Description Usage Arguments Value Author(s) References See Also Examples
View source: R/inla.climate.ar1.R
Computes Bayesian inference about the weights and first-order autocorrelation parameters of the AR(1) components based on the posterior distribution obtained from INLA.
1 | inla.climate.ar1(result, m, nsamples = 100000, seed = 1234, print.progress = FALSE)
|
result |
An |
m |
The number of AR(1) components used to model the temperature response. |
nsamples |
The number of Monte Carlo simulations used in obtaining transient climate response inference. |
seed |
Seed used for random number generator. |
print.progress |
Prints progression if |
If result
is a list of class inla.climate
, then this function returns the same object, but appends the following objects:
|
A list containing the mean, standard deviation, quantiles and samples for the weights and first lag correlation parameter. If |
|
The number of Monte Carlo simulations used. They are only used if |
|
The number of Monte Carlo simulations used. They are only used if |
|
The seed used for the random number generator. |
If result
is a list of class inla
, then a list containing the elements of result$ar1
and result$time$ar1
above is returned.
Eirik Myrvoll-Nilsen eirik.myrvoll-nilsen@uit.no
Fredriksen, H.B., Rypdal, M. (2017) Long-Range Persistence in Global Surface Temperatures Explained by Linear Multibox Energy Balance Models. Journal of Climate 30 (18), 7157–7168.
1 2 3 4 5 | if(require("INLA",quietly=TRUE)){
data(GISS_E2_R)
result.climate <- inla.climate(data=GISS_E2_R$Temperature,forcing=GISS_E2_R$Forcing,m=3,model="ar1")
result.tcr <- inla.climate.ar1(result.climate$inla.result,m=3)
}
|
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