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Create a matrix of output plots from a mvgam object

Source: R/pairs.mvgam.R
pairs.mvgam.Rd

A pairs method that is customized for MCMC output.

Usage

# S3 method for mvgam
pairs(x, variable = NULL, regex = FALSE, use_alias = TRUE, ...)

Arguments

x

An object of class mvgam or jsdgam

variable

Names of the variables (parameters) to plot, as given by a character vector or a regular expression (if regex = TRUE). By default, a hopefully not too large selection of variables is plotted.

regex

Logical; Indicates whether variable should be treated as regular expressions. Defaults to FALSE.

use_alias

Logical. If more informative names for parameters are available (i.e. for beta coefficients b or for smoothing parameters rho), replace the uninformative names with the more informative alias. Defaults to TRUE

...

Further arguments to be passed to mcmc_pairs.

Value

Plottable objects whose classes depend on the arguments supplied. See mcmc_pairs for details.

Details

For a detailed description see mcmc_pairs.

Examples

# \donttest{
simdat <- sim_mvgam(n_series = 1, trend_model = 'AR1')
mod <- mvgam(y ~ s(season, bs = 'cc'),
             trend_model = AR(),
             noncentred = TRUE,
             data = simdat$data_train,
             chains = 2)
#> Compiling Stan program using cmdstanr
#> 
#> Start sampling
#> Running MCMC with 2 parallel chains...
#> 
#> Chain 1 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 1 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 1 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 2 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 1 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 1 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 1 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 1 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 2 Iteration: 100 / 1000 [ 10%]  (Warmup) 
#> Chain 2 Iteration: 200 / 1000 [ 20%]  (Warmup) 
#> Chain 1 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 2 Iteration: 300 / 1000 [ 30%]  (Warmup) 
#> Chain 2 Iteration: 400 / 1000 [ 40%]  (Warmup) 
#> Chain 1 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 2 Iteration: 500 / 1000 [ 50%]  (Warmup) 
#> Chain 2 Iteration: 501 / 1000 [ 50%]  (Sampling) 
#> Chain 1 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 2 Iteration: 600 / 1000 [ 60%]  (Sampling) 
#> Chain 1 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 2 Iteration: 700 / 1000 [ 70%]  (Sampling) 
#> Chain 1 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 2 Iteration: 800 / 1000 [ 80%]  (Sampling) 
#> Chain 1 finished in 1.0 seconds.
#> Chain 2 Iteration: 900 / 1000 [ 90%]  (Sampling) 
#> Chain 2 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 2 finished in 1.1 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 1.0 seconds.
#> Total execution time: 1.4 seconds.
#> 
pairs(mod)

pairs(mod, variable = c('ar1', 'sigma'), regex = TRUE)

# }