inla.climate.ar1: AR(1) weights and parameter evaluation

In eirikmn/INLA.climate: Full Bayesian analysis of radiative forcing climate model

AR(1) weights and parameter evaluation

Description

Computes Bayesian inference about the weights and first-order autocorrelation parameters of the AR(1) components based on the posterior distribution obtained from INLA.

Usage

inla.climate.ar1(result, m, nsamples = 100000, seed = 1234, print.progress = FALSE)

Arguments

result	An inla.climate or an inla object which can be accessed using results$inla.result, where results is the returned object from the inla.climate function.
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 TRUE.

Value

If result is a list of class inla.climate, then this function returns the same object, but appends the following objects:

result$ar1	A list containing the mean, standard deviation, quantiles and samples for the weights and first lag correlation parameter. If m is equal to one, the weight is equal to one and the posterior distribution for the first-lag correlation parameter is given instead of the samples.
result$time$ar1	The number of Monte Carlo simulations used. They are only used if m>1.
result$misc$ar1.options$nsamples	The number of Monte Carlo simulations used. They are only used if m>1.
result$misc$ar1.options$seed	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.

Author(s)

Eirik Myrvoll-Nilsen [e-mail]

References

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.

See Also

inla.climate, inla.climate.mu

Examples

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)
}

eirikmn/INLA.climate documentation built on Feb. 6, 2023, 11:41 a.m.