有什么方法可以做出让empirical copula和真实的copula进行对比的图形啊？感谢您的赐教~

方法很多

你也可以安装 R package"copula"

   里面也有contour-methods

也可以使用matlab Patton_copula_toolbox

   里面有empiricalCDF,contour_plots

如果你一定要用s-plus

   就自己写一个 empiricalCDF function

方不方便我加您QQ，有些方面想请教您~

您好！请问gpd.ml函数在哪个程序包中？我现在遇到楼主一样的问题，但是我的数据应该适合GPD分布，我的运算过程如下：
> C
  [1] 3.315360 4.757943 4.157517 3.782731 3.497177 4.811970 4.102455 3.731899
  [9] 3.442007 4.628033 3.983903 3.816525 3.454336 4.652390 3.981934 3.587910
[17] 3.312573 4.676566 3.976223 3.570154 3.105115 4.545338 3.936504 3.629576
[25] 3.296124 4.477624 3.818021 3.408960 3.129856 4.457513 3.809019 3.387957
[33] 3.205075 4.505066 3.819157 3.482076 3.181455 4.530721 3.862444 3.489019
[41] 3.480265 5.218835 4.482865 4.012570 3.456527 4.652185 3.973127 3.569683
[49] 3.265626 4.529945 3.823249 3.397874 3.169428 4.566491 3.856223 3.489356
[57] 3.217635 4.600834 3.895416 3.510910 3.198806 4.831769 4.282207 3.777696
[65] 3.413453 4.585380 3.947783 3.554253 3.270865 4.522082 3.805820 3.346651
[73] 3.059685 4.320367 3.666295 3.289446 3.019385 4.406175 3.740336 3.373145
[81] 3.198242 4.911823 4.362288 3.943151 3.618115 4.848633 4.143348 3.764030
[89] 3.445338 4.675521 3.984265 3.573928 3.287748 4.589158 3.929644 3.550972
[97] 3.281446 4.677469 3.996971 3.604100 3.185847 4.788255 4.160568 3.763943
[105] 3.442712 4.657123 3.961336 3.576085 3.301508 4.689115 3.906024 3.473048
[113] 3.130764 4.302487 3.582810 3.246245 3.028595 4.395285 3.659999 3.291031
[121] 3.249206 4.854692 4.259089 3.871668 3.566314 4.909074 4.213613 3.828620
[129] 3.517667 4.811869 4.112048 3.723223 3.426417 4.688422 3.999342 3.609172
[137] 3.324153 4.714478 4.051038 3.669868 3.331002 4.988998 4.391037 3.969018
[145] 3.614645 4.938494 4.243859 3.852944 3.551171 4.865890 4.167319 3.748544
[153] 3.465652 4.802825 4.108264 3.711061 3.437046 4.824698 4.136743 3.737381
[161] 3.186387 4.793307 4.140911 3.767570 3.494028 4.812608 4.179852 3.780124
[169] 3.484855 4.782014 4.091565 3.705425 3.417458 4.762402 4.056331 3.659985
[177] 3.386382 4.793315 4.118548 3.717675 3.169020 4.727164 4.113192 3.738973
[185] 3.466193 4.848432 4.173382 3.771632 3.476768 4.737682 4.069155 3.682633
[193] 3.361679 4.715678 4.016952 3.609458 3.340481 4.718649 4.032944 3.657015
[201] 3.313888 4.941173 4.349685 3.951787 3.674656 5.033092 4.320938 3.943688
[209] 3.637203 4.951356 4.260577 3.839035 3.552612 4.914357 4.218586 3.839058
[217] 3.522565 4.916584 4.207290 3.806874 3.164978 4.943506 4.357010 3.874182
[225] 3.557427 4.822041 4.153810 3.716574 3.453615 4.664525 3.984816 3.530965
[233] 3.243873 4.552935 3.905133 3.534265 3.265903 4.656385 3.991647 3.565699
> b=sample(C,240,replace=T)
> b
  [1] 4.167319 3.674656 3.164978 4.909074 4.689115 3.313888 4.812608 3.738973
  [9] 3.554253 3.856223 3.981934 4.179852 3.782731 3.510910 3.417458 3.105115
[17] 3.331002 4.831769 3.530965 3.550972 4.812608 4.143348 3.466193 4.505066
[25] 3.489019 4.243859 3.717675 4.848432 3.130764 3.301508 3.723223 3.476768
[33] 4.012570 3.682633 3.249206 4.938494 3.454336 4.505066 3.771632 4.914357
[41] 4.727164 4.718649 3.397874 4.988998 4.793315 4.477624 3.819157 3.296124
[49] 3.552612 4.988998 4.656385 3.983903 3.482076 4.552935 3.442007 3.456527
[57] 3.442007 4.812608 3.737381 4.118548 3.999342 3.951787 3.839058 4.941173
[65] 3.609458 4.600834 3.313888 4.112048 3.657015 3.453615 3.961336 4.988998
[73] 4.566491 4.349685 3.246245 3.976223 4.909074 4.714478 4.689115 3.473048
[81] 3.806874 3.387957 3.905133 3.059685 3.740336 4.664525 3.748544 4.522082
[89] 3.943688 3.604100 4.652185 3.473048 4.153810 3.818021 3.614645 3.763943
[97] 4.589158 4.260577 3.604100 5.218835 3.819157 4.457513 4.938494 3.576085
[105] 3.186387 3.604100 3.780124 4.457513 3.782731 4.259089 4.357010 3.984265
[113] 3.874182 3.947783 4.811970 3.573928 3.999342 3.895416 4.914357 4.016952
[121] 3.169428 4.118548 4.737682 3.331002 4.320938 3.181455 3.618115 4.675521
[129] 3.984816 3.763943 4.108264 3.983903 4.032944 3.819157 3.951787 3.823249
[137] 3.217635 3.723223 3.480265 3.243873 3.129856 3.717675 3.534265 3.129856
[145] 3.716574 4.457513 4.320938 3.169020 3.397874 3.874182 4.793307 3.777696
[153] 3.296124 3.991647 3.552612 3.669868 3.473048 4.675521 3.373145 4.909074
[161] 3.324153 3.582810 3.816525 3.905133 3.185847 4.811869 3.805820 3.976223
[169] 3.281446 3.181455 3.874182 4.012570 3.981934 3.947783 4.988998 3.059685
[177] 4.822041 3.711061 3.346651 4.688422 3.426417 4.102455 4.718649 3.465652
[185] 3.169428 4.477624 3.587910 3.780124 3.973127 4.909074 3.281446 3.576085
[193] 4.302487 4.302487 4.689115 4.762402 3.473048 3.497177 3.494028 3.570154
[201] 3.816525 4.552935 3.315360 3.340481 4.320367 3.243873 3.198806 3.618115
[209] 3.408960 4.916584 3.943688 4.406175 4.051038 3.331002 3.205075 3.296124
[217] 3.614645 3.186387 4.302487 4.793307 4.911823 4.788255 3.340481 3.828620
[225] 4.320367 3.862444 3.856223 4.909074 4.302487 3.243873 3.669868 3.489356
[233] 4.118548 3.809019 3.205075 4.737682 3.999342 3.659985 3.806874 3.996971
> data1=b
> n1 <- length(data1)
> n1
[1] 240
> sorted.data1 <- sort(data1)
> sorted.data1
  [1] 3.059685 3.059685 3.105115 3.129856 3.129856 3.130764 3.164978 3.169020
  [9] 3.169428 3.169428 3.181455 3.181455 3.185847 3.186387 3.186387 3.198806
[17] 3.205075 3.205075 3.217635 3.243873 3.243873 3.243873 3.246245 3.249206
[25] 3.281446 3.281446 3.296124 3.296124 3.296124 3.301508 3.313888 3.313888
[33] 3.315360 3.324153 3.331002 3.331002 3.331002 3.340481 3.340481 3.346651
[41] 3.373145 3.387957 3.397874 3.397874 3.408960 3.417458 3.426417 3.442007
[49] 3.442007 3.453615 3.454336 3.456527 3.465652 3.466193 3.473048 3.473048
[57] 3.473048 3.473048 3.476768 3.480265 3.482076 3.489019 3.489356 3.494028
[65] 3.497177 3.510910 3.530965 3.534265 3.550972 3.552612 3.552612 3.554253
[73] 3.570154 3.573928 3.576085 3.576085 3.582810 3.587910 3.604100 3.604100
[81] 3.604100 3.609458 3.614645 3.614645 3.618115 3.618115 3.657015 3.659985
[89] 3.669868 3.669868 3.674656 3.682633 3.711061 3.716574 3.717675 3.717675
[97] 3.723223 3.723223 3.737381 3.738973 3.740336 3.748544 3.763943 3.763943
[105] 3.771632 3.777696 3.780124 3.780124 3.782731 3.782731 3.805820 3.806874
[113] 3.806874 3.809019 3.816525 3.816525 3.818021 3.819157 3.819157 3.819157
[121] 3.823249 3.828620 3.839058 3.856223 3.856223 3.862444 3.874182 3.874182
[129] 3.874182 3.895416 3.905133 3.905133 3.943688 3.943688 3.947783 3.947783
[137] 3.951787 3.951787 3.961336 3.973127 3.976223 3.976223 3.981934 3.981934
[145] 3.983903 3.983903 3.984265 3.984816 3.991647 3.996971 3.999342 3.999342
[153] 3.999342 4.012570 4.012570 4.016952 4.032944 4.051038 4.102455 4.108264
[161] 4.112048 4.118548 4.118548 4.118548 4.143348 4.153810 4.167319 4.179852
[169] 4.243859 4.259089 4.260577 4.302487 4.302487 4.302487 4.302487 4.320367
[177] 4.320367 4.320938 4.320938 4.349685 4.357010 4.406175 4.457513 4.457513
[185] 4.457513 4.477624 4.477624 4.505066 4.505066 4.522082 4.552935 4.552935
[193] 4.566491 4.589158 4.600834 4.652185 4.656385 4.664525 4.675521 4.675521
[201] 4.688422 4.689115 4.689115 4.689115 4.714478 4.718649 4.718649 4.727164
[209] 4.737682 4.737682 4.762402 4.788255 4.793307 4.793307 4.793315 4.811869
[217] 4.811970 4.812608 4.812608 4.812608 4.822041 4.831769 4.848432 4.909074
[225] 4.909074 4.909074 4.909074 4.909074 4.911823 4.914357 4.914357 4.916584
[233] 4.938494 4.938494 4.941173 4.988998 4.988998 4.988998 4.988998 5.218835
> uu11 <- sorted.data1[n1 - trunc(n1 * 0.24)]
> uu11
[1] 4.457513
> uu21 <- sorted.data[n1 - trunc(n1 * 0.24) - 1]
Problem: Object "sorted.data" not found
Use traceback() to see the call stack
>  ??sorted.data
> ?sorted.data
No documentation for topic "sorted.data"
> uu21 <- sorted.data1[n1 - trunc(n1 * 0.24) - 1]
> uu21
[1] 4.406175
> upper1 <- (uu11 + uu21)/2
> upper1
[1] 4.431844
> upper.exceedances1 <- data1[data1 > upper1]
> upper.exceedances1
[1] 4.909074 4.689115 4.812608 4.831769 4.812608 4.505066 4.848432 4.938494
[9] 4.505066 4.914357 4.727164 4.718649 4.988998 4.793315 4.477624 4.988998
[17] 4.656385 4.552935 4.812608 4.941173 4.600834 4.988998 4.566491 4.909074
[25] 4.714478 4.689115 4.664525 4.522082 4.652185 4.589158 5.218835 4.457513
[33] 4.938494 4.457513 4.811970 4.914357 4.737682 4.675521 4.457513 4.793307
[41] 4.675521 4.909074 4.811869 4.988998 4.822041 4.688422 4.718649 4.477624
[49] 4.909074 4.689115 4.762402 4.552935 4.916584 4.793307 4.911823 4.788255
[57] 4.909074 4.737682
> excess1 <- upper.exceedances1 - upper1
> excess1
[1] 0.47723016 0.25727090 0.38076349 0.39992462 0.38076349 0.07322230 0.41658790
[8] 0.50664953 0.07322230 0.48251265 0.29531970 0.28680465 0.55715397 0.36147109
[15] 0.04578021 0.55715397 0.22454120 0.12109074 0.38076349 0.50932924 0.16898966
[22] 0.55715397 0.13464670 0.47723016 0.28263374 0.25727090 0.23268098 0.09023806
[29] 0.22034069 0.15731411 0.78699057 0.02566939 0.50664953 0.02566939 0.38012549
[36] 0.48251265 0.30583755 0.24367717 0.02566939 0.36146272 0.24367717 0.47723016
[43] 0.38002455 0.55715397 0.39019712 0.25657759 0.28680465 0.04578021 0.47723016
[50] 0.25727090 0.33055762 0.12109074 0.48474029 0.36146272 0.47997912 0.35641067
[57] 0.47723016 0.30583755
> gpd.ml(sample = excess1, location = 0)
Problem: Couldn't find a function definition for "gpd.ml"
Use traceback() to see the call stac

您好！请问gpd.ml函数在哪个程序包中？我现在遇到楼主一样的问题，但是我的数据应该适合GPD分布，我的运算过程如下：
> C
  [1] 3.315360 4.757943 4.157517 3.782731 3.497177 4.811970 4.102455 3.731899
  [9] 3.442007 4.628033 3.983903 3.816525 3.454336 4.652390 3.981934 3.587910
[17] 3.312573 4.676566 3.976223 3.570154 3.105115 4.545338 3.936504 3.629576
[25] 3.296124 4.477624 3.818021 3.408960 3.129856 4.457513 3.809019 3.387957
[33] 3.205075 4.505066 3.819157 3.482076 3.181455 4.530721 3.862444 3.489019
[41] 3.480265 5.218835 4.482865 4.012570 3.456527 4.652185 3.973127 3.569683
[49] 3.265626 4.529945 3.823249 3.397874 3.169428 4.566491 3.856223 3.489356
[57] 3.217635 4.600834 3.895416 3.510910 3.198806 4.831769 4.282207 3.777696
[65] 3.413453 4.585380 3.947783 3.554253 3.270865 4.522082 3.805820 3.346651
[73] 3.059685 4.320367 3.666295 3.289446 3.019385 4.406175 3.740336 3.373145
[81] 3.198242 4.911823 4.362288 3.943151 3.618115 4.848633 4.143348 3.764030
[89] 3.445338 4.675521 3.984265 3.573928 3.287748 4.589158 3.929644 3.550972
[97] 3.281446 4.677469 3.996971 3.604100 3.185847 4.788255 4.160568 3.763943
[105] 3.442712 4.657123 3.961336 3.576085 3.301508 4.689115 3.906024 3.473048
[113] 3.130764 4.302487 3.582810 3.246245 3.028595 4.395285 3.659999 3.291031
[121] 3.249206 4.854692 4.259089 3.871668 3.566314 4.909074 4.213613 3.828620
[129] 3.517667 4.811869 4.112048 3.723223 3.426417 4.688422 3.999342 3.609172
[137] 3.324153 4.714478 4.051038 3.669868 3.331002 4.988998 4.391037 3.969018
[145] 3.614645 4.938494 4.243859 3.852944 3.551171 4.865890 4.167319 3.748544
[153] 3.465652 4.802825 4.108264 3.711061 3.437046 4.824698 4.136743 3.737381
[161] 3.186387 4.793307 4.140911 3.767570 3.494028 4.812608 4.179852 3.780124
[169] 3.484855 4.782014 4.091565 3.705425 3.417458 4.762402 4.056331 3.659985
[177] 3.386382 4.793315 4.118548 3.717675 3.169020 4.727164 4.113192 3.738973
[185] 3.466193 4.848432 4.173382 3.771632 3.476768 4.737682 4.069155 3.682633
[193] 3.361679 4.715678 4.016952 3.609458 3.340481 4.718649 4.032944 3.657015
[201] 3.313888 4.941173 4.349685 3.951787 3.674656 5.033092 4.320938 3.943688
[209] 3.637203 4.951356 4.260577 3.839035 3.552612 4.914357 4.218586 3.839058
[217] 3.522565 4.916584 4.207290 3.806874 3.164978 4.943506 4.357010 3.874182
[225] 3.557427 4.822041 4.153810 3.716574 3.453615 4.664525 3.984816 3.530965
[233] 3.243873 4.552935 3.905133 3.534265 3.265903 4.656385 3.991647 3.565699
> b=sample(C,240,replace=T)
> b
  [1] 4.167319 3.674656 3.164978 4.909074 4.689115 3.313888 4.812608 3.738973
  [9] 3.554253 3.856223 3.981934 4.179852 3.782731 3.510910 3.417458 3.105115
[17] 3.331002 4.831769 3.530965 3.550972 4.812608 4.143348 3.466193 4.505066
[25] 3.489019 4.243859 3.717675 4.848432 3.130764 3.301508 3.723223 3.476768
[33] 4.012570 3.682633 3.249206 4.938494 3.454336 4.505066 3.771632 4.914357
[41] 4.727164 4.718649 3.397874 4.988998 4.793315 4.477624 3.819157 3.296124
[49] 3.552612 4.988998 4.656385 3.983903 3.482076 4.552935 3.442007 3.456527
[57] 3.442007 4.812608 3.737381 4.118548 3.999342 3.951787 3.839058 4.941173
[65] 3.609458 4.600834 3.313888 4.112048 3.657015 3.453615 3.961336 4.988998
[73] 4.566491 4.349685 3.246245 3.976223 4.909074 4.714478 4.689115 3.473048
[81] 3.806874 3.387957 3.905133 3.059685 3.740336 4.664525 3.748544 4.522082
[89] 3.943688 3.604100 4.652185 3.473048 4.153810 3.818021 3.614645 3.763943
[97] 4.589158 4.260577 3.604100 5.218835 3.819157 4.457513 4.938494 3.576085
[105] 3.186387 3.604100 3.780124 4.457513 3.782731 4.259089 4.357010 3.984265
[113] 3.874182 3.947783 4.811970 3.573928 3.999342 3.895416 4.914357 4.016952
[121] 3.169428 4.118548 4.737682 3.331002 4.320938 3.181455 3.618115 4.675521
[129] 3.984816 3.763943 4.108264 3.983903 4.032944 3.819157 3.951787 3.823249
[137] 3.217635 3.723223 3.480265 3.243873 3.129856 3.717675 3.534265 3.129856
[145] 3.716574 4.457513 4.320938 3.169020 3.397874 3.874182 4.793307 3.777696
[153] 3.296124 3.991647 3.552612 3.669868 3.473048 4.675521 3.373145 4.909074
[161] 3.324153 3.582810 3.816525 3.905133 3.185847 4.811869 3.805820 3.976223
[169] 3.281446 3.181455 3.874182 4.012570 3.981934 3.947783 4.988998 3.059685
[177] 4.822041 3.711061 3.346651 4.688422 3.426417 4.102455 4.718649 3.465652
[185] 3.169428 4.477624 3.587910 3.780124 3.973127 4.909074 3.281446 3.576085
[193] 4.302487 4.302487 4.689115 4.762402 3.473048 3.497177 3.494028 3.570154
[201] 3.816525 4.552935 3.315360 3.340481 4.320367 3.243873 3.198806 3.618115
[209] 3.408960 4.916584 3.943688 4.406175 4.051038 3.331002 3.205075 3.296124
[217] 3.614645 3.186387 4.302487 4.793307 4.911823 4.788255 3.340481 3.828620
[225] 4.320367 3.862444 3.856223 4.909074 4.302487 3.243873 3.669868 3.489356
[233] 4.118548 3.809019 3.205075 4.737682 3.999342 3.659985 3.806874 3.996971
> data1=b
> n1 <- length(data1)
> n1
[1] 240
> sorted.data1 <- sort(data1)
> sorted.data1
  [1] 3.059685 3.059685 3.105115 3.129856 3.129856 3.130764 3.164978 3.169020
  [9] 3.169428 3.169428 3.181455 3.181455 3.185847 3.186387 3.186387 3.198806
[17] 3.205075 3.205075 3.217635 3.243873 3.243873 3.243873 3.246245 3.249206
[25] 3.281446 3.281446 3.296124 3.296124 3.296124 3.301508 3.313888 3.313888
[33] 3.315360 3.324153 3.331002 3.331002 3.331002 3.340481 3.340481 3.346651
[41] 3.373145 3.387957 3.397874 3.397874 3.408960 3.417458 3.426417 3.442007
[49] 3.442007 3.453615 3.454336 3.456527 3.465652 3.466193 3.473048 3.473048
[57] 3.473048 3.473048 3.476768 3.480265 3.482076 3.489019 3.489356 3.494028
[65] 3.497177 3.510910 3.530965 3.534265 3.550972 3.552612 3.552612 3.554253
[73] 3.570154 3.573928 3.576085 3.576085 3.582810 3.587910 3.604100 3.604100
[81] 3.604100 3.609458 3.614645 3.614645 3.618115 3.618115 3.657015 3.659985
[89] 3.669868 3.669868 3.674656 3.682633 3.711061 3.716574 3.717675 3.717675
[97] 3.723223 3.723223 3.737381 3.738973 3.740336 3.748544 3.763943 3.763943
[105] 3.771632 3.777696 3.780124 3.780124 3.782731 3.782731 3.805820 3.806874
[113] 3.806874 3.809019 3.816525 3.816525 3.818021 3.819157 3.819157 3.819157
[121] 3.823249 3.828620 3.839058 3.856223 3.856223 3.862444 3.874182 3.874182
[129] 3.874182 3.895416 3.905133 3.905133 3.943688 3.943688 3.947783 3.947783
[137] 3.951787 3.951787 3.961336 3.973127 3.976223 3.976223 3.981934 3.981934
[145] 3.983903 3.983903 3.984265 3.984816 3.991647 3.996971 3.999342 3.999342
[153] 3.999342 4.012570 4.012570 4.016952 4.032944 4.051038 4.102455 4.108264
[161] 4.112048 4.118548 4.118548 4.118548 4.143348 4.153810 4.167319 4.179852
[169] 4.243859 4.259089 4.260577 4.302487 4.302487 4.302487 4.302487 4.320367
[177] 4.320367 4.320938 4.320938 4.349685 4.357010 4.406175 4.457513 4.457513
[185] 4.457513 4.477624 4.477624 4.505066 4.505066 4.522082 4.552935 4.552935
[193] 4.566491 4.589158 4.600834 4.652185 4.656385 4.664525 4.675521 4.675521
[201] 4.688422 4.689115 4.689115 4.689115 4.714478 4.718649 4.718649 4.727164
[209] 4.737682 4.737682 4.762402 4.788255 4.793307 4.793307 4.793315 4.811869
[217] 4.811970 4.812608 4.812608 4.812608 4.822041 4.831769 4.848432 4.909074
[225] 4.909074 4.909074 4.909074 4.909074 4.911823 4.914357 4.914357 4.916584
[233] 4.938494 4.938494 4.941173 4.988998 4.988998 4.988998 4.988998 5.218835
> uu11 <- sorted.data1[n1 - trunc(n1 * 0.24)]
> uu11
[1] 4.457513
> uu21 <- sorted.data[n1 - trunc(n1 * 0.24) - 1]
Problem: Object "sorted.data" not found
Use traceback() to see the call stack
>  ??sorted.data
> ?sorted.data
No documentation for topic "sorted.data"
> uu21 <- sorted.data1[n1 - trunc(n1 * 0.24) - 1]
> uu21
[1] 4.406175
> upper1 <- (uu11 + uu21)/2
> upper1
[1] 4.431844
> upper.exceedances1 <- data1[data1 > upper1]
> upper.exceedances1
[1] 4.909074 4.689115 4.812608 4.831769 4.812608 4.505066 4.848432 4.938494
[9] 4.505066 4.914357 4.727164 4.718649 4.988998 4.793315 4.477624 4.988998
[17] 4.656385 4.552935 4.812608 4.941173 4.600834 4.988998 4.566491 4.909074
[25] 4.714478 4.689115 4.664525 4.522082 4.652185 4.589158 5.218835 4.457513
[33] 4.938494 4.457513 4.811970 4.914357 4.737682 4.675521 4.457513 4.793307
[41] 4.675521 4.909074 4.811869 4.988998 4.822041 4.688422 4.718649 4.477624
[49] 4.909074 4.689115 4.762402 4.552935 4.916584 4.793307 4.911823 4.788255
[57] 4.909074 4.737682
> excess1 <- upper.exceedances1 - upper1
> excess1
[1] 0.47723016 0.25727090 0.38076349 0.39992462 0.38076349 0.07322230 0.41658790
[8] 0.50664953 0.07322230 0.48251265 0.29531970 0.28680465 0.55715397 0.36147109
[15] 0.04578021 0.55715397 0.22454120 0.12109074 0.38076349 0.50932924 0.16898966
[22] 0.55715397 0.13464670 0.47723016 0.28263374 0.25727090 0.23268098 0.09023806
[29] 0.22034069 0.15731411 0.78699057 0.02566939 0.50664953 0.02566939 0.38012549
[36] 0.48251265 0.30583755 0.24367717 0.02566939 0.36146272 0.24367717 0.47723016
[43] 0.38002455 0.55715397 0.39019712 0.25657759 0.28680465 0.04578021 0.47723016
[50] 0.25727090 0.33055762 0.12109074 0.48474029 0.36146272 0.47997912 0.35641067
[57] 0.47723016 0.30583755
> gpd.ml(sample = excess1, location = 0)
Problem: Couldn't find a function definition for "gpd.ml"
Use traceback() to see the call stac

我知道gpd.ml函数在FinMetrics包中了。结果如下：
> gpd.ml(sample = excess1, location = 0)
Warning messages:
  Maximum Likelihood Method for the GPD did not converge in: gpd.ml(sample =
        excess1, location = 0)
$n:
[1] 58

$data:
[1] 0.47723016 0.25727090 0.38076349 0.39992462 0.38076349 0.07322230 0.41658790
[8] 0.50664953 0.07322230 0.48251265 0.29531970 0.28680465 0.55715397 0.36147109
[15] 0.04578021 0.55715397 0.22454120 0.12109074 0.38076349 0.50932924 0.16898966
[22] 0.55715397 0.13464670 0.47723016 0.28263374 0.25727090 0.23268098 0.09023806
[29] 0.22034069 0.15731411 0.78699057 0.02566939 0.50664953 0.02566939 0.38012549
[36] 0.48251265 0.30583755 0.24367717 0.02566939 0.36146272 0.24367717 0.47723016
[43] 0.38002455 0.55715397 0.39019712 0.25657759 0.28680465 0.04578021 0.47723016
[50] 0.25727090 0.33055762 0.12109074 0.48474029 0.36146272 0.47997912 0.35641067
[57] 0.47723016 0.30583755

$par.ests:
     beta mu        xi
0.757966  0 -1.338459

$convergence:
[1] F

$nllh.final:
[1] NA
但存在警告，这个结果说明什么？数据不适合GPD分布？但是当我选取门限值为3.243873时，用KS检验，P值为0.5。这说明拟合效果好啊！