R Under development (unstable) (2024-11-13 r87330 ucrt) -- "Unsuffered Consequences"
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> library("lpcde")
>
> set.seed(42)
> n=1000
> x_data = matrix(rnorm(1*n, mean=0, sd=1), ncol=1)
> y_data = matrix(rnorm(n, mean=0, sd=1))
> y_grid = stats::quantile(y_data, seq(from=0.1, to=0.9, by=0.1))
>
> #bw estimation
> model1 = lpbwcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=0, bw_type = "imse-rot")
> summary(model1)
Call: lpbwcde
Sample size 1000
Polynomial order for Y point estimation (p=) 2
Polynomial order for X point estimation (q=) 1
Density function estimated (mu=) 1
Order of derivative estimated for covariates (nu=) 0
Kernel function epanechnikov
Bandwidth method imse-rot
==================================
Index y_grid B.W. Eff.n
==================================
1 -1.2512 1.0910 305
2 -0.8452 1.0910 415
3 -0.5287 1.0910 477
4 -0.2550 1.0910 518
5 -0.0106 1.0910 528
----------------------------------
6 0.2397 1.0910 493
7 0.5039 1.0910 478
8 0.8026 1.0910 421
9 1.2896 1.0910 280
==================================
>
> # density estimation
> model2 = lpcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=0, bw=model1$BW[,2])
> summary(model2)
Call: lpcde
Sample size 1000
Polynomial order for Y point estimation (p=) 2
Polynomial order for X point estimation (q=) 1
Density function estimated (mu=) 1
Order of derivative estimated for covariates (nu=) 0
Kernel function epanechnikov
Bandwidth method
=============================================================================
Point Std. Robust B.C.
Index Grid B.W. Eff.n Est. Error [ 95% C.I. ]
=============================================================================
1 -1.2512 1.0910 304 0.1833 0.0049 0.1635 , 0.2081
2 -0.8452 1.0910 417 0.2823 0.0034 0.2788 , 0.3135
3 -0.5287 1.0910 481 0.3473 0.0029 0.3571 , 0.3866
4 -0.2550 1.0910 525 0.3731 0.0024 0.3804 , 0.4078
5 -0.0106 1.0910 532 0.3776 0.0022 0.3790 , 0.4046
-----------------------------------------------------------------------------
6 0.2397 1.0910 503 0.3660 0.0023 0.3665 , 0.3931
7 0.5039 1.0910 480 0.3290 0.0023 0.3372 , 0.3637
8 0.8026 1.0910 426 0.2737 0.0025 0.2945 , 0.3259
9 1.2896 1.0910 281 0.1761 0.0049 0.1515 , 0.1943
=============================================================================
>
> # non-negative and integrating to 1 density estimation
> model2 = lpcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=0, bw=model1$BW[,2], nonneg=TRUE, normalize=TRUE)
> summary(model2)
Call: lpcde
Sample size 1000
Polynomial order for Y point estimation (p=) 2
Polynomial order for X point estimation (q=) 1
Density function estimated (mu=) 1
Order of derivative estimated for covariates (nu=) 0
Kernel function epanechnikov
Bandwidth method
=============================================================================
Point Std. Robust B.C.
Index Grid B.W. Eff.n Est. Error [ 95% C.I. ]
=============================================================================
1 -1.2512 1.0910 304 0.2135 0.0049 0.1635 , 0.2081
2 -0.8452 1.0910 417 0.3288 0.0034 0.2788 , 0.3135
3 -0.5287 1.0910 481 0.4045 0.0029 0.3571 , 0.3866
4 -0.2550 1.0910 525 0.4345 0.0024 0.3804 , 0.4078
5 -0.0106 1.0910 532 0.4398 0.0022 0.3790 , 0.4046
-----------------------------------------------------------------------------
6 0.2397 1.0910 503 0.4264 0.0023 0.3665 , 0.3931
7 0.5039 1.0910 480 0.3832 0.0023 0.3372 , 0.3637
8 0.8026 1.0910 426 0.3188 0.0025 0.2945 , 0.3259
9 1.2896 1.0910 281 0.2051 0.0049 0.1515 , 0.1943
=============================================================================
>
> #bw estimation
> model1 = lpbwcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=0, bw_type = "mse-rot")
> summary(model1)
Call: lpbwcde
Sample size 1000
Polynomial order for Y point estimation (p=) 2
Polynomial order for X point estimation (q=) 1
Density function estimated (mu=) 1
Order of derivative estimated for covariates (nu=) 0
Kernel function epanechnikov
Bandwidth method mse-rot
==================================
Index y_grid B.W. Eff.n
==================================
1 -1.2512 1.5034 527
2 -0.8452 1.6325 705
3 -0.5287 1.1896 531
4 -0.2550 1.0767 512
5 -0.0106 1.0492 503
----------------------------------
6 0.2397 1.0761 486
7 0.5039 1.1816 538
8 0.8026 1.5485 675
9 1.2896 1.4176 469
==================================
>
> # density estimation
> model2 = lpcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=0, bw=model1$BW[,2])
> summary(model2)
Call: lpcde
Sample size 1000
Polynomial order for Y point estimation (p=) 2
Polynomial order for X point estimation (q=) 1
Density function estimated (mu=) 1
Order of derivative estimated for covariates (nu=) 0
Kernel function epanechnikov
Bandwidth method
=============================================================================
Point Std. Robust B.C.
Index Grid B.W. Eff.n Est. Error [ 95% C.I. ]
=============================================================================
1 -1.2512 1.5034 529 0.1913 0.0025 0.1749 , 0.1945
2 -0.8452 1.6325 716 0.2737 0.0016 0.3039 , 0.3165
3 -0.5287 1.1896 538 0.3429 0.0024 0.3608 , 0.3848
4 -0.2550 1.0767 520 0.3737 0.0025 0.3799 , 0.4079
5 -0.0106 1.0492 507 0.3792 0.0024 0.3756 , 0.4037
-----------------------------------------------------------------------------
6 0.2397 1.0761 493 0.3666 0.0023 0.3648 , 0.3924
7 0.5039 1.1816 537 0.3256 0.0018 0.3430 , 0.3642
8 0.8026 1.5485 678 0.2720 0.0011 0.2824 , 0.2928
9 1.2896 1.4176 467 0.1805 0.0020 0.1797 , 0.1980
=============================================================================
>
> set.seed(42)
> n=1000
> x_data = matrix(rnorm(2*n, mean=0, sd=1), ncol=2)
> y_data = matrix(rnorm(n, mean=0, sd=1))
> y_grid = stats::quantile(y_data, seq(from=0.1, to=0.9, by=0.1))
>
> #bw estimation
> model1 = lpbwcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=matrix(c(0, 0), ncol=2), bw_type="imse-rot")
> summary(model1)
Call: lpbwcde
Sample size 1000
Polynomial order for Y point estimation (p=) 2
Polynomial order for X point estimation (q=) 1
Density function estimated (mu=) 1
Order of derivative estimated for covariates (nu=) 0
Kernel function epanechnikov
Bandwidth method imse-rot
==================================
Index y_grid B.W. Eff.n
==================================
1 -1.3242 0.8299 104
2 -0.8334 0.8299 157
3 -0.5201 0.8299 190
4 -0.2590 0.8299 210
5 0.0114 0.8299 216
----------------------------------
6 0.2612 0.8299 209
7 0.5211 0.8299 194
8 0.8037 0.8299 169
9 1.3115 0.8299 106
==================================
>
> # density estimation
> model2 = lpcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=matrix(c(0, 0), ncol=2), bw=model1$BW[,2])
> summary(model2)
Call: lpcde
Sample size 1000
Polynomial order for Y point estimation (p=) 2
Polynomial order for X point estimation (q=) 1
Density function estimated (mu=) 1
Order of derivative estimated for covariates (nu=) 0
Kernel function epanechnikov
Bandwidth method
=============================================================================
Point Std. Robust B.C.
Index Grid B.W. Eff.n Est. Error [ 95% C.I. ]
=============================================================================
1 -1.3242 0.8299 102 0.1857 0.0256 0.0568 , 0.4152
2 -0.8334 0.8299 156 0.2866 0.0212 0.2032 , 0.5153
3 -0.5201 0.8299 186 0.3352 0.0164 0.2920 , 0.5400
4 -0.2590 0.8299 201 0.3682 0.0162 0.2625 , 0.5187
5 0.0114 0.8299 208 0.3777 0.0152 0.2393 , 0.4699
-----------------------------------------------------------------------------
6 0.2612 0.8299 204 0.3574 0.0139 0.2935 , 0.5156
7 0.5211 0.8299 187 0.3204 0.0149 0.2091 , 0.4130
8 0.8037 0.8299 167 0.2618 0.0157 0.0780 , 0.3297
9 1.3115 0.8299 106 0.1781 0.0305 -0.0049 , 0.3028
=============================================================================
>
> #bw estimation
> model1 = lpbwcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=matrix(c(0, 0), ncol=2), bw_type="mse-rot")
> summary(model1)
Call: lpbwcde
Sample size 1000
Polynomial order for Y point estimation (p=) 2
Polynomial order for X point estimation (q=) 1
Density function estimated (mu=) 1
Order of derivative estimated for covariates (nu=) 0
Kernel function epanechnikov
Bandwidth method mse-rot
==================================
Index y_grid B.W. Eff.n
==================================
1 -1.3242 0.4586 22
2 -0.8334 0.4491 37
3 -0.5201 0.4901 46
4 -0.2590 0.5991 87
5 0.0114 1.5432 668
----------------------------------
6 0.2612 0.5930 80
7 0.5211 0.4884 48
8 0.8037 0.4503 30
9 1.3115 0.4578 21
==================================
>
> # density estimation
> model2 = lpcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=matrix(c(0, 0), ncol=2), bw=model1$BW[,2])
> summary(model2)
Call: lpcde
Sample size 1000
Polynomial order for Y point estimation (p=) 2
Polynomial order for X point estimation (q=) 1
Density function estimated (mu=) 1
Order of derivative estimated for covariates (nu=) 0
Kernel function epanechnikov
Bandwidth method
=============================================================================
Point Std. Robust B.C.
Index Grid B.W. Eff.n Est. Error [ 95% C.I. ]
=============================================================================
1 -1.3242 0.4586 23 0.2533 0.6090 -2.7431 , 3.2656
2 -0.8334 0.4491 37 0.4750 0.5990 -5.3966 , 4.9616
3 -0.5201 0.4901 44 0.7018 0.2888 -2.2469 , 6.1616
4 -0.2590 0.5991 86 0.4353 0.0986 -0.3179 , 1.2875
5 0.0114 1.5432 663 0.3496 0.0014 0.3866 , 0.4038
-----------------------------------------------------------------------------
6 0.2612 0.5930 81 0.3687 0.0931 -0.3728 , 1.1874
7 0.5211 0.4884 48 0.2773 0.1024 -1.0636 , 1.4493
8 0.8037 0.4503 28 0.0813 0.2698 -1.7130 , 1.8954
9 1.3115 0.4578 19 0.0001 0.0017 -0.6811 , 0.7200
=============================================================================
>
> proc.time()
user system elapsed
242.56 6.21 249.01