Extract the Y values for a PrestoGP model.
Source:R/PrestoGP_Model.R, R/PrestoGP_Vecchia.R, R/PrestoGP_Multivariate_Vecchia.R
get_Y.RdThis method extracts the values of Y for a PrestoGP model. If imputation was used when fitting the model, the missing Y's will be replaced with their imputed values.
Usage
get_Y(model)
# S4 method for class 'VecchiaModel'
get_Y(model)
# S4 method for class 'MultivariateVecchiaModel'
get_Y(model)Value
A vector or list containing the values of Y. Any missing Y's will be replaced with their imputed values.
References
Messier, K.P. and Katzfuss, M. "Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide", The Annals of Applied Statistics (2021) 15(2):688-710.
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: 487.1692
#> 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: 487.0956
#> Current MSE: 9.106944
#> Beginning iteration 3
#> Estimating theta...
#> Estimation of theta complete
#> Estimating beta...
#> Estimation of beta complete
#> Iteration 3 complete
#> Current penalized negative log likelihood: 487.0956
#> Current MSE: 9.106944
get_Y(soil.vm)
#> [1] 7.233333 14.633333 12.666667 13.733333 7.400000 15.966667 10.266667
#> [8] 8.033333 9.633333 7.433333 8.966667 7.933333 8.200000 7.700000
#> [15] 8.933333 7.066667 15.000000 9.766667 10.500000 13.366667 13.700000
#> [22] 12.866667 22.300000 14.600000 15.566667 17.066667 10.700000 7.233333
#> [29] 10.700000 12.133333 14.066667 9.700000 8.866667 7.833333 8.633333
#> [36] 7.566667 13.233333 13.633333 11.233333 12.700000 9.400000 6.966667
#> [43] 7.533333 12.533333 9.200000 8.833333 9.466667 10.133333 6.933333
#> [50] 8.533333 8.300000 4.566667 8.450000 11.100000 6.050000 10.950000
#> [57] 8.700000 14.050000 14.500000 10.250000 11.100000 7.300000 16.200000
#> [64] 14.950000 13.400000 11.200000 8.750000 13.500000 11.350000 11.050000
#> [71] 9.500000 10.300000 10.100000 9.200000 8.550000 9.600000 10.600000
#> [78] 7.233333 9.100000 8.633333 9.366667 8.333333 9.300000 10.766667
#> [85] 13.266667 15.433333 10.733333 11.400000 9.733333 9.866667 13.166667
#> [92] 13.633333 11.100000 10.633333 12.400000 11.200000 9.433333 14.000000
#> [99] 12.300000 15.300000 14.333333 10.400000 9.666667 14.450000 14.833333
#> [106] 13.666667 9.233333 11.850000 12.900000 12.466667 15.566667 13.266667
#> [113] 10.700000 10.666667 12.533333 12.566667 14.366667 12.333333 12.566667
#> [120] 12.600000 11.800000 12.866667 8.225000 12.600000 8.200000 8.850000
#> [127] 12.250000 8.500000 9.450000 8.300000 13.550000 9.650000 11.150000
#> [134] 13.650000 12.800000 8.500000 11.700000 11.150000 5.550000 10.000000
#> [141] 10.100000 10.100000 9.400000 7.550000 8.700000 8.900000 9.700000
#> [148] 6.950000 8.650000 11.400000 12.350000 12.050000 11.950000 11.850000
#> [155] 10.600000 12.450000 13.500000 13.100000 9.300000 11.600000 10.650000
#> [162] 9.300000 9.150000 9.350000 10.733333 16.950000 10.900000 12.766667
#> [169] 15.433333 9.833333 12.466667 13.450000 14.266667 11.566667 6.266667
#> [176] 15.466667 12.500000 13.233333 10.566667 12.166667 8.866667 8.933333
#> [183] 9.066667 8.466667 17.966667 14.800000 19.366667 19.300000 13.733333
#> [190] 13.600000 20.800000 7.966667 8.833333 20.466667 14.700000 15.000000
#> [197] 14.533333 12.066667 18.633333 19.100000