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
morie_ghosal_np_regression(x = rnorm(50), y = rnorm(50))
#> $estimate
#> [1] 0.1194677
#>
#> $se
#> [1] 0.03344252
#>
#> $mu
#> [1] 0.08373154 0.08966508 0.36074030 0.57761326 -0.29096326 0.09533367
#> [7] -0.24096685 -0.10992229 0.53151951 -0.17294515 0.63162629 -0.18923964
#> [13] 0.59815734 0.59617612 0.56395345 0.15891160 -0.28875131 0.26149126
#> [19] 0.13327751 0.47973769 0.70210765 0.41180470 -0.26846434 0.04604136
#> [25] -0.28724505 -0.05878704 0.02408455 0.33846717 -0.27354209 0.62454471
#> [31] 0.01117130 0.56305800 0.08683965 -0.19106926 0.07572124 -0.17100635
#> [37] -0.27415450 -0.17221035 -0.05756124 -0.11494179 0.07795698 0.55534665
#> [43] 0.69745549 -0.27737399 -0.23126497 0.10471086 -0.28878024 0.59498421
#> [49] 0.14761351 -0.29126650
#>
#> $sd
#> [1] 0.03937807 0.02768082 0.03110638 0.03021408 0.02610666 0.03362807
#> [7] 0.03534697 0.04909508 0.03242036 0.06036015 0.03276423 0.02359676
#> [13] 0.03026739 0.03026105 0.09121340 0.05507816 0.02567968 0.03153874
#> [19] 0.03276869 0.03027196 0.03115270 0.03054060 0.02417509 0.02700401
#> [25] 0.02546416 0.04438563 0.04550035 0.03097488 0.03182068 0.03037915
#> [31] 0.04471818 0.03257282 0.04013827 0.02358408 0.03591848 0.02375283
#> [37] 0.03173889 0.02374104 0.02533690 0.02445482 0.03766427 0.03018519
#> [43] 0.03261762 0.02460404 0.02351401 0.02790784 0.02568426 0.03268936
#> [49] 0.03258030 0.02854891
#>
#> $ci_lower
#> [1] -0.12708783 -0.11388205 0.15530162 0.37268680 -0.49370981 -0.11162564
#> [7] -0.44902362 -0.32843409 0.32530185 -0.40203940 0.42519995 -0.39080085
#> [13] 0.39320066 0.39122304 0.29853008 -0.06501201 -0.49128827 0.05579950
#> [19] -0.07315155 0.27477842 0.49664201 0.20669239 -0.47028867 -0.15715620
#> [25] -0.48967745 -0.27339405 -0.19141739 0.13310483 -0.47940060 0.41952452
#> [31] -0.20370076 0.35674807 -0.12452974 -0.39262478 -0.13271121 -0.37263798
#> [37] -0.47996450 -0.37383664 -0.25993228 -0.31689553 -0.13165593 0.35043655
#> [43] 0.49111837 -0.47939736 -0.43278903 -0.09895532 -0.49131944 0.38860348
#> [49] -0.05870095 -0.49527374
#>
#> $ci_upper
#> [1] 0.29455090 0.29321221 0.56617898 0.78253972 -0.08821671 0.30229297
#> [7] -0.03291008 0.10858951 0.73773716 0.05614911 0.83805262 0.01232157
#> [13] 0.80311402 0.80112920 0.82937681 0.38283521 -0.08621435 0.46718303
#> [19] 0.33970656 0.68469696 0.90757329 0.61691702 -0.06664002 0.24923893
#> [25] -0.08481265 0.15581996 0.23958649 0.54382951 -0.06768358 0.82956490
#> [31] 0.22604335 0.76936793 0.29820904 0.01048625 0.28415368 0.03062527
#> [37] -0.06834449 0.02941593 0.14480981 0.08701196 0.28756990 0.76025676
#> [43] 0.90379260 -0.07535063 -0.02974091 0.30837704 -0.08624105 0.80136494
#> [49] 0.35392798 -0.08725927
#>
#> $r2
#> [1] 0.1257658
#>
#> $log_marginal
#> [1] -1880.633
#>
#> $length_scale
#> [1] 0.9079563
#>
#> $noise
#> [1] 0.1000935
#>
#> $n
#> [1] 50
#>
#> $method
#> [1] "GP regression posterior"
#>