print method for class SPQR

Summarizes and print the output produced by SPQR() in an organized way.

Usage

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

Arguments

x

An object of class SPQR

...

Arguments passed on to print.summary.SPQR

showModel

If TRUE, prints the detailed NN architecture by layer.

Value

No return value, called for side effects.

Details

This is equivalent to the function call print.summary.SPQR(summary.SPQR(object), ...).

Examples

# \donttest{
set.seed(919)
n <- 200
X <- rbinom(n, 1, 0.5)
Y <- rnorm(n, X, 0.8)
control <- list(iter = 200, warmup = 150, thin = 1)
fit <- SPQR(X = X, Y = Y, method = "MCMC", control = control,
            normalize = TRUE, verbose = FALSE)
print(fit, showModel = TRUE)
#> Warning: The ESS has been capped to avoid unstable estimates.
#> Warning: The ESS has been capped to avoid unstable estimates.
#> Warning: The ESS has been capped to avoid unstable estimates.
#> Warning: The ESS has been capped to avoid unstable estimates.
#> Warning: The ESS has been capped to avoid unstable estimates.
#> Warning: The ESS has been capped to avoid unstable estimates.
#> Warning: The ESS has been capped to avoid unstable estimates.
#> Warning: The ESS has been capped to avoid unstable estimates.
#> Warning: The ESS has been capped to avoid unstable estimates.
#> Warning: The ESS has been capped to avoid unstable estimates.
#> Warning: The ESS has been capped to avoid unstable estimates.
#> 
#> SPQR fitted using MCMC approach with ARD prior🚀
#> 
#> Model specification:
#>     Layers
#>    Input Output Activation
#>        1     10       tanh
#>       10     10    softmax
#> 
#> MCMC diagnostics:
#>   Final acceptance ratio is 0.90 and target is 0.9
#> 
#> Expected log pointwise predictive density (elpd) estimates:
#>   elpd.LOO = 90.65508,  elpd.WAIC = 90.01237
#> 
#> Elapsed time: 0.04 minutes
# }