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: 487.6832
#> 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: 482.6875
#> Current MSE: 9.034514
#> Beginning iteration 3
#> Estimating theta...
#> Estimation of theta complete
#> Estimating beta...
#> Estimation of beta complete
#> Iteration 3 complete
#> Current penalized negative log likelihood: 482.4418
#> Current MSE: 9.041655
#> Beginning iteration 4
#> Estimating theta...
#> Estimation of theta complete
#> Estimating beta...
#> Estimation of beta complete
#> Iteration 4 complete
#> Current penalized negative log likelihood: 482.4418
#> Current MSE: 9.029602
show(soil.vm)
#> Matern covariance parameters (theta):
#> $sigma
#> [1] 10.28376
#>
#> $scale
#> [1] 13.52572
#>
#> $smoothness
#> [1] 0.9088149
#>
#> $nuggets
#> [1] 0.7629514
#>
#> Regression coefficients (beta):
#> $Y
#> NO3.N NH4.N DOC N20N
#> -0.033945212 0.026459220 0.002100641 24.125223093
#>
#> $`(Intercept)`
#> (Intercept)
#> 11.39899
#>
#> Model type: VecchiaModel
#> Nearest neighbors: 10
#> Scaling: 1 1
#> Penalized likelihood: 482.4418
#> MSE: 9.029602