Calculate acceptance rates in MCMC chains

Calculate acceptance rates of parameters in the IFM.

Usage

accept.calculate(x, model = c("naive", "missing", "robust"))

Arguments

x

A named list with the MCMC chains estimated by ifm.naive.MCMC, ifm.missing.MCMC, or ifm.robust.MCMC.

model

Either "naive", "missing", or "robust"

Value

Named list containing MCMC chain acceptance rates. Names are built from the input list, e.g., for model=“naive":

acc.b.chain

Acceptance rates of parameter b

acc.e.chain

Acceptance rates of parameter e

acc.y.chain

Acceptance rates of parameter y

acc.alpha.chain

Acceptance rates of parameter alpha

acc.x.chain

Acceptance rates of parameter x

Author

Benjamin Risk

Examples

data(simulatedifm)

# Here, we run a chain with random initial values:
init1=list(alpha=runif(1,1,30), b=runif(1,0,5),y=runif(1,0,20),e=runif(1,0,1),x=runif(1,0,5))

inm1 <- ifm.naive.MCMC(niter=1000,init=init1,z.data =
 z.sim,site.distance=sim.distance,site.area=sim.area,
  sd.prop.alpha=4,sd.prop.b=0.6,sd.prop.y=40,sd.prop.e=0.05,sd.prop.x=0.4,nthin=1,print.by=100)
#> [1] 1
#> [1] 101
#> [1] 201
#> [1] 301
#> [1] 401
#> [1] 501
#> [1] 601
#> [1] 701
#> [1] 801
#> [1] 901
accept.calculate(inm1,model='naive')
#> $e.chain
#> [1] 0.493
#> 
#> $x.chain
#> [1] 0.341
#> 
#> $y.chain
#> [1] 0.547
#> 
#> $b.chain
#> [1] 0.211
#> 
#> $alpha.chain
#> [1] 0.216
#> 
#> $deviance.chain
#> [1] 0.912
#>