when using lme4.
>> I hope these answer your questions. Please see below summaries of some of
>> the models I ran.
>> I have also attached the structure of my data in case it might help.
>> Thank you for your help.
>> Please let me know if you require anything else or if you have any
>> suggestions for data exploration.
>>
>> *Data structure*
>> data.frame': 175 obs. of 8 variables:
>> $ Site : Factor w/ 86 levels "AA1","carcass",..: 66 66 66 68 68
>> 68 21 21 21 32 ...
>> $ Landuse : Factor w/ 2 levels "farmland","reserve": 1 1 1 1 1 1 1
>> 1 1 1 ...
>> $ MP : Factor w/ 4 levels "FM","FQ","NM",..: 3 1 4 3 2 4 3 2
>> 1 3 ...
>> $ Human_presence: Factor w/ 5 levels "0","1","2","3",..: 4 4 4 4 4 4 5 5
>> 5 5 ...
>> $ Jackal_trapped: int 1 0 0 0 1 0 0 0 0 0 ...
>>
>> *model1<-lmer(Jackal_trapped~ Human_presence + (1|Site), data=data)*
>> REML criterion at convergence: 77.7
>>
>> Scaled residuals:
>> Min 1Q Median 3Q Max
>> -1.3261 -0.7568 0.0000 0.0000 2.6909
>>
>> Random effects:
>> Groups Name Variance Std.Dev.
>> Site (Intercept) 0.00000 0.00
>> Residual 0.08413 0.29
>> Number of obs: 175, groups: Site, 86
>>
>> Fixed effects:
>> Estimate Std. Error t value
>> (Intercept) 0.21951 0.04530 4.846
>> Human_presence1 0.16510 0.09232 1.788
>> Human_presence2 -0.21951 0.10230 -2.146
>> Human_presence3 0.08049 0.07911 1.017
>> Human_presence4 -0.21951 0.05456 -4.024
>>
>> Correlation of Fixed Effects:
>> (Intr) Hmn_p1 Hmn_p2 Hmn_p3
>> Humn_prsnc1 -0.491
>> Humn_prsnc2 -0.443 0.217
>> Humn_prsnc3 -0.573 0.281 0.254
>> Humn_prsnc4 -0.830 0.407 0.368 0.475
>> convergence code: 0
>>
>> *model1.1<-lmer(Jackal_trapped~ Human_presence + MP + Landuse + (1|Site),
>> data=data)*
>> REML criterion at convergence: 88.5
>>
>> Scaled residuals:
>> Min 1Q Median 3Q Max
>> -1.36759 -0.73677 -0.00991 0.03404 2.68699
>>
>> Random effects:
>> Groups Name Variance Std.Dev.
>> Site (Intercept) 0.00000 0.0000
>> Residual 0.08531 0.2921
>> Number of obs: 175, groups: Site, 86
>>
>> Fixed effects:
>> Estimate Std. Error t value
>> (Intercept) 0.344378 0.181977 1.892
>> Human_presence1 0.161919 0.094159 1.720
>> Human_presence2 -0.228924 0.103472 -2.212
>> Human_presence3 0.014851 0.154207 0.096
>> Human_presence4 -0.341485 0.179179 -1.906
>> MPFQ -0.012836 0.060249 -0.213
>> MPNM 0.009494 0.060567 0.157
>> MPTQ -0.122941 0.114023 -1.078
>> Landusereserve -0.116350 0.169306 -0.687
>>
>> Correlation of Fixed Effects:
>> (Intr) Hmn_p1 Hmn_p2 Hmn_p3 Hmn_p4 MPFQ MPNM MPTQ
>> Humn_prsnc1 -0.159
>> Humn_prsnc2 -0.144 0.221
>> Humn_prsnc3 -0.868 0.159 0.138
>> Humn_prsnc4 -0.963 0.131 0.134 0.868
>> MPFQ -0.248 0.158 0.052 0.094 0.052
>> MPNM -0.178 0.082 0.047 0.003 0.002 0.594
>> MPTQ -0.327 0.040 0.090 0.063 0.260 0.293 0.322
>> Landusersrv -0.929 0.004 0.017 0.838 0.947 0.021 -0.080 0.207
>> convergence code: 0
>> boundary (singular) fit: see ?isSingular
>>
>> *model2<-lmer(Jackal_trapped~ Human_presence + Landuse + (1|Site),
>> data=data)*
>> Linear mixed model fit by REML ['lmerMod']
>> Formula: Jackal_trapped ~ Human_presence + Landuse + (1 | Site)
>> Data: data
>>
>> REML criterion at convergence: 79.4
>>
>> Scaled residuals:
>> Min 1Q Median 3Q Max
>> -1.3227 -0.7549 0.0000 0.0000 2.6842
>>
>> Random effects:
>> Groups Name Variance Std.Dev.
>> Site (Intercept) 0.00000 0.0000
>> Residual 0.08455 0.2908
>> Number of obs: 175, groups: Site, 86
>>
>> Fixed effects:
>> Estimate Std. Error t value
>> (Intercept) 0.28201 0.16877 1.671
>> Human_presence1 0.16510 0.09255 1.784
>> Human_presence2 -0.21951 0.10255 -2.140
>> Human_presence3 0.03049 0.15231 0.200
>> Human_presence4 -0.28201 0.17150 -1.644
>> Landusereserve -0.06250 0.16255 -0.385
>>
>> Correlation of Fixed Effects:
>> (Intr) Hmn_p1 Hmn_p2 Hmn_p3 Hmn_p4
>> Humn_prsnc1 -0.132
>> Humn_prsnc2 -0.119 0.217
>> Humn_prsnc3 -0.902 0.146 0.132
>> Humn_prsnc4 -0.984 0.130 0.117 0.888
>> Landusersrv -0.963 0.000 0.000 0.854 0.948
>> convergence code: 0
>>
>> *model3<-lmer(Jackal_trapped~ Human_presence + MP + (1|Landuse) (1|Site),
>> data=data)*
>> Formula: Jackal_trapped ~ Human_presence + MP + (1 | Landuse) + (1 | Site)
>> Data: data
>>
>> REML criterion at convergence: 87.2
>>
>> Scaled residuals:
>> Min 1Q Median 3Q Max
>> -1.35994 -0.74172 -0.01178 0.02922 2.68746
>>
>> Random effects:
>> Groups Name Variance Std.Dev.
>> Site (Intercept) 0.00000 0.0000
>> Landuse (Intercept) 0.00000 0.0000
>> Residual 0.08504 0.2916
>> Number of obs: 175, groups: Site, 86; Landuse, 2
>>
>> Fixed effects:
>> Estimate Std. Error t value
>> (Intercept) 0.228251 0.067428 3.385
>> Human_presence1 0.162180 0.094010 1.725
>> Human_presence2 -0.227740 0.103294 -2.205
>> Human_presence3 0.103705 0.083907 1.236
>> Human_presence4 -0.224817 0.057214 -3.929
>> MPFQ -0.011955 0.060140 -0.199
>> MPNM 0.006149 0.060276 0.102
>> MPTQ -0.106692 0.111368 -0.958
>>
>> Correlation of Fixed Effects:
>> (Intr) Hmn_p1 Hmn_p2 Hmn_p3 Hmn_p4 MPFQ MPNM
>> Humn_prsnc1 -0.418
>> Humn_prsnc2 -0.347 0.221
>> Humn_prsnc3 -0.443 0.285 0.227
>> Humn_prsnc4 -0.705 0.398 0.371 0.422
>> MPFQ -0.614 0.158 0.052 0.139 0.100
>> MPNM -0.682 0.083 0.049 0.130 0.245 0.598
>> MPTQ -0.370 0.040 0.088 -0.208 0.202 0.295 0.347
>> convergence code: 0
>> boundary (singular) fit: see ?isSingular
>> *model4<-glmer(Jackal_trapped~Human_presence + MP + Landuse +(1|Site),
>> data = data, family = binomial)*
>>
>> Generalized linear mixed model fit by maximum likelihood (Laplace
>> Approximation) ['glmerMod']
>> Family: binomial ( logit )
>> Formula: Jackal_trapped ~ Human_presence + MP + Landuse + (1 | Site)
>> Data: data
>>
>> AIC BIC logLik deviance df.resid
>> 104.1 135.7 -42.0 84.1 165
>>
>> Scaled residuals:
>> Min 1Q Median 3Q Max
>> -0.8317 -0.4895 0.0000 0.0000 2.0429
>>
>> Random effects:
>> Groups Name Variance Std.Dev.
>> Site (Intercept) 0 0
>> Number of obs: 175, groups: Site, 86
>>
>> Fixed effects:
>> Estimate Std. Error z value Pr(>|z|)
>> (Intercept) -5.455e-01 1.511e+00 -0.361 0.718
>> Human_presence1 7.428e-01 7.188e-01 1.033 0.301
>> Human_presence2 -3.302e+02 2.122e+07 0.000 1.000
>> Human_presence3 3.143e-02 1.238e+00 0.025 0.980
>> Human_presence4 -4.416e+01 7.035e+06 0.000 1.000
>> MPFQ -2.413e-01 8.942e-01 -0.270 0.787
>> MPNM 7.619e-02 7.622e-01 0.100 0.920
>> MPTQ -7.063e-01 1.039e+00 -0.680 0.497
>> Landusereserve -6.420e-01 1.331e+00 -0.482 0.630
>>
>> Correlation of Fixed Effects:
>> (Intr) Hmn_p1 Hmn_p2 Hmn_p3 Hmn_p4 MPFQ MPNM MPTQ
>> Humn_prsnc1 -0.251
>> Humn_prsnc2 0.000 0.000
>> Humn_prsnc3 -0.861 0.210 0.000
>> Humn_prsnc4 0.000 0.000 0.000 0.000
>> MPFQ -0.413 0.296 0.000 0.166 0.000
>> MPNM -0.317 0.192 0.000 0.044 0.000 0.646
>> MPTQ -0.383 0.116 0.000 0.083 0.000 0.430 0.459
>> Landusersrv -0.860 0.007 0.000 0.838 0.000 0.035 -0.115 0.158
>> convergence code: 0
>>
>> *model5<-glmer(Jackal_trapped~Human_presence + MP + Landuse +(1|Site),
>> data = data, family = poisson)*
>> Generalized linear mixed model fit by maximum likelihood (Laplace
>> Approximation) ['glmerMod']
>> Family: poisson ( log )
>> Formula: Jackal_trapped ~ Human_presence + MP + Landuse + (1 | Site)
>> Data: data
>>
>> AIC BIC logLik deviance df.resid
>> 110.7 142.3 -45.3 90.7 165
>>
>> Scaled residuals:
>> Min 1Q Median 3Q Max
>> -0.6418 -0.4413 0.0000 0.0000 1.8248
>>
>> Random effects:
>> Groups Name Variance Std.Dev.
>> Site (Intercept) 0 0
>> Number of obs: 175, groups: Site, 86
>>
>> Fixed effects:
>> Estimate Std. Error z value Pr(>|z|)
>> (Intercept) -1.008e+00 1.278e+00 -0.789 0.430
>> Human_presence1 5.166e-01 5.851e-01 0.883 0.377
>> Human_presence2 -3.688e+01 2.122e+07 0.000 1.000
>> Human_presence3 3.101e-02 1.072e+00 0.029 0.977
>> Human_presence4 -3.153e+01 1.255e+06 0.000 1.000
>> MPFQ -1.762e-01 7.501e-01 -0.235 0.814
>> MPNM 5.648e-02 6.197e-01 0.091 0.927
>> MPTQ -5.145e-01 8.896e-01 -0.578 0.563
>> Landusereserve -4.524e-01 1.134e+00 -0.399 0.690
>>
>> Correlation of Fixed Effects:
>> (Intr) Hmn_p1 Hmn_p2 Hmn_p3 Hmn_p4 MPFQ MPNM MPTQ
>> Humn_prsnc1 -0.267
>> Humn_prsnc2 0.000 0.000
>> Humn_prsnc3 -0.875 0.226 0.000
>> Humn_prsnc4 0.000 0.000 0.000 0.000
>> MPFQ -0.392 0.302 0.000 0.160 0.000
>> MPNM -0.299 0.185 0.000 0.039 0.000 0.618
>> MPTQ -0.345 0.119 0.000 0.076 0.000 0.395 0.431
>> Landusersrv -0.865 0.010 0.000 0.848 0.000 0.033 -0.116 0.139
>> convergence code: 0
>>
>> With kind regards,
>>
>> Alexander Edward Botha
>> alexbotha555 using gmail.com 082 414 9030
>> PhD candidate
>> Mammal ecology
>>
>>
>> On Fri, May 15, 2020 at 4:25 PM sree datta <sreedta8 using gmail.com> wrote:
>>
>>> Hi Alexander,
>>>
>>> Prior to running the model with "MCMCglmm", have you attempted to run a
>>> log-linear model / a mixed-model with "nlme" or "lme4" / a decision tree? I
>>> ask these questions to better understand what the simpler univariate and
>>> multivariate approaches would reveal in terms of association between your
>>> dependent variable and your predictor variables.
>>>
>>> These are the exploratory models I would run to understand what would
>>> represent reasonable priors to use for Bayesian based models. If you could
>>> share more on some of your explorations with the data, that would be
>>> helpful.
>>>
>>> Sree
>>>
>>> On Fri, May 15, 2020 at 9:29 AM Alexander Botha <alexbotha555 using gmail.com>
>>> wrote:
>>>
>>>> Good day List,
>>>> My name is Alex, I am currently using the package mcmcglmm to determine
>>>> the
>>>> impact of lunar cycles, human presence and land use type (agricultural
>>>> vs
>>>> protected) on the trapping success of meso-predators for my PhD. I am
>>>> new
>>>> to MCMCglmm and I was wondering if you could assist with my problem.
>>>>
>>>> Structure of data: I am testing if and how the change in lunar cycle
>>>> (factor) and human presence ( factor) impact trapping success. Trapping
>>>> success is count data but because we only trapped 1 individual at most,
>>>> the
>>>> data can also be considered binary. Human presence (HP) is split into 5
>>>> categories.
>>>>
>>>> Model: I am running Human presence, Moon phase and Land use type as
>>>> fixed
>>>> effects and the site name as a random effect.
>>>>
>>>> Problem: I have quantified human presence in various locations, with
>>>> areas
>>>> exposed to intense human pressures, there is a complete absence of any
>>>> successful trappings (HP2 and HP4 have no trapping success resulting in
>>>> only zeros). Post mcmcglmm, these parameters display auto correlation in
>>>> their graphs (if i set them as fixed or random effects) as well as when
>>>> using the geweke and gelman tests, but almost perfect mixing for the
>>>> others, especially when I run it with 5-20 million iterations. I also
>>>> see
>>>> poorly mixed VCV graphs if the degree of belief is less than 20. I have
>>>> used a variety of priors, see below, with no success. I have also
>>>> increased
>>>> the amount of itterations to 20 million, decreased the thinning factor,
>>>> increased the burnin and used poisson, zapoisson, zipoisson, ordinal and
>>>> threshold distributions, with threshold and ordinal having the best DIC
>>>> values. Together with this, I am also seeing extreme post mean values
>>>> for
>>>> the models that are displaying the lowest DIC values, which is not
>>>> something that I see in any of the literature (see below).
>>>>
>>>> I have read Jarrod Hadfields awesome course and tutorial notes, as well
>>>> as
>>>> other literature in my field, online tutorials, information documents
>>>> and
>>>> the vignettes and help functions in R as well as the correspondence on
>>>> this
>>>> email list between Jarrod Hadfield and other users but I cannot seem to
>>>> figure out why the above is happening. My data set is quite small (176
>>>> entries in total, with about 40 entries per HP category) and according
>>>> to
>>>> what I have read, priors can have a large impact on small to moderately
>>>> sized data sets.
>>>>
>>>> Questions:
>>>> 1. Does the problem lie with my priors? And if so, do you have any tips
>>>> on
>>>> how to solve it?
>>>> 2. Should I be using ordinal or threshold family? I have read that the
>>>> mixing of zi and za poisson models can be poor, and from my tries with
>>>> them, this seems to be the case.
>>>> 3. Are either the extreme post mean values or the auto correlation in
>>>> certain parameters avoidable in this case?
>>>>
>>>> *Examples of priors*
>>>> prior1<-list(R=list(V=diag(1)*1e-8,
>>>> nu=0.2),G=list(G1=list(V=diag(1)*1e-6,
>>>> nu=0.2))
>>>> prior2<-list(R=list(V=diag(1), nu=0.2),G=list(G1=list(V=diag(1), nu=2)))
>>>> prior3<-list(R=list(V=diag(2), nu=0, fix=2), G=list(G1=G1))
>>>> G1=list(V=diag(2), nu=2, alpha.mu=c(0,0), alpha.V=diag(2)*1000)
>>>> prior4:
>>>> A note: I have these with degree belief ranging from 0.0002 to 20, with
>>>> the
>>>> best mixing (apart from the auto correlation in certain parameters)
>>>> with nu
>>>> set at 20 for the R structure. If i set the G structures degree of
>>>> belief
>>>> to less than 20, it mixes the random effect extremely poorly. I have
>>>> also
>>>> used ranging values from 1e-1 to 1e-20 and fixed the variances at 1 and
>>>> 2
>>>> using various different priors.
>>>> *Examples of model scripts:*
>>>> OM1.1<-MCMCglmm(Jackal_trapped ~ Human_presence + MP + Landuse, random
>>>> =~Site, data = data, prior = prior1,
>>>> family = "ordinal", nitt = 5000000, thin = 500, burnin = 500000,
>>>> verbose =
>>>> FALSE, pl = TRUE, DIC=TRUE)
>>>> model1.1<-MCMCglmm(Jackal_trapped~trait,random=~us(trait):Human_presence
>>>> +
>>>>
>>>> us(trait):Landuse,family="zapoisson",rcov=~idh(trait):units,data=data,prior=prior,nitt=500000,thin=500,burnin=200000,verbose=FALSE)
>>>>
>>>> model1.2<-MCMCglmm(Jackal_trapped~trait,random=~idh(trait):Human_presence
>>>> +
>>>>
>>>> idh(trait):Landuse,family="zapoisson",rcov=~idh(trait):units,data=data,prior=prior,nitt=500000,thin=500,burnin=200000,verbose=FALSE)
>>>>
>>>> *Example of large post mean summary*
>>>> family: I have used zapoisson, ordinal and zipoisson distributions
>>>> prior1<-list(R=list(V=diag(1)*1e-4,
>>>> nu=0.2),G=list(G1=list(V=diag(1)*1e-2,
>>>> nu=2)))
>>>> OM1.1<-MCMCglmm(Jackal_trapped ~ Human_presence + MP + Landuse, random
>>>> =~Site, data = data, prior = prior1, family = "family", nitt = 5000000,
>>>> thin = 500, burnin = 500000, verbose = FALSE, pl = TRUE, DIC=TRUE)
>>>> Iterations = 500001:4999501
>>>> Thinning interval = 500
>>>> Sample size = 9000
>>>>
>>>> DIC: 0.003093406
>>>>
>>>> G-structure: ~Site
>>>>
>>>> post.mean l-95% CI u-95% CI eff.samp
>>>> Site 0.1943 0.001154 0.1913 9000
>>>>
>>>> R-structure: ~units
>>>>
>>>> post.mean l-95% CI u-95% CI eff.samp
>>>> units 1.755e+10 1.72e+09 3.97e+10 3632
>>>>
>>>> Location effects: Jackal_trapped ~ Human_presence + MP + Landuse
>>>> post.mean l-95% CI u-95% CI eff.samp
>>>> pMCMC
>>>> (Intercept) -107789 -229797 9915 4666
>>>> 0.0498 *
>>>> Human_presence1 49459 -12241 117809 6957 0.1002
>>>> Human_presence2 -49280 -153682 46279 8478 0.3184
>>>> Human_presence3 18642 -66603 104880 8529 0.6684
>>>> Human_presence4 -214029 -340008 -89259 6098 <1e-04
>>>> ***
>>>> MPFQ -44627 -119345 24385 7328
>>>> 0.1856
>>>> MPNM 12362 -52404 72882 9000
>>>> 0.6691
>>>> MPTQ -43907 -122103 22055 8357
>>>> 0.1942
>>>> Landusereserve -51105 -138603 35033 9000
>>>> 0.2302
>>>>
>>>> *Example of model outputs with auto correlation*
>>>> family: I have used zapoisson, ordinal and zipoisson distributions
>>>> prior2<-list(R=list(V=diag(1)*1e-6,
>>>> nu=20),G=list(G1=list(V=diag(1)*1e-6,
>>>> nu=20)))
>>>> OM2.1<-MCMCglmm(Jackal_trapped ~ Human_presence + MP + Landuse, random
>>>> =~Site, data = data, prior = prior2,
>>>> family = "family", nitt = 5000000, thin = 500, burnin = 500000, verbose
>>>> =
>>>> FALSE, pl = TRUE, DIC=TRUE)
>>>> Iterations = 500001:4999501
>>>> Thinning interval = 500
>>>> Sample size = 9000
>>>>
>>>> DIC: 182.6921
>>>>
>>>> G-structure: ~Site
>>>> post.mean l-95% CI u-95% CI eff.samp
>>>> Site 1.114e-06 5.171e-07 1.911e-06 8515
>>>>
>>>> R-structure: ~units
>>>>
>>>> post.mean l-95% CI u-95% CI eff.samp
>>>> units 1.106e-06 5.129e-07 1.87e-06 9000
>>>>
>>>> Location effects: Jackal_trapped ~ Human_presence + MP + Landuse
>>>>
>>>> post.mean l-95% CI u-95% CI eff.samp
>>>> pMCMC
>>>>
>>>> (Intercept) -0.70349 -1.20259 -0.28723 2.027 <
>>>> 1e-04
>>>> ***
>>>> Human_presence1 0.53752 0.41033 0.67240 6.030 < 1e-04 ***
>>>> Human_presence2 -0.44909 -0.65858 -0.03042 2.600 0.00289 **
>>>> Human_presence3 -0.11236 -0.48808 0.30079 1.683 0.53556
>>>> Human_presence4 -1.38806 -1.74144 -0.93892 2.829 < 1e-04 ***
>>>> MPFQ -0.38918 -0.57513 -0.23967 2.300 <
>>>> 1e-04
>>>> ***
>>>> MPNM 0.14273 -0.05171 0.28830 1.625
>>>> 0.14356
>>>>
>>>> MPTQ -0.25338 -0.52899 -0.03162 1.477
>>>> 0.01222
>>>> *
>>>> Landusereserve -0.64469 -1.06522 -0.18880 2.239 < 1e-04
>>>> ***
>>>>
>>>> *Example: Using my variables as random effects*
>>>> *family: I have used zapoisson, ordinal and zipoisson distributions*
>>>> model1.1<-MCMCglmm(Jackal_trapped~trait,random=~us(trait):Human_presence
>>>> +
>>>>
>>>> us(trait):Landuse,family="family",rcov=~idh(trait):units,data=data,prior=prior,nitt=500000,thin=500,burnin=200000,verbose=FALSE)
>>>> Iterations = 200001:499501
>>>> Thinning interval = 500
>>>> Sample size = 600
>>>>
>>>> DIC: 175.5933
>>>>
>>>> G-structure: ~us(trait):Human_presence
>>>>
>>>>
>>>> post.mean l-95% CI u-95% CI eff.samp
>>>> traitJackal_trapped:traitJackal_trapped.Human_presence 1.179e-06
>>>> 5.584e-07
>>>> 2.051e-06 472.7
>>>> traitza_Jackal_trapped:traitJackal_trapped.Human_presence -1.243e-08
>>>> -5.964e-07 5.494e-07 600.0
>>>> traitJackal_trapped:traitza_Jackal_trapped.Human_presence -1.243e-08
>>>> -5.964e-07 5.494e-07 600.0
>>>> traitza_Jackal_trapped:traitza_Jackal_trapped.Human_presence 1.172e-06
>>>> 5.252e-07 1.883e-06 507.3
>>>>
>>>> ~us(trait):Landuse
>>>>
>>>> post.mean l-95% CI u-95% CI eff.samp
>>>> traitJackal_trapped:traitJackal_trapped.Landuse 1.143e-06
>>>> 4.876e-07
>>>> 2.029e-06 600
>>>> traitza_Jackal_trapped:traitJackal_trapped.Landuse 7.237e-09
>>>> -5.758e-07
>>>> 4.980e-07 600
>>>> traitJackal_trapped:traitza_Jackal_trapped.Landuse 7.237e-09
>>>> -5.758e-07
>>>> 4.980e-07 600
>>>> traitza_Jackal_trapped:traitza_Jackal_trapped.Landuse 1.157e-06
>>>> 4.896e-07
>>>> 2.022e-06 600
>>>>
>>>> R-structure: ~idh(trait):units
>>>>
>>>> post.mean l-95% CI u-95% CI eff.samp
>>>> traitJackal_trapped.units 9.855e-07 4.689e-07 1.685e-06 704.6
>>>> traitza_Jackal_trapped.units 1.017e-06 4.490e-07 1.625e-06 600.0
>>>>
>>>> Location effects: Jackal_trapped ~ trait
>>>>
>>>> post.mean l-95% CI u-95% CI eff.samp pMCMC
>>>> (Intercept) 0.7789 0.7730 0.7875 8.419 <0.002 **
>>>> traitza_Jackal_trapped -2.8911 -2.9052 -2.8741 6.982 <0.002 **
>>>>
>>>> I hope I have shared enough info regarding my problem and that it makes
>>>> sense. I hope my post meets the requirements of the list and that it
>>>> does
>>>> not seem like I am making my problem somebody elses, I am honestly just
>>>> lost at the moment.
>>>> I welcome any constructive criticism and any other help you can provide.
>>>>
>>>> Thank you for your help.
>>>> I look forward to your responses.
>>>>
>>>> With kind regards,
>>>>
>>>> Alexander Edward Botha
>>>> alexbotha555 using gmail.com 082 414 9030
>>>> PhD candidate
>>>> Mammal ecology
>>>>
>>>> [[alternative HTML version deleted]]
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models using r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>
>>>
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