Set up an autoregressive (AR) term of order p in brms. The function does not evaluate its arguments – it exists purely to help set up a model with AR terms.
Arguments
- time
An optional time variable specifying the time ordering of the observations. By default, the existing order of the observations in the data is used.
- gr
An optional grouping variable. If specified, the correlation structure is assumed to apply only to observations within the same grouping level.
- p
A non-negative integer specifying the autoregressive (AR) order of the ARMA structure. Default is
1.- cov
A flag indicating whether ARMA effects should be estimated by means of residual covariance matrices. This is currently only possible for stationary ARMA effects of order 1. If the model family does not have natural residuals, latent residuals are added automatically. If
FALSE(the default), a regression formulation is used that is considerably faster and allows for ARMA effects of order higher than 1 but is only available forgaussianmodels and some of its generalizations.
Value
An object of class 'arma_term', which is a list
of arguments to be interpreted by the formula
parsing functions of brms.
Examples
# \dontrun{
data("LakeHuron")
LakeHuron <- as.data.frame(LakeHuron)
fit <- brm(x ~ ar(p = 2), data = LakeHuron)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 2.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 1.142 seconds (Warm-up)
#> Chain 1: 0.227 seconds (Sampling)
#> Chain 1: 1.369 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
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#> Chain 2:
#> Chain 2: Elapsed Time: 2.05 seconds (Warm-up)
#> Chain 2: 0.208 seconds (Sampling)
#> Chain 2: 2.258 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 2.1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
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#> Chain 3:
#> Chain 3: Elapsed Time: 1.144 seconds (Warm-up)
#> Chain 3: 0.229 seconds (Sampling)
#> Chain 3: 1.373 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 2.1e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
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#> Chain 4:
#> Chain 4: Elapsed Time: 1.015 seconds (Warm-up)
#> Chain 4: 0.224 seconds (Sampling)
#> Chain 4: 1.239 seconds (Total)
#> Chain 4:
summary(fit)
#> Family: gaussian
#> Links: mu = identity
#> Formula: x ~ ar(p = 2)
#> Data: LakeHuron (Number of observations: 98)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Correlation Structures:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> ar[1] 1.07 0.10 0.86 1.26 1.00 2338 2200
#> ar[2] -0.25 0.10 -0.46 -0.05 1.00 2179 2037
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 579.24 0.41 578.56 580.22 1.00 2470 1601
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 0.71 0.05 0.62 0.83 1.00 2651 2513
#>
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
# }