Short neuralGAM summary

Default print statement for a neuralGAM object.

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

Arguments

x

neuralGAM object.

...

Other arguments.

Value

The printed output of the object:

• Distribution family

• Formula

• Intercept value

• Mean Squared Error (MSE)

• Training sample size

Author

Ines Ortega-Fernandez, Marta Sestelo.

Examples

# \dontrun{

n <- 24500

seed <- 42
set.seed(seed)

x1 <- runif(n, -2.5, 2.5)
x2 <- runif(n, -2.5, 2.5)
x3 <- runif(n, -2.5, 2.5)

f1 <-x1**2
f2 <- 2*x2
f3 <- sin(x3)
f1 <- f1 - mean(f1)
f2 <- f2 - mean(f2)
f3 <- f3 - mean(f3)

eta0 <- 2 + f1 + f2 + f3
epsilon <- rnorm(n, 0.25)
y <- eta0 + epsilon
train <- data.frame(x1, x2, x3, y)

library(neuralGAM)
ngam <- neuralGAM(y ~ s(x1) + x2 + s(x3), data = train,
                 num_units = 1024, family = "gaussian",
                 activation = "relu",
                 learning_rate = 0.001, bf_threshold = 0.001,
                 max_iter_backfitting = 10, max_iter_ls = 10,
                 seed = seed
                 )
#> [1] "Initializing neuralGAM..."
#> [1] "BACKFITTING Iteration 1 - Current Err =  0.32048205436 BF Threshold =  0.001 Converged =  FALSE"
#> [1] "BACKFITTING Iteration 2 - Current Err =  0.000623513488713123 BF Threshold =  0.001 Converged =  TRUE"
print(ngam)
#> Class: neuralGAM 
#> 
#> Distribution Family:  gaussian
#> Formula:  y ~ s(x1) + x2 + s(x3)
#> Intercept: 2.2215
#> MSE: 1.0049
#> Sample size: 24500
# }