Set up an unstructured (UNSTR) correlation term in brms. The function does not evaluate its arguments – it exists purely to help set up a model with UNSTR terms.
Value
An object of class 'unstr_term', which is a list
of arguments to be interpreted by the formula
parsing functions of brms.
Examples
# \dontrun{
# add an unstructured correlation matrix for visits within the same patient
fit <- brm(count ~ Trt + unstr(visit, patient), data = epilepsy)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 9.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.99 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.739 seconds (Warm-up)
#> Chain 1: 0.483 seconds (Sampling)
#> Chain 1: 1.222 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 4.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.614 seconds (Warm-up)
#> Chain 2: 0.497 seconds (Sampling)
#> Chain 2: 1.111 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 4.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3:
#> Chain 3: Elapsed Time: 0.629 seconds (Warm-up)
#> Chain 3: 0.507 seconds (Sampling)
#> Chain 3: 1.136 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 4.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
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#> Chain 4:
#> Chain 4: Elapsed Time: 0.69 seconds (Warm-up)
#> Chain 4: 0.559 seconds (Sampling)
#> Chain 4: 1.249 seconds (Total)
#> Chain 4:
summary(fit)
#> Family: gaussian
#> Links: mu = identity
#> Formula: count ~ Trt + unstr(visit, patient)
#> Data: epilepsy (Number of observations: 236)
#> 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
#> cortime(1,2) 0.79 0.05 0.68 0.87 1.00 2510 2697
#> cortime(1,3) 0.69 0.06 0.56 0.79 1.00 2967 2909
#> cortime(2,3) 0.73 0.06 0.59 0.84 1.00 2910 2558
#> cortime(1,4) 0.78 0.05 0.68 0.86 1.00 2494 2811
#> cortime(2,4) 0.91 0.02 0.86 0.95 1.00 2869 2538
#> cortime(3,4) 0.74 0.07 0.59 0.84 1.00 2865 2817
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 8.32 2.16 4.01 12.54 1.00 4395 2687
#> Trt1 -0.69 3.02 -6.70 5.09 1.00 4621 3026
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 12.59 0.92 10.89 14.59 1.00 2627 2910
#>
#> 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).
# }