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)
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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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