Wraps morie_ghosal_gp_squared_exponential.
Value
Named list with estimate, se, mu, sd, ci_lower, ci_upper, r2, log_marginal, length_scale, noise, n, method.
Examples
set.seed(1)
morie_ghosal_np_regression(x = rnorm(50), y = rnorm(50))
#> $estimate
#> [1] 0.1170772
#>
#> $se
#> [1] 0.03487577
#>
#> $mu
#> [1] 0.80390118 -0.20399032 1.20217771 0.25887980 -0.04464533 1.18485493
#> [7] 0.10509424 0.17369597 0.15549033 -0.02250289 0.20168695 0.01934957
#> [13] 0.79061893 -0.17392582 0.05468760 -0.31921145 -0.32112680 0.10588530
#> [19] 0.15302918 0.16221678 0.11587455 0.16461122 -0.29236834 -0.67174490
#> [25] 0.16968374 -0.31688732 -0.25494405 0.07578198 0.40370783 0.04674204
#> [31] 0.10502807 -0.29727329 0.01715742 -0.31744398 0.38584683 0.23513475
#> [37] 0.18233809 -0.31606077 0.05801875 0.16911315 -0.24590275 -0.12128079
#> [43] 0.17751476 0.14704263 0.95312345 0.99386451 -0.00672778 0.16792436
#> [49] -0.29123316 0.13105517
#>
#> $sd
#> [1] 0.03329001 0.02882010 0.04411942 0.07236252 0.02888946 0.04307109
#> [7] 0.02709173 0.02590847 0.02600087 0.02867464 0.05605108 0.02835028
#> [13] 0.03314600 0.08640793 0.04256209 0.02647477 0.02666642 0.03257666
#> [19] 0.02758870 0.02583243 0.03135294 0.02662205 0.02765663 0.07396358
#> [25] 0.02564364 0.02642408 0.02665284 0.06580259 0.03081810 0.02801917
#> [31] 0.04886122 0.02637643 0.02837411 0.02643342 0.06363126 0.03015074
#> [37] 0.02990941 0.02641231 0.04125640 0.02626646 0.02672981 0.02787028
#> [43] 0.02556680 0.02620533 0.03550223 0.03635348 0.02860875 0.02635956
#> [49] 0.02640063 0.02967948
#>
#> $ci_lower
#> [1] 0.60311356 -0.40210427 0.99352470 0.02186880 -0.24279809 0.97704507
#> [7] -0.09208056 -0.02286695 -0.04111946 -0.22053574 -0.01769299 -0.17850380
#> [13] 0.58992285 -0.42836829 -0.15271899 -0.51606404 -0.51807875 -0.09445234
#> [19] -0.04441017 -0.03430770 -0.08371160 -0.03231766 -0.48984419 -0.91064700
#> [25] -0.02674572 -0.51371374 -0.45188893 -0.15376620 0.20444194 -0.15093005
#> [31] -0.10764497 -0.49407517 -0.18070907 -0.51427522 0.15866252 0.03626146
#> [37] -0.01639517 -0.51288113 -0.14837181 -0.02763221 -0.44288770 -0.31887200
#> [43] -0.01887622 -0.04967141 0.75088523 0.79104617 -0.20472403 -0.02886883
#> [49] -0.48804750 -0.06754561
#>
#> $ci_upper
#> [1] 1.004688808 -0.005876361 1.410830722 0.495890806 0.153507430
#> [6] 1.392664793 0.302269035 0.370258899 0.352100117 0.175529966
#> [11] 0.421066894 0.217202936 0.991315008 0.080516646 0.262094185
#> [16] -0.122358858 -0.124174860 0.306222940 0.350468526 0.358741251
#> [21] 0.315460708 0.361540106 -0.094892483 -0.432842790 0.366113210
#> [26] -0.120060888 -0.057999172 0.305330164 0.602973718 0.244414125
#> [31] 0.317701108 -0.100471420 0.215023911 -0.120612734 0.613031142
#> [36] 0.434008041 0.381071336 -0.119240414 0.264409321 0.365858504
#> [41] -0.048917803 0.076310424 0.373905747 0.343756679 1.155361670
#> [46] 1.196682848 0.191268465 0.364717551 -0.094418824 0.329655947
#>
#> $r2
#> [1] 0.1813669
#>
#> $log_marginal
#> [1] -1696.74
#>
#> $length_scale
#> [1] 0.7740596
#>
#> $noise
#> [1] 0.09688279
#>
#> $n
#> [1] 50
#>
#> $method
#> [1] "GP regression posterior"
#>