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Set up COSY correlation structures

Source: R/formula-ac.R
cosy.Rd

Set up a compounds symmetry (COSY) term in brms. The function does not evaluate its arguments – it exists purely to help set up a model with COSY terms.

Usage

cosy(time = NA, gr = NA)

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.

Value

An object of class 'cosy_term', which is a list of arguments to be interpreted by the formula parsing functions of brms.

See also

autocor-terms

Examples

# \dontrun{
data("lh")
lh <- as.data.frame(lh)
fit <- brm(x ~ cosy(), data = lh)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000493 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.93 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 2.204 seconds (Warm-up)
#> Chain 1:                2.295 seconds (Sampling)
#> Chain 1:                4.499 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.00014 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.4 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 2.166 seconds (Warm-up)
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#> Chain 2:                4.257 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.00014 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.4 seconds.
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 2.232 seconds (Warm-up)
#> Chain 3:                2.061 seconds (Sampling)
#> Chain 3:                4.293 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0.000141 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.41 seconds.
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 2.35 seconds (Warm-up)
#> Chain 4:                2.035 seconds (Sampling)
#> Chain 4:                4.385 seconds (Total)
#> Chain 4: 
summary(fit)
#>  Family: gaussian 
#>   Links: mu = identity 
#> Formula: x ~ cosy() 
#>    Data: lh (Number of observations: 48) 
#>   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
#> cosy     0.55      0.28     0.04     0.97 1.00      463      874
#> 
#> Regression Coefficients:
#>           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept     2.37      0.93     0.22     4.34 1.00      944      651
#> 
#> Further Distributional Parameters:
#>       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma     1.09      0.68     0.54     3.01 1.01      482      807
#> 
#> 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).
# }