Revisions to Getting different predicted values at a nonparametric regression at R

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edited May 29, 2020 at 1:05

kjetil b halvorsen ♦

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r nonparametric l

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edited May 27, 2020 at 14:19

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> predict(model_lokmodel_np,data_test$x)
$x
  [1]    0.1570512   93.5554103   36.4963909   -3.5661127   34.7541054  -62.9400893   12.0450577  -35.6331874  -39.8288668   55.4826490 -184.9628427
 [12]   18.4531852   24.0251748 
 [14]  -17.2227224   72.0898450   77.3400270   11.4445439  -39.8399136   18.5753251  -91.0200934  -53.7306953    7.8290271
  [23]  -81.2767392   86.2556579  -14.7859426   25.3677870 
 [27]  -32.6501861   99.8278848   34.9829942   73.0140475  -16.6109127  -32.7592336   74.8537874
  [34]  -37.8227589   12.7268935   36.3066722 -104.5535744   82.2742695  -16.2766760 
 [40]  -14.8270656  -69.9284468  -18.6364153   20.2791386  -48.9278429
  [45]   71.9757185   21.1260822  103.4247543   42.6072554   17.7123515   14.6981172  -16.6135251  -60.3311966 
 [53]   60.0246973  -25.8563621    9.8286898
  [56]  -10.2896468  -64.5899488   74.4190914  -20.2832034  -41.4624966   22.6792451   62.3720212   43.0183778  -19.2322428 -103.9732312 
 [66]    8.6415758
  [67]  -77.8111614   12.0402859    7.7602400  -29.4432033  169.3314805   20.2505791   11.6731976   -1.3726103  -24.2041204  -48.8151629   28.1533661
  [78]   32.9620623 
 [79]    6.7930378  -41.4405364   61.2589756  -38.8352966   31.5222920  102.4265876   -8.3005717    3.6229150  -17.2961421 -128.8072678
  [89] -113.0477625   25.2152322  -18.7119726 
 [92] -169.8448571  -67.2163913   41.2838093  -39.2234895  -43.5948520   15.8241092  113.0589380   57.0302050
[100] -159.7652908

$y
  [1]  0.2626652  1.0362184 36009101 0.2094705 32846545 0.2748579 68153382 0.1950373 38608090 0.4859794 66311088 0.2287054 29850071 0.3314901 29482348 0.3854881 39911713 0.3650532 37939114 10.9136908 69109190 0.2067608 20305681 0.1952171
 [14] 33928391 0.3094527 47218019 0.4440854 43021110 0.5514184 51132988 0.230908745387648
 [17] 0.3856282 29602706 0.2063207 37935451 0.6261288 34154393 0.4965176 31747282 0.2426026 34292331 0.5106526 30097045 0.8121246 25197283 0.3204007 37870979 0.1926424
 [27] 42909551 0.3209612 50262694 10.1667891 41085661 0.1968960 28147848 0.4591294 66571168 0.3043276 50020514 0.3207863 43004120 0.494347141047623
 [33] 0.2997292 47934204 0.2263014 38850477 0.207932629338361 -0.4634532 67971227 0.6883727 47394636 0.3070574
 [40] 40898355 0.3060559 42994694 0.5260798 42913529 0.3100390 26796969 0.2012345 43009166 0.4686115 37838300 0.4423612 35720021 0.1996103 51272056 10.2168268 39929755 0.2730666 25286335 0.209606272536260
 [49] 0.2199320 32675933 0.3089271 29689589 0.5184803
 [53] 43004358 0.3813810 31165451 0.3121229 65366386 0.2365161 42507672 0.2944705 29829339 0.5235469 42021518 0.4854362 29158235 0.3001235 48410922 0.4050712 42917560 0.1974770 37334202 0.3866054 43945155 0.2773536 63039245 0.3101219 72679567 0.374761142969163
 [66] [65] 0.2402602 46593919 0.5215129 29918173 0.2287226 24064245 0.2427921 29483343 0.3237291 30118668 0.9836179 42028865 0.2013009 06706009 0.2300669 37770963 0.2681752 29558649 0.3179349 37134149 0.4678521 42644011 0.1877380 35747902 0.1846270
 [79] 56081171 0.2453106 64072744 0.4048227 30540376 0.384354637341155
 [81] 0.3362410 64169987 0.1819359 38367882 10.2044661 61990595 0.2748352 26089882 0.2530661 41260902 0.309500933069992 -0.0574095 43023107 0.2669386 72733479 0.1929319 58602504 0.3100428
 [92] 49925112 0.5893979 43004441 0.5251369 40246554 0.2587267 28114869 0.3776657 71925867 0.4273564 38195198 0.216313536833077
 [97] 10.289269230465860 0.18813435 0.371281167963988 -0.476875255540036

   > predict(model_lokmodel_np,0.1570512)
$x
[1] 0.1570512

$y
[1] 0.2626652

> predict(model_lok,-62.9400893)
$x
[1] -62.94009

$y
[1] 0.5221146

For x=0.1570512 , I get the same estimate as the previous one : y=0.2626652

However,36009101 for the 6th x value of the first vector x=-62.9400893, I got the estimate y=0.4859794 at the first tableone.

But atfor the second one(prediction just with one input) , I get y= 0.52211462626652. They are different from the first resulteach other.

> predict(model_lok,data_test$x)
$x
  [1]    0.1570512   93.5554103   36.4963909   -3.5661127   34.7541054  -62.9400893   12.0450577  -35.6331874  -39.8288668   55.4826490 -184.9628427
 [12]   18.4531852   24.0251748  -17.2227224   72.0898450   77.3400270   11.4445439  -39.8399136   18.5753251  -91.0200934  -53.7306953    7.8290271
  [23]  -81.2767392   86.2556579  -14.7859426   25.3677870  -32.6501861   99.8278848   34.9829942   73.0140475  -16.6109127  -32.7592336   74.8537874
  [34]  -37.8227589   12.7268935   36.3066722 -104.5535744   82.2742695  -16.2766760  -14.8270656  -69.9284468  -18.6364153   20.2791386  -48.9278429
  [45]   71.9757185   21.1260822  103.4247543   42.6072554   17.7123515   14.6981172  -16.6135251  -60.3311966   60.0246973  -25.8563621    9.8286898
  [56]  -10.2896468  -64.5899488   74.4190914  -20.2832034  -41.4624966   22.6792451   62.3720212   43.0183778  -19.2322428 -103.9732312    8.6415758
  [67]  -77.8111614   12.0402859    7.7602400  -29.4432033  169.3314805   20.2505791   11.6731976   -1.3726103  -24.2041204  -48.8151629   28.1533661
  [78]   32.9620623    6.7930378  -41.4405364   61.2589756  -38.8352966   31.5222920  102.4265876   -8.3005717    3.6229150  -17.2961421 -128.8072678
  [89] -113.0477625   25.2152322  -18.7119726 -169.8448571  -67.2163913   41.2838093  -39.2234895  -43.5948520   15.8241092  113.0589380   57.0302050
[100] -159.7652908

$y
  [1]  0.2626652  1.0362184  0.2094705  0.2748579  0.1950373  0.4859794  0.2287054  0.3314901  0.3854881  0.3650532  1.9136908  0.2067608  0.1952171
 [14]  0.3094527  0.4440854  0.5514184  0.2309087  0.3856282  0.2063207  0.6261288  0.4965176  0.2426026  0.5106526  0.8121246  0.3204007  0.1926424
 [27]  0.3209612  1.1667891  0.1968960  0.4591294  0.3043276  0.3207863  0.4943471  0.2997292  0.2263014  0.2079326 -0.4634532  0.6883727  0.3070574
 [40]  0.3060559  0.5260798  0.3100390  0.2012345  0.4686115  0.4423612  0.1996103  1.2168268  0.2730666  0.2096062  0.2199320  0.3089271  0.5184803
 [53]  0.3813810  0.3121229  0.2365161  0.2944705  0.5235469  0.4854362  0.3001235  0.4050712  0.1974770  0.3866054  0.2773536  0.3101219  0.3747611
 [66]  0.2402602  0.5215129  0.2287226  0.2427921  0.3237291  0.9836179  0.2013009  0.2300669  0.2681752  0.3179349  0.4678521  0.1877380  0.1846270
 [79]  0.2453106  0.4048227  0.3843546  0.3362410  0.1819359  1.2044661  0.2748352  0.2530661  0.3095009 -0.0574095  0.2669386  0.1929319  0.3100428
 [92]  0.5893979  0.5251369  0.2587267  0.3776657  0.4273564  0.2163135  1.2892692  0.3712811 -0.4768752

> predict(model_lok,0.1570512)
$x
[1] 0.1570512

$y
[1] 0.2626652

> predict(model_lok,-62.9400893)
$x
[1] -62.94009

$y
[1] 0.5221146

For x=0.1570512 , I get the same estimate as the previous one : y=0.2626652

However, for the 6th x value of the first vector x=-62.9400893, I got the estimate y=0.4859794 at the first table.

But at the second one(prediction just with one input), I get 0.5221146 different from the first result.

> predict(model_np,data_test$x)
$x
  [1]    0.1570512   93.5554103   36.4963909   -3.5661127   34.7541054  -62.9400893   12.0450577  -35.6331874  -39.8288668   55.4826490 -184.9628427   18.4531852   24.0251748 
 [14]  -17.2227224   72.0898450   77.3400270   11.4445439  -39.8399136   18.5753251  -91.0200934  -53.7306953    7.8290271  -81.2767392   86.2556579  -14.7859426   25.3677870 
 [27]  -32.6501861   99.8278848   34.9829942   73.0140475  -16.6109127  -32.7592336   74.8537874  -37.8227589   12.7268935   36.3066722 -104.5535744   82.2742695  -16.2766760 
 [40]  -14.8270656  -69.9284468  -18.6364153   20.2791386  -48.9278429   71.9757185   21.1260822  103.4247543   42.6072554   17.7123515   14.6981172  -16.6135251  -60.3311966 
 [53]   60.0246973  -25.8563621    9.8286898  -10.2896468  -64.5899488   74.4190914  -20.2832034  -41.4624966   22.6792451   62.3720212   43.0183778  -19.2322428 -103.9732312 
 [66]    8.6415758  -77.8111614   12.0402859    7.7602400  -29.4432033  169.3314805   20.2505791   11.6731976   -1.3726103  -24.2041204  -48.8151629   28.1533661   32.9620623 
 [79]    6.7930378  -41.4405364   61.2589756  -38.8352966   31.5222920  102.4265876   -8.3005717    3.6229150  -17.2961421 -128.8072678 -113.0477625   25.2152322  -18.7119726 
 [92] -169.8448571  -67.2163913   41.2838093  -39.2234895  -43.5948520   15.8241092  113.0589380   57.0302050 -159.7652908

$y
  [1] 0.36009101 0.32846545 0.68153382 0.38608090 0.66311088 0.29850071 0.29482348 0.39911713 0.37939114 0.69109190 0.20305681 0.33928391 0.47218019 0.43021110 0.51132988 0.45387648
 [17] 0.29602706 0.37935451 0.34154393 0.31747282 0.34292331 0.30097045 0.25197283 0.37870979 0.42909551 0.50262694 0.41085661 0.28147848 0.66571168 0.50020514 0.43004120 0.41047623
 [33] 0.47934204 0.38850477 0.29338361 0.67971227 0.47394636 0.40898355 0.42994694 0.42913529 0.26796969 0.43009166 0.37838300 0.35720021 0.51272056 0.39929755 0.25286335 0.72536260
 [49] 0.32675933 0.29689589 0.43004358 0.31165451 0.65366386 0.42507672 0.29829339 0.42021518 0.29158235 0.48410922 0.42917560 0.37334202 0.43945155 0.63039245 0.72679567 0.42969163
 [65] 0.46593919 0.29918173 0.24064245 0.29483343 0.30118668 0.42028865 0.06706009 0.37770963 0.29558649 0.37134149 0.42644011 0.35747902 0.56081171 0.64072744 0.30540376 0.37341155
 [81] 0.64169987 0.38367882 0.61990595 0.26089882 0.41260902 0.33069992 0.43023107 0.72733479 0.58602504 0.49925112 0.43004441 0.40246554 0.28114869 0.71925867 0.38195198 0.36833077
 [97] 0.30465860 0.18813435 0.67963988 0.55540036

   > predict(model_np,0.1570512)
$x
[1] 0.1570512

$y
[1] 0.2626652

For x=0.1570512 , I get estimate y=0.36009101 for the first one. But for the second one , I get y= 0.2626652. They are different from each other.

added 11 characters in body

Source Link

edited May 27, 2020 at 13:51

oercim

699
9
19

data_test<-structure(list(y = c(0.875261480116371, 0.13319865469368, 0.00127171595390059, 
                     0.120784784396633, 0.396145484602405, 0.0145083415906443, 0.126972404606687, 
                     0.158633863426125, 0.307832433458906, 3.42112531824949, 0.0340520045576305, 
                     0.0577209021937775, 0.0296622168402153, 0.519694575795073, 0.598147976985479, 
                     0.0130977584533862, 0.158721871619698, 0.0345042704092106, 0.828465740122911, 
                     0.288698762072103, 0.00612936654816416, 0.660590833311049, 0.744003852280185, 
                     0.0218624098840227, 0.0643524619734905, 0.106603465426499, 0.996560657600869, 
                     0.122380988335843, 0.533105113544206, 0.0275922420213573, 0.107316738492487, 
                     0.560308948944375, 0.143056108849691, 0.0161973817777236, 0.131817444834554, 
                     1.0931449919045, 0.676905541419034, 0.0264930182311347, 0.0219841874288544, 
                     0.488998767576592, 0.0347315975270806, 0.0411243463454958, 0.239393381406891, 
                     0.518050404977722, 0.0446311348629937, 1.06966798067092, 0.181537821115414, 
                     0.0313727396250001, 0.0216034647860423, 0.0276009215712176, 0.363985328271051, 
                     0.360296428331267, 0.0668551459074575, 0.0096603143441187, 0.0105876830440506, 
                     0.417186148227153, 0.553820116962763, 0.0411408340216935, 0.171913862343572, 
                     0.0514348158606772, 0.389026902873765, 0.185058082651845, 0.0369879163899647, 
                     1.08104328029981, 0.00746768318488792, 0.605457684587019, 0.0144968484938537, 
                     0.00602213252556212, 0.0866902221106011, 2.86731502732902, 0.0410085953665955, 
                     0.013626354236944, 0.000188405910625031, 0.0585839444657251, 
                     0.238292013239626, 0.0792612023923057, 0.108649755268567, 0.00461453622722967, 
                     0.171731805762336, 0.375266208662383, 0.15081802592681, 0.0993654895544528, 
                     1.04912058404724, 0.00688994906597782, 0.00131255129276553, 0.0299156532828988, 
                     1.65913122506183, 1.2779796600082, 0.0635807935616347, 0.0350137919082677, 
                     2.88472754915262, 0.451804325441365, 0.170435290928851, 0.153848212504272, 
                     0.190051111767901, 0.0250402432610551, 1.27823234596823, 0.325244428486599, 
                     2.55249481518541, 0.652738453178641), x = c(0.157051206555181, 
                                                                 93.5554103254521, 36.4963908754934, -3.56611266493445, 34.7541054260692, 
                                                                 -62.9400893391807, 12.0450577377795, -35.6331874250237, -39.828866846312, 
                                                                 55.4826489507221, -184.96284270765, 18.45318524202, 24.0251747535325, 
                                                                 -17.2227224445543, 72.0898450404128, 77.3400269579394, 11.4445438761823, 
                                                                 -39.8399136067962, 18.5753251409526, -91.0200933927729, -53.7306953307048, 
                                                                 7.82902710952271, -81.2767391884694, 86.2556579176221, -14.7859426091212, 
                                                                 25.3677870484381, -32.6501861291018, 99.8278847617673, 34.9829942023039, 
                                                                 73.0140475212959, -16.6109126845448, -32.7592335826843, 74.8537874088129, 
                                                                 -37.8227588694546, 12.7268934849489, 36.3066722290206, -104.553574396311, 
                                                                 82.2742694540057, -16.276676021576, -14.8270655993876, -69.9284468279249, 
                                                                 -18.6364153009855, 20.2791386270462, -48.9278429329243, 71.9757184735048, 
                                                                 21.1260821883741, 103.424754322692, 42.6072553816148, 17.7123515166677, 
                                                                 14.698117153582, -16.6135250838639, -60.331196596044, 60.0246972779761, 
                                                                 -25.8563620618713, 9.82868981305174, -10.2896467597535, -64.5899487712409, 
                                                                 74.419091432425, -20.2832034012612, -41.4624965895172, 22.6792451066338, 
                                                                 62.3720212013179, 43.0183777764626, -19.2322428203173, -103.973231184753, 
                                                                 8.64157577348479, -77.8111614478938, 12.0402859159796, 7.7602400256449, 
                                                                 -29.4432033091852, 169.331480455615, 20.2505790945828, 11.6731976068873, 
                                                                 -1.37261032571168, -24.2041204066012, -48.8151629352629, 28.1533661206446, 
                                                                 32.9620623245219, 6.79303777939566, -41.4405364060766, 61.2589755596993, 
                                                                 -38.8352965646988, 31.5222920414193, 102.426587566278, -8.30057170680298, 
                                                                 3.62291497659761, -17.2961421371642, -128.80726784859, -113.047762472691, 
                                                                 25.2152322142063, -18.7119726133478, -169.84485712416, -67.2163912629475, 
                                                                 41.2838092875223, -39.2234894552068, -43.5948519630358, 15.8241092201283, 
                                                                 113.058937991131, 57.0302050221283, -159.765290823302)), class = "data.frame", row.names = c(NA, 
                                                                                                                                                              -100L))

structure(list(y = c(0.875261480116371, 0.13319865469368, 0.00127171595390059, 
                     0.120784784396633, 0.396145484602405, 0.0145083415906443, 0.126972404606687, 
                     0.158633863426125, 0.307832433458906, 3.42112531824949, 0.0340520045576305, 
                     0.0577209021937775, 0.0296622168402153, 0.519694575795073, 0.598147976985479, 
                     0.0130977584533862, 0.158721871619698, 0.0345042704092106, 0.828465740122911, 
                     0.288698762072103, 0.00612936654816416, 0.660590833311049, 0.744003852280185, 
                     0.0218624098840227, 0.0643524619734905, 0.106603465426499, 0.996560657600869, 
                     0.122380988335843, 0.533105113544206, 0.0275922420213573, 0.107316738492487, 
                     0.560308948944375, 0.143056108849691, 0.0161973817777236, 0.131817444834554, 
                     1.0931449919045, 0.676905541419034, 0.0264930182311347, 0.0219841874288544, 
                     0.488998767576592, 0.0347315975270806, 0.0411243463454958, 0.239393381406891, 
                     0.518050404977722, 0.0446311348629937, 1.06966798067092, 0.181537821115414, 
                     0.0313727396250001, 0.0216034647860423, 0.0276009215712176, 0.363985328271051, 
                     0.360296428331267, 0.0668551459074575, 0.0096603143441187, 0.0105876830440506, 
                     0.417186148227153, 0.553820116962763, 0.0411408340216935, 0.171913862343572, 
                     0.0514348158606772, 0.389026902873765, 0.185058082651845, 0.0369879163899647, 
                     1.08104328029981, 0.00746768318488792, 0.605457684587019, 0.0144968484938537, 
                     0.00602213252556212, 0.0866902221106011, 2.86731502732902, 0.0410085953665955, 
                     0.013626354236944, 0.000188405910625031, 0.0585839444657251, 
                     0.238292013239626, 0.0792612023923057, 0.108649755268567, 0.00461453622722967, 
                     0.171731805762336, 0.375266208662383, 0.15081802592681, 0.0993654895544528, 
                     1.04912058404724, 0.00688994906597782, 0.00131255129276553, 0.0299156532828988, 
                     1.65913122506183, 1.2779796600082, 0.0635807935616347, 0.0350137919082677, 
                     2.88472754915262, 0.451804325441365, 0.170435290928851, 0.153848212504272, 
                     0.190051111767901, 0.0250402432610551, 1.27823234596823, 0.325244428486599, 
                     2.55249481518541, 0.652738453178641), x = c(0.157051206555181, 
                                                                 93.5554103254521, 36.4963908754934, -3.56611266493445, 34.7541054260692, 
                                                                 -62.9400893391807, 12.0450577377795, -35.6331874250237, -39.828866846312, 
                                                                 55.4826489507221, -184.96284270765, 18.45318524202, 24.0251747535325, 
                                                                 -17.2227224445543, 72.0898450404128, 77.3400269579394, 11.4445438761823, 
                                                                 -39.8399136067962, 18.5753251409526, -91.0200933927729, -53.7306953307048, 
                                                                 7.82902710952271, -81.2767391884694, 86.2556579176221, -14.7859426091212, 
                                                                 25.3677870484381, -32.6501861291018, 99.8278847617673, 34.9829942023039, 
                                                                 73.0140475212959, -16.6109126845448, -32.7592335826843, 74.8537874088129, 
                                                                 -37.8227588694546, 12.7268934849489, 36.3066722290206, -104.553574396311, 
                                                                 82.2742694540057, -16.276676021576, -14.8270655993876, -69.9284468279249, 
                                                                 -18.6364153009855, 20.2791386270462, -48.9278429329243, 71.9757184735048, 
                                                                 21.1260821883741, 103.424754322692, 42.6072553816148, 17.7123515166677, 
                                                                 14.698117153582, -16.6135250838639, -60.331196596044, 60.0246972779761, 
                                                                 -25.8563620618713, 9.82868981305174, -10.2896467597535, -64.5899487712409, 
                                                                 74.419091432425, -20.2832034012612, -41.4624965895172, 22.6792451066338, 
                                                                 62.3720212013179, 43.0183777764626, -19.2322428203173, -103.973231184753, 
                                                                 8.64157577348479, -77.8111614478938, 12.0402859159796, 7.7602400256449, 
                                                                 -29.4432033091852, 169.331480455615, 20.2505790945828, 11.6731976068873, 
                                                                 -1.37261032571168, -24.2041204066012, -48.8151629352629, 28.1533661206446, 
                                                                 32.9620623245219, 6.79303777939566, -41.4405364060766, 61.2589755596993, 
                                                                 -38.8352965646988, 31.5222920414193, 102.426587566278, -8.30057170680298, 
                                                                 3.62291497659761, -17.2961421371642, -128.80726784859, -113.047762472691, 
                                                                 25.2152322142063, -18.7119726133478, -169.84485712416, -67.2163912629475, 
                                                                 41.2838092875223, -39.2234894552068, -43.5948519630358, 15.8241092201283, 
                                                                 113.058937991131, 57.0302050221283, -159.765290823302)), class = "data.frame", row.names = c(NA, 
                                                                                                                                                              -100L))

data_test<-structure(list(y = c(0.875261480116371, 0.13319865469368, 0.00127171595390059, 
                     0.120784784396633, 0.396145484602405, 0.0145083415906443, 0.126972404606687, 
                     0.158633863426125, 0.307832433458906, 3.42112531824949, 0.0340520045576305, 
                     0.0577209021937775, 0.0296622168402153, 0.519694575795073, 0.598147976985479, 
                     0.0130977584533862, 0.158721871619698, 0.0345042704092106, 0.828465740122911, 
                     0.288698762072103, 0.00612936654816416, 0.660590833311049, 0.744003852280185, 
                     0.0218624098840227, 0.0643524619734905, 0.106603465426499, 0.996560657600869, 
                     0.122380988335843, 0.533105113544206, 0.0275922420213573, 0.107316738492487, 
                     0.560308948944375, 0.143056108849691, 0.0161973817777236, 0.131817444834554, 
                     1.0931449919045, 0.676905541419034, 0.0264930182311347, 0.0219841874288544, 
                     0.488998767576592, 0.0347315975270806, 0.0411243463454958, 0.239393381406891, 
                     0.518050404977722, 0.0446311348629937, 1.06966798067092, 0.181537821115414, 
                     0.0313727396250001, 0.0216034647860423, 0.0276009215712176, 0.363985328271051, 
                     0.360296428331267, 0.0668551459074575, 0.0096603143441187, 0.0105876830440506, 
                     0.417186148227153, 0.553820116962763, 0.0411408340216935, 0.171913862343572, 
                     0.0514348158606772, 0.389026902873765, 0.185058082651845, 0.0369879163899647, 
                     1.08104328029981, 0.00746768318488792, 0.605457684587019, 0.0144968484938537, 
                     0.00602213252556212, 0.0866902221106011, 2.86731502732902, 0.0410085953665955, 
                     0.013626354236944, 0.000188405910625031, 0.0585839444657251, 
                     0.238292013239626, 0.0792612023923057, 0.108649755268567, 0.00461453622722967, 
                     0.171731805762336, 0.375266208662383, 0.15081802592681, 0.0993654895544528, 
                     1.04912058404724, 0.00688994906597782, 0.00131255129276553, 0.0299156532828988, 
                     1.65913122506183, 1.2779796600082, 0.0635807935616347, 0.0350137919082677, 
                     2.88472754915262, 0.451804325441365, 0.170435290928851, 0.153848212504272, 
                     0.190051111767901, 0.0250402432610551, 1.27823234596823, 0.325244428486599, 
                     2.55249481518541, 0.652738453178641), x = c(0.157051206555181, 
                                                                 93.5554103254521, 36.4963908754934, -3.56611266493445, 34.7541054260692, 
                                                                 -62.9400893391807, 12.0450577377795, -35.6331874250237, -39.828866846312, 
                                                                 55.4826489507221, -184.96284270765, 18.45318524202, 24.0251747535325, 
                                                                 -17.2227224445543, 72.0898450404128, 77.3400269579394, 11.4445438761823, 
                                                                 -39.8399136067962, 18.5753251409526, -91.0200933927729, -53.7306953307048, 
                                                                 7.82902710952271, -81.2767391884694, 86.2556579176221, -14.7859426091212, 
                                                                 25.3677870484381, -32.6501861291018, 99.8278847617673, 34.9829942023039, 
                                                                 73.0140475212959, -16.6109126845448, -32.7592335826843, 74.8537874088129, 
                                                                 -37.8227588694546, 12.7268934849489, 36.3066722290206, -104.553574396311, 
                                                                 82.2742694540057, -16.276676021576, -14.8270655993876, -69.9284468279249, 
                                                                 -18.6364153009855, 20.2791386270462, -48.9278429329243, 71.9757184735048, 
                                                                 21.1260821883741, 103.424754322692, 42.6072553816148, 17.7123515166677, 
                                                                 14.698117153582, -16.6135250838639, -60.331196596044, 60.0246972779761, 
                                                                 -25.8563620618713, 9.82868981305174, -10.2896467597535, -64.5899487712409, 
                                                                 74.419091432425, -20.2832034012612, -41.4624965895172, 22.6792451066338, 
                                                                 62.3720212013179, 43.0183777764626, -19.2322428203173, -103.973231184753, 
                                                                 8.64157577348479, -77.8111614478938, 12.0402859159796, 7.7602400256449, 
                                                                 -29.4432033091852, 169.331480455615, 20.2505790945828, 11.6731976068873, 
                                                                 -1.37261032571168, -24.2041204066012, -48.8151629352629, 28.1533661206446, 
                                                                 32.9620623245219, 6.79303777939566, -41.4405364060766, 61.2589755596993, 
                                                                 -38.8352965646988, 31.5222920414193, 102.426587566278, -8.30057170680298, 
                                                                 3.62291497659761, -17.2961421371642, -128.80726784859, -113.047762472691, 
                                                                 25.2152322142063, -18.7119726133478, -169.84485712416, -67.2163912629475, 
                                                                 41.2838092875223, -39.2234894552068, -43.5948519630358, 15.8241092201283, 
                                                                 113.058937991131, 57.0302050221283, -159.765290823302)), class = "data.frame", row.names = c(NA, 
                                                                                                                                                              -100L))

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asked May 27, 2020 at 13:33

oercim

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