R 非线性模型以漏掉一个ID的交叉验证模式预测NA值
我有一个数据帧R 非线性模型以漏掉一个ID的交叉验证模式预测NA值,r,prediction,cross-validation,nonlinear-functions,R,Prediction,Cross Validation,Nonlinear Functions,我有一个数据帧df df<-structure(list(ID = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 19L, 19L, 19L, 19L, 19L, 19L, 24L, 24L, 24L, 25L, 25L, 26L, 26L, 36L, 36L, 36L, 36L, 36L, 36L, 41L, 41L, 41L, 41L,
dfdf<-structure(list(ID = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 19L, 19L,
19L, 19L, 19L, 19L, 24L, 24L, 24L, 25L, 25L, 26L, 26L, 36L, 36L,
36L, 36L, 36L, 36L, 41L, 41L, 41L, 41L, 41L, 41L, 42L, 45L, 45L,
45L, 45L, 45L, 47L, 55L, 64L, 64L, 65L, 71L, 71L, 71L, 71L, 71L,
43L, 43L, 43L, 43L, 43L, 43L, 43L, 60L, 60L, 60L, 60L, 60L, 60L,
60L, 60L, 60L), .Label = c("AU-Tum", "BE-Bra", "BR-Sa3", "CA-Ca1",
"CA-Ca2", "CA-Ca3", "CA-Gro", "Ca-Man", "CA-NS1", "CA-NS2", "CA-NS3",
"CA-NS4", "CA-NS5", "CA-NS6", "CA-NS7", "CA-Oas", "CA-Obs", "CA-Ojp",
"CA-Qcu", "CA-Qfo", "CA-SF1", "CA-SF2", "CA-SF3", "CA-SJ1", "CA-SJ2",
"CA-SJ3", "CA-TP1", "CA-TP2", "CA-TP4", "CZ-Bk1", "DE-Har", "DE-Wet",
"DK-Sor", "FI-Hyy", "FR-Hes", "FR-Pue", "ID-Pag", "IT-Ro1", "IT-Ro2",
"IT-Sro", "JP-Tak", "JP-Tef", "NL-Loo", "SE-Abi", "SE-Fla", "SE-Nor",
"SE-Sk1", "SE-Sk2", "SE-St1", "UK-Gri", "US-Blo", "US-Bn1", "US-Bn2",
"Us-Bn3", "US-Dk3", "US-Fmf", "US-Fwf", "US-Ha1", "US-Ha2", "US-Ho1",
"US-Ho2", "US-Lph", "US-Me1", "US-Me3", "US-Nc2", "US-NR1", "US-Sp1",
"US-Sp2", "US-Sp3", "US-Umb", "US-Wcr", "US-Wi0", "US-Wi1", "US-Wi2",
"US-Wi4", "US-Wi8"), class = "factor"), y = c(380.654850683175,
467.840079978108, 358.497598990798, 431.528439198621, 442.010991849005,
351.189937948249, 343.098349086009, 357.122478995472, -471.194518864155,
-514.390561270528, -518.123427070677, -554.919201846235, -614.669225180172,
-391.545088194311, -124.396037524566, 32.3765077856369, 43.199114789255,
-88.3050879996736, 58.9395819107303, 162.441016515717, 116.965395963751,
-108.997818851843, -144.127755056645, -126.626824281528, -175.750439967494,
-151.262252734334, -135.830685457215, 28.9567997518461, -68.2299433113076,
-37.1677788909292, -167.045088054205, -108.258462657337, 100.907804159913,
90.3369144331664, 233.031065647025, 287.956774678081, 189.082761215046,
390.740067397826, 89.1989531565923, 155.527563805692, 224.442115622107,
315.516411969438, 283.912847682368, 390.026366345584, 322.790248586796,
312.50101460889, -638.973101716489, 132.601979068451, -42.7843619789928,
82.2957233709167, 8.36848279205151, 115.376620422816, -186.42650026083,
577.658561848104, 188.342473105964, 95.6089326666552, 936.236027855426,
70.9266221858561, -5.91938436031342, 338.1149700284, 185.940875658067,
198.121665383659, 254.551377562806, 45.4501812993549, 187.152575587854,
152.183998291846, 226.360116416588, 225.67982583819, -0.0398367510642856,
217.845216980437, 241.779151081573, 214.481376983225, 219.953942558961,
315.959296110785, 263.547381375218, 194.449290025979, 305.158690313809,
326.318877183832), x = c(49, 50, 51, 52, 53, 54, 55, 56, 0, 1,
2, 3, 4, 5, 13, 14, 15, 16, 17, 71, 72, 1, 2, 3, 4, 5, 6, 9,
10, 11, 1, 3, 29, 30, 54, 55, 56, 57, 58, 59, 64, 65, 66, 67,
68, 69, 1, 34, 35, 37, 38, 39, 2, 19, 17, 18, 16, 67, 69, 70,
72, 73, 101, 105, 106, 107, 108, 109, 110, 131, 132, 133, 134,
135, 136, 137, 138, 139)), .Names = c("ID", "y", "x"), row.names = c(712L,
713L, 714L, 715L, 716L, 717L, 718L, 719L, 720L, 721L, 722L, 723L,
724L, 725L, 726L, 727L, 728L, 729L, 730L, 731L, 732L, 784L, 785L,
786L, 787L, 788L, 789L, 793L, 794L, 795L, 796L, 797L, 798L, 799L,
841L, 842L, 843L, 844L, 845L, 846L, 866L, 867L, 868L, 869L, 870L,
871L, 872L, 880L, 881L, 882L, 883L, 884L, 889L, 892L, 916L, 917L,
918L, 936L, 937L, 938L, 939L, 940L, 873L, 874L, 875L, 876L, 877L,
878L, 879L, 905L, 906L, 907L, 908L, 909L, 910L, 911L, 912L, 913L
), class = "data.frame")
stat(df)
dfdf<-structure(list(ID = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 19L, 19L,
19L, 19L, 19L, 19L, 24L, 24L, 24L, 25L, 25L, 26L, 26L, 36L, 36L,
36L, 36L, 36L, 36L, 41L, 41L, 41L, 41L, 41L, 41L, 42L, 45L, 45L,
45L, 45L, 45L, 47L, 55L, 64L, 64L, 65L, 71L, 71L, 71L, 71L, 71L,
43L, 43L, 43L, 43L, 43L, 43L, 43L, 60L, 60L, 60L, 60L, 60L, 60L,
60L, 60L, 60L), .Label = c("AU-Tum", "BE-Bra", "BR-Sa3", "CA-Ca1",
"CA-Ca2", "CA-Ca3", "CA-Gro", "Ca-Man", "CA-NS1", "CA-NS2", "CA-NS3",
"CA-NS4", "CA-NS5", "CA-NS6", "CA-NS7", "CA-Oas", "CA-Obs", "CA-Ojp",
"CA-Qcu", "CA-Qfo", "CA-SF1", "CA-SF2", "CA-SF3", "CA-SJ1", "CA-SJ2",
"CA-SJ3", "CA-TP1", "CA-TP2", "CA-TP4", "CZ-Bk1", "DE-Har", "DE-Wet",
"DK-Sor", "FI-Hyy", "FR-Hes", "FR-Pue", "ID-Pag", "IT-Ro1", "IT-Ro2",
"IT-Sro", "JP-Tak", "JP-Tef", "NL-Loo", "SE-Abi", "SE-Fla", "SE-Nor",
"SE-Sk1", "SE-Sk2", "SE-St1", "UK-Gri", "US-Blo", "US-Bn1", "US-Bn2",
"Us-Bn3", "US-Dk3", "US-Fmf", "US-Fwf", "US-Ha1", "US-Ha2", "US-Ho1",
"US-Ho2", "US-Lph", "US-Me1", "US-Me3", "US-Nc2", "US-NR1", "US-Sp1",
"US-Sp2", "US-Sp3", "US-Umb", "US-Wcr", "US-Wi0", "US-Wi1", "US-Wi2",
"US-Wi4", "US-Wi8"), class = "factor"), y = c(380.654850683175,
467.840079978108, 358.497598990798, 431.528439198621, 442.010991849005,
351.189937948249, 343.098349086009, 357.122478995472, -471.194518864155,
-514.390561270528, -518.123427070677, -554.919201846235, -614.669225180172,
-391.545088194311, -124.396037524566, 32.3765077856369, 43.199114789255,
-88.3050879996736, 58.9395819107303, 162.441016515717, 116.965395963751,
-108.997818851843, -144.127755056645, -126.626824281528, -175.750439967494,
-151.262252734334, -135.830685457215, 28.9567997518461, -68.2299433113076,
-37.1677788909292, -167.045088054205, -108.258462657337, 100.907804159913,
90.3369144331664, 233.031065647025, 287.956774678081, 189.082761215046,
390.740067397826, 89.1989531565923, 155.527563805692, 224.442115622107,
315.516411969438, 283.912847682368, 390.026366345584, 322.790248586796,
312.50101460889, -638.973101716489, 132.601979068451, -42.7843619789928,
82.2957233709167, 8.36848279205151, 115.376620422816, -186.42650026083,
577.658561848104, 188.342473105964, 95.6089326666552, 936.236027855426,
70.9266221858561, -5.91938436031342, 338.1149700284, 185.940875658067,
198.121665383659, 254.551377562806, 45.4501812993549, 187.152575587854,
152.183998291846, 226.360116416588, 225.67982583819, -0.0398367510642856,
217.845216980437, 241.779151081573, 214.481376983225, 219.953942558961,
315.959296110785, 263.547381375218, 194.449290025979, 305.158690313809,
326.318877183832), x = c(49, 50, 51, 52, 53, 54, 55, 56, 0, 1,
2, 3, 4, 5, 13, 14, 15, 16, 17, 71, 72, 1, 2, 3, 4, 5, 6, 9,
10, 11, 1, 3, 29, 30, 54, 55, 56, 57, 58, 59, 64, 65, 66, 67,
68, 69, 1, 34, 35, 37, 38, 39, 2, 19, 17, 18, 16, 67, 69, 70,
72, 73, 101, 105, 106, 107, 108, 109, 110, 131, 132, 133, 134,
135, 136, 137, 138, 139)), .Names = c("ID", "y", "x"), row.names = c(712L,
713L, 714L, 715L, 716L, 717L, 718L, 719L, 720L, 721L, 722L, 723L,
724L, 725L, 726L, 727L, 728L, 729L, 730L, 731L, 732L, 784L, 785L,
786L, 787L, 788L, 789L, 793L, 794L, 795L, 796L, 797L, 798L, 799L,
841L, 842L, 843L, 844L, 845L, 846L, 866L, 867L, 868L, 869L, 870L,
871L, 872L, 880L, 881L, 882L, 883L, 884L, 889L, 892L, 916L, 917L,
918L, 936L, 937L, 938L, 939L, 940L, 873L, 874L, 875L, 876L, 877L,
878L, 879L, 905L, 906L, 907L, 908L, 909L, 910L, 911L, 912L, 913L
), class = "data.frame")
stat(df)
dfNA[1] NA NA NA NA NA NA 208.10405 NA NA NA NA NA NA 38.21535 NA
[16] 90.76849 95.81436 103.35304 105.50550 NA NA NA 15.39989 NA NA NA 44.12078 NA NA NA
[31] NA NA 153.22284 156.78703 NA NA NA NA NA NA NA NA NA 222.45487 NA
[46] NA NA NA 175.06730 NA 181.72692 181.68818 NA NA NA NA NA NA NA NA
[61] NA NA NA NA NA 252.93564 NA NA 259.31112 NA NA NA NA 252.88759 NA
[76] 258.52018 253.69781 252.87979
有人对此有解释吗 您使用了错误的模型。让我们绘制您的数据：
plot(y ~ x, data = df)
fit <- nls(y ~ SSasympOff(x, A, lrc, c0), data = df)
lines(predict(fit, newdata = data.frame(x = 0:140)))
fit查看i=1的情况；nls（y~A*（1-exp（k*x）），data=df[-i，]，start=list（A=1000，k=-0.224））
，就是这样发生的<代码>nls中的错误（y~A*（1-exp（k*x）），数据=df[-i，]，开始=list（A=1000，：迭代次数超过了最大值50次。NASSASymoff