This method prints a summary of a PrestoGP model and its parameters.
Examples
data(soil)
soil <- soil[!is.na(soil[,5]),] # remove rows with NA's
y <- soil[,4] # predict moisture content
X <- as.matrix(soil[,5:9])
locs <- as.matrix(soil[,1:2])
soil.vm <- new("VecchiaModel", n_neighbors = 10)
soil.vm <- prestogp_fit(soil.vm, y, X, locs)
#>
#> Estimating initial beta...
#> Estimation of initial beta complete
#>
#> Beginning iteration 1
#> Estimating theta...
#> Estimation of theta complete
#> Estimating beta...
#> Estimation of beta complete
#> Iteration 1 complete
#> Current penalized negative log likelihood: 486.7002
#> Current MSE: 9.104869
#> Beginning iteration 2
#> Estimating theta...
#> Estimation of theta complete
#> Estimating beta...
#> Estimation of beta complete
#> Iteration 2 complete
#> Current penalized negative log likelihood: 486.3541
#> Current MSE: 9.107815
#> Beginning iteration 3
#> Estimating theta...
#> Estimation of theta complete
#> Estimating beta...
#> Estimation of beta complete
#> Iteration 3 complete
#> Current penalized negative log likelihood: 486.3306
#> Current MSE: 9.108282
#> Beginning iteration 4
#> Estimating theta...
#> Estimation of theta complete
#> Estimating beta...
#> Estimation of beta complete
#> Iteration 4 complete
#> Current penalized negative log likelihood: 486.3306
#> Current MSE: 9.108079
show(soil.vm)
#> Matern covariance parameters (theta):
#> $sigma
#> [1] 9.726825
#>
#> $scale
#> [1] 14.74194
#>
#> $smoothness
#> [1] 0.7231443
#>
#> $nuggets
#> [1] 0.9498517
#>
#> Regression coefficients (beta):
#> $Y
#> named numeric(0)
#>
#> $`(Intercept)`
#> (Intercept)
#> 11.41328
#>
#> Model type: VecchiaModel
#> Nearest neighbors: 10
#> Scaling: 1 1
#> Penalized likelihood: 486.3306
#> MSE: 9.108079