Description Usage Arguments Details Value Author(s) See Also Examples
density
method for class mcmcSTmodel
.
1 2 |
x |
|
BurnIn |
Number of initial points to ignore. |
estSTmodel |
Either a |
... |
Additional parameters passed to
|
Computes kernel density estimates for the MCMC-parameters; as well as approximate Gaussian densities based on the Fischer-information.
List containing density estimate and Gaussian densities for all model parameters.
Johan Lindstrom
Other mcmcSTmodel methods: MCMC.STmodel
,
plot.density.mcmcSTmodel
,
plot.mcmcSTmodel
,
print.mcmcSTmodel
,
print.summary.mcmcSTmodel
,
summary.mcmcSTmodel
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ##load estimation results
data(est.mesa.model)
##and MCMC results instead
data(MCMC.mesa.model)
##compute density estimates for the results, and use the Gaussian approximation
##based on Fischer information as reference.
dens <- density(MCMC.mesa.model, estSTmodel=est.mesa.model)
##all the estimated densities
str(dens,1)
##or results for one paramter
dens[[1]]
##plot density functions
plot(dens)
##for a different paramter, along with Gaussian approx
plot(dens, 3, norm.col="red")
##all covariance parameters
par(mfrow=c(3,3),mar=c(4,4,2.5,.5))
for(i in 9:17){
plot(dens, i, norm.col="red")
}
|
Loading required package: Matrix
List of 19
$ gamma.lax.conc.1500 :List of 2
$ alpha.const.(Intercept) :List of 2
$ alpha.const.log10.m.to.a1 :List of 2
$ alpha.const.s2000.pop.div.10000:List of 2
$ alpha.const.km.to.coast :List of 2
$ alpha.V1.(Intercept) :List of 2
$ alpha.V1.km.to.coast :List of 2
$ alpha.V2.(Intercept) :List of 2
$ alpha.V2.km.to.coast :List of 2
$ log.range.const.exp :List of 2
$ log.sill.const.exp :List of 2
$ log.range.V1.exp :List of 2
$ log.sill.V1.exp :List of 2
$ log.range.V2.exp :List of 2
$ log.sill.V2.exp :List of 2
$ nu.log.range.exp :List of 2
$ nu.log.sill.exp :List of 2
$ nu.log.nugget.(Intercept).exp :List of 2
$ nu.log.nugget.typeFIXED.exp :List of 2
- attr(*, "class")= chr "density.mcmcSTmodel"
$density
Call:
density.default(x = newX[, i])
Data: newX[, i] (2500 obs.); Bandwidth 'bw' = 0.0004833
x y
Min. :-0.008585 Min. : 0.00747
1st Qu.:-0.003368 1st Qu.: 6.48915
Median : 0.001849 Median : 24.59657
Mean : 0.001849 Mean : 47.87467
3rd Qu.: 0.007066 3rd Qu.: 85.99468
Max. : 0.012282 Max. :156.83253
$approx.gauss
$approx.gauss$mean
[1] 0.0008978266
$approx.gauss$sd
[1] 0.002694145
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