Dear List,


We are using an unmatched count technique to understand the prevalence of illegal hunting. This method is used to make sure people are anonymous and thus hopefully give honest answers about their illegal activity. People are allocated to two treatments and get either a card with four activities (teaching, farming, livestock herding and trading,) or with five activities (teaching, farming, livestock herding, trading and hunting of which one is the sensitive question). The respondents then indicate how many activities they are involved in on their card. We have also asked each person four times the same question but wanted to know whether each of the activities relate to the dry or wet season and whether it was for cash or non-cash. So we have Counts as a dependent variable as the number of activities each person does, Question with four levels (cash dry, cash wet, NonCash dry, NonCash wet), treat with two levels (card with 4 activities, card with 5 activities including the sensitive question, Treatment). We have run the model below with Village and id as random effects.

We are now interested why villages are different from each other and extracted the random effects BLUPs using ranef() to be able to use village level explanatory variables in a new model (e.g. distance to protected area). This will give us the estimate how much each village deviates from the overall number of activities, the overall intercept; not really what we are interested in. We are interested in how much the villages deviate from the estimate of the interaction Question*treat because this gives us estimates about the prevalence of illegal hunting in the different seasons rather than the number of activities people are involved in. Any comments or ideas how to implement this in R would be greatly appreciated. An output from our model is below.

Many thanks,
Nils


> library(lme4)

> m1 <- lmer(Counts~Question*treat+(1|Village/id),data=data2,REML=F)



> summary(m1)

Linear mixed model fit by maximum likelihood

Formula: Counts ~ Question * treat + (1 | Village/id)

   Data: data2

  AIC  BIC logLik deviance REMLdev

7806 7876  -3892     7784    7825

Random effects:

Groups     Name        Variance Std.Dev.

id:Village (Intercept) 0.21712  0.46596

Village    (Intercept) 0.13535  0.36791

Residual               0.23434  0.48409

Number of obs: 4356, groups: id:Village, 1092; Village, 15



Fixed effects:

                                 Estimate Std. Error t value

(Intercept)                      1.488733   0.099243  15.001

QuestionCash wet                 0.095400   0.029285   3.258

QuestionNonCash Dry              0.285618   0.029226   9.773

QuestionNonCash Wet              0.514709   0.029226  17.611

treatTreatment                   0.184647   0.040999   4.504

QuestionCash wet:treatTreatment -0.065976   0.041561  -1.587

QuestionNonCash Dry:treatTreatment0.005799   0.041520   0.140

QuestionNonCash Wet:treatTreatment-0.030882   0.041532  -0.744





> ranef(m1)

$Village

               (Intercept)

Guta          0.0822752928

Hunyari      -0.0614857190

Ketembere     0.6976740857

Kitunguruma  -0.3494539970

Koreri       -0.1217766001

Kunzugu      -0.4999783998

Ligamba      -0.4522420174

Makundusi     0.8675942165

Manyamanyama -0.1824419221

Merenga      -0.0006875998

Migungani    -0.1540653530

Morotonga     0.1473949913

Nyamburu     -0.1751358164

Nyamoko       0.0567038683

Robanda       0.1456249700





------------------------------------------------------------------------
Dr Nils Bunnefeld
Imperial College London
Silwood Park
SL5 7PY, Ascot, UK
http://www.iccs.org.uk/nils-bunnefeld
http://fp7hunt.net/
Tel: +44 20 7594 9086


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