Prints a summary for objects fitted by bnec

Prints a summary for objects fitted by bnec. x should be of class bayesnecfit or bayesmanecfit.

# S3 method for class 'bayesnecfit'
print(x, ...)

# S3 method for class 'bayesmanecfit'
print(x, ...)

Arguments

x

An object of class bayesnecfit or bayesmanecfit.

...

Unused.

Value

A summary print of the fitted model as returned for a brmsfit object.

Examples

# \donttest{
library(bayesnec)
print(manec_example)
#> Warning: Parts of the model have not converged (some Rhats are > 1.05). Be careful when analysing the results! We recommend running more iterations and/or setting stronger priors.
#> Object of class bayesmanecfit
#> 
#>  Family: gaussian  
#>   Links: mu = identity; sigma = identity  
#> 
#> Number of posterior draws per model:  100
#> 
#> Model weights (Method: pseudobma_bb_weights):
#>             waic   wi
#> nec4param 181.68 0.83
#> ecx4param 199.31 0.17
#> 
#> 
#> Summary of weighted N(S)EC posterior estimates:
#> NB: Model set contains a combination of ECx and NEC
#>     models, and is therefore a model averaged
#>     combination of NEC and NSEC estimates.
#>        Estimate Q2.5 Q97.5
#> N(S)EC     1.45 0.75  1.53
#> 
#> 
#> Bayesian R2 estimates:
#>           Estimate Est.Error Q2.5 Q97.5
#> nec4param     0.86      0.01 0.84  0.88
#> ecx4param     0.85      0.01 0.82  0.87
#> 
#> 
#> Warning: The following model had Rhats > 1.05 (no convergence):
#>   -  nec4param
#>   -  ecx4param
#> Consider dropping them (see ?amend)
nec4param <- pull_out(manec_example, "nec4param")
#> Pulling out model(s): nec4param
print(nec4param)
#> Warning: Parts of the model have not converged (some Rhats are > 1.05). Be careful when analysing the results! We recommend running more iterations and/or setting stronger priors.
#> Object of class bayesnecfit containing the nec4param model
#> 
#>  Family: gaussian 
#>   Links: mu = identity; sigma = identity 
#> Formula: y ~ bot + (top - bot) * exp(-exp(beta) * (x - nec) * step(x - nec)) 
#>          bot ~ 1
#>          top ~ 1
#>          beta ~ 1
#>          nec ~ 1
#>    Data: data (Number of observations: 100) 
#>   Draws: 2 chains, each with iter = 200; warmup = 150; thin = 1;
#>          total post-warmup draws = 100
#> 
#> Regression Coefficients:
#>      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> bot     -8.42      1.90   -13.58    -5.68 1.21       10       35
#> top      2.17      0.06     2.05     2.26 1.01       95       76
#> beta    -0.67      0.25    -1.18    -0.20 1.17       13       44
#> nec      1.46      0.04     1.36     1.53 1.01       58       33
#> 
#> Further Distributional Parameters:
#>       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma     0.52      0.04     0.46     0.61 0.99      155      102
#> 
#> 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).
#> 
#> 
#>     Estimate Q2.5 Q97.5
#> NEC     1.46 1.36  1.53
#> 
#> 
#> Bayesian R2 estimates:
#>    Estimate Est.Error Q2.5 Q97.5
#> R2     0.86      0.01 0.84  0.88
#> 
#> 
# }