Set up COSY correlation structures

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.

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 0.000515 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.15 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 2.559 seconds (Warm-up)
#> Chain 1:                2.705 seconds (Sampling)
#> Chain 1:                5.264 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000151 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.51 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 2.5 seconds (Warm-up)
#> Chain 2:                2.396 seconds (Sampling)
#> Chain 2:                4.896 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.00015 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.5 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 2.575 seconds (Warm-up)
#> Chain 3:                2.381 seconds (Sampling)
#> Chain 3:                4.956 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0.000152 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.52 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 2.696 seconds (Warm-up)
#> Chain 4:                2.34 seconds (Sampling)
#> Chain 4:                5.036 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).
# }

Set up COSY correlation structures

Usage

Arguments

Value

See also

Examples