我已经成功地使用nlme包的nlme（）函数拟合了非线性混合模型，但是在尝试通过包含权重参数来改进所述模型时，除了我无法解释的错误之外我什么都没得到，而且我有因此无法确定问题的确切来源。
我将以下代码用于非加权模型：
fctmod<-function(x,y,z){
resultat<-(1+x*((z-mean(z))/mean(z))+y*((z-mean(z))/mean(z))^2)
return(resultat)
}
mod<- nlme(INC ~ (a*(CUMU)^(b) - c*CUMU)*
fctmod(d,e,tpt)*
fctmod(f,g,rdan),
fixed = a + b + c + d + e + f + g ~ 1,
random = a ~ 1,
start = list(fixed = c(a = 1, b = 0.1, c = 0.1, d=0, e=0, f=0, g=0)),
groups = ~ ID,
data = dataEg)
这是加权模型：
mod<- nlme(INC ~ (a*(CUMU)^(b) - c*CUMU)*
fctmod(d,e,tpt)*
fctmod(f,g,rdan),
fixed = a + b + c + d + e + f + g ~ 1,
random = a ~ 1,
start = list(fixed = c(a = 1, b = 0.1, c = 0.1, d=0, e=0, f=0, g=0)),
groups = ~ ID,
data = dataEg,
weights=varFixed(~CUMU))
我收到以下错误：
conLin $ Xy * varWeights（object）中的错误：不一致的数组
我理解这与矩阵和矢量的乘积有关，其中一个不是相同的维度，因此可以成功地执行操作。但是，我无法弄清楚如何检查这些对象的尺寸，甚至无法访问它们并尝试解决问题。
如果我将varFixed（）参数更改为varIdent参数，则消息会发生变化但基本上告诉我同样的事情，就我自己收集而言：
[...]
weights=varIdent(~CUMU))
recalc.varFunc（object [[i]]，conLin）中的错误：dims [product 3618] 不匹配对象的长度[37776]另外：警告 message：在conLin $ Xy * varWeights（object）中：对象长度越长 不是较短物体长度的倍数
非常感谢任何有关此事的帮助！
以下是我的数据示例：
ID INC CUMU tpt rdan
1801 50.87048 5.09E+01 0.37017968 8.003372945
1801 250.7865 3.02E+02 -0.93841232 -0.284327055
1801 216.115 5.18E+02 -1.97075332 -11.63592706
1801 17.49655 5.35E+02 -0.57010332 -13.39202706
1801 64.32044 6.00E+02 0.48817968 -14.29292706
1801 93.44014 6.93E+02 0.74130468 -11.03592706
1801 323.8984 1.02E+03 0.04017168 -6.111227055
1801 340.4834 1.36E+03 -0.71888732 12.76487295
1801 538.6622 1.90E+03 -0.78026232 -4.722627055
1801 836.1861 2.73E+03 -2.75356232 12.19917295
1801 993.915 3.73E+03 0.84063768 7.208372945
1801 1350.227 5.08E+03 -0.16196232 2.234672945
1801 1704.331 6.78E+03 2.06117168 13.33377295
1801 1497.009 8.28E+03 2.43130468 -7.332427055
1801 2343.468 1.06E+04 0.26309668 8.642372945
1801 2694.576 1.33E+04 -2.19830332 8.492172945
1801 1929.02 1.52E+04 -2.44949532 -2.072127055
1801 2224.844 1.75E+04 -3.29004532 7.834272945
1801 2481.327 2.00E+04 -1.31985332 0.178472945
1801 4105.118 2.41E+04 -2.53618732 15.21767295
1801 2861.757 2.69E+04 -2.98210332 -6.081427055
1801 3757.36 3.07E+04 -1.94142032 7.024772945
1801 4883.06 3.56E+04 -3.02842032 7.071672945
1801 4492.688 4.01E+04 -2.61557032 13.03677295
1801 4421.439 4.45E+04 -4.64508732 15.32817295
1801 4291.048 4.88E+04 0.08944668 -23.36592706
1801 5872.25 5.46E+04 -2.48509532 -11.84332706
1801 4726.314 5.94E+04 -1.97220332 -2.063327055
1801 2258.114 6.16E+04 -2.40127032 0.703472945
1801 3064.254 6.47E+04 0.06097168 2.285672945
1801 5209.305 6.99E+04 -3.75766232 3.212772945
1801 5789.695 7.57E+04 -1.10404532 -2.296027055
1801 5278.369 8.10E+04 -1.44937832 -10.85002706
1801 5776.588 8.67E+04 -0.70907832 0.248972945
1801 5965.722 9.27E+04 -2.72012832 -7.313327055
1801 5381.872 9.81E+04 -0.98598732 -20.67842706
1801 3894.213 1.02E+05 -0.57961232 -8.999627055
1801 5239.56 1.07E+05 -2.10707032 6.151372945
1801 7147.558 1.14E+05 -2.53260332 -8.591527055
1801 8615.51 1.23E+05 -0.24122032 -15.93492706
1801 6256.669 1.29E+05 0.37691268 -23.57042706
1801 6037.065 1.35E+05 -2.67912032 -8.294527055
1801 6629.371 1.42E+05 -1.11789532 -11.14572706
1801 6476.749 1.48E+05 -0.51492832 -0.753727055
1801 6581.073 1.55E+05 -2.93513732 7.555172945
1801 6969.042 1.62E+05 -2.23327032 -9.324827055
1801 4403.415 1.66E+05 -1.18624532 -9.837027055
1801 5096.871 1.71E+05 -2.07377032 -6.567227055
1801 5027.219 1.76E+05 -1.05444532 5.552872945
1801 4732.223 1.81E+05 -1.79232832 7.504872945
1801 6492.61 1.88E+05 0.73975468 -14.91762706
1801 7186.891 1.95E+05 0.44052968 0.669772945
1801 5676.406 2.01E+05 0.10896268 -19.10652706
1808 2.653267 2.65E+00 -2.06587032 -13.18752706
1808 12.81145 1.55E+01 -0.75933732 -13.96612706
1808 25.68672 4.12E+01 0.27944668 -21.30192706
1808 37.65382 7.88E+01 0.61759668 -17.10762706
1808 71.66865 1.50E+02 -0.18621232 -9.912727055
1808 158.0589 3.09E+02 -0.86542032 8.144972945
1808 156.2768 4.65E+02 -0.86728732 -7.570827055
1808 237.5628 7.02E+02 -2.82411232 6.361572945
1808 184.6583 8.87E+02 0.67944668 3.986372945
1808 219.4781 1.11E+03 -0.26756232 -2.530227055
1808 386.6515 1.49E+03 2.29544668 15.52257295
1808 376.0046 1.87E+03 1.92659668 -9.689227055
1808 455.2105 2.32E+03 -0.47513732 6.732472945
1808 505.2185 2.83E+03 -2.13908732 6.763472945
1808 578.1172 3.41E+03 -2.33707032 -6.411127055
1808 570.791 3.98E+03 -3.25053732 5.383972945
1808 645.5343 4.62E+03 -1.38504532 -1.277927055
1808 835.1992 5.46E+03 -2.60448732 14.52987295
1808 719.335 6.18E+03 -3.06864532 -6.788227055
1808 803.2229 6.98E+03 -2.12332832 2.891972945
1808 1017.481 8.00E+03 -2.99362832 7.325372945
1808 1039.884 9.04E+03 -2.54442032 7.693072945
1808 1103.892 1.01E+04 -4.66533732 12.84257295
1808 1128.305 1.13E+04 -0.08787832 -27.45782706
1808 1203.774 1.25E+04 -2.51752032 -12.42242706
1808 1416.5 1.39E+04 -1.78362832 -3.775227055
1808 1149.501 1.50E+04 -2.73605332 -1.345627055
1808 1342.186 1.64E+04 0.30392968 1.708072945
1808 1692.817 1.81E+04 -3.53136232 -0.090827055
1808 1683.827 1.98E+04 -0.48927832 -2.944427055
1808 1754.922 2.15E+04 -0.58983732 -17.19532706
1808 1701.573 2.32E+04 -0.64008732 -6.623727055
1808 2075.097 2.53E+04 -2.37770332 -7.776127055
1808 1962.803 2.73E+04 -0.45017832 -21.55882706
1808 2167.067 2.94E+04 -0.30337032 -16.21162706
1808 2191.808 3.16E+04 -1.36269532 0.789772945
1808 2540.392 3.42E+04 -2.69541232 -11.72912706
1808 2449.676 3.66E+04 -0.22992832 -26.32262706
1808 2176.656 3.88E+04 0.88247168 -25.90452706
1808 1559.047 4.03E+04 -2.49282032 -10.71292706
1808 2051.73 4.24E+04 -0.94173732 -13.36192706
1808 2353.799 4.47E+04 -0.06031232 -4.518227055
1808 2339.927 4.71E+04 -2.61190332 -3.615227055
1808 2542.576 4.96E+04 -1.90476232 -17.21042706
1808 1851.814 5.15E+04 -1.00652032 -14.89022706
1808 1799.821 5.33E+04 -1.66852032 -12.93212706
1808 1751.032 5.50E+04 -0.76849532 -2.244227055
1808 2068.087 5.71E+04 -1.28739532 -13.79412706
1808 2520.929 5.96E+04 1.74033768 -40.11402706
1808 2524.768 6.21E+04 0.31034668 -15.37072706
1808 2245.268 6.44E+04 -0.14590332 -26.91642706
1822 243.2774 2.43E+02 0.71213768 0.361472945
1822 295.3962 5.39E+02 1.24134668 9.947772945
1822 422.28 9.61E+02 -0.99325332 9.723072945
1822 635.918 1.60E+03 -0.84666232 4.894672945
1822 684.5842 2.28E+03 -0.15511232 2.767772945
1822 740.4322 3.02E+03 0.01338768 13.02837295
1822 877.1983 3.90E+03 -0.76973732 11.24047295
1822 1102.142 5.00E+03 -0.06317832 3.155572945
1822 1114.751 6.12E+03 -0.01521232 10.34347295
1822 1498.066 7.61E+03 0.93285468 -4.146727055
1822 1274.27 8.89E+03 -0.99130332 11.08257295
1822 1295.652 1.02E+04 -0.74763732 13.15277295
1822 1456.543 1.16E+04 0.45261268 13.63757295
1822 1378.481 1.30E+04 -2.81537832 11.68637295
1822 1427.113 1.44E+04 0.84238768 -5.599227055
1822 1501.924 1.59E+04 -2.36837832 1.537272945
1822 2055.408 1.80E+04 -1.26663732 6.664072945
1822 2243.367 2.02E+04 -1.17715332 -5.906827055
1822 2117.086 2.24E+04 -1.52857832 -1.450027055
1822 2319.644 2.47E+04 -1.05047032 11.79147295
1822 1801.73 2.65E+04 -0.03891232 4.133472945
1822 2361.298 2.88E+04 -2.83277832 16.39077295
1822 2195.976 3.10E+04 -7.93014532 38.54507295
1822 2076.264 3.31E+04 -4.72564532 13.35337295
1822 1933.169 3.51E+04 -6.25872032 11.54057295
1822 1865.861 3.69E+04 -7.17471232 26.36887295
1822 1950.644 3.89E+04 -4.29537832 0.003172945
1822 1923.462 4.08E+04 -3.98904532 3.892672945
1822 1626.137 4.24E+04 -3.51962032 15.16987295
1822 2067.186 4.45E+04 -3.14743732 1.357472945
1822 2397.914 4.69E+04 -1.64246232 -13.69462706
1822 2238.5 4.91E+04 -6.21394532 3.198272945
1822 2409.343 5.15E+04 -6.55646232 27.01797295
1822 2037.574 5.36E+04 -5.08392832 0.779972945
1822 2410.954 5.60E+04 -6.51442832 -3.198127055
1822 1831.59 5.78E+04 -6.70302032 6.626672945
1822 2113.152 5.99E+04 -3.89295332 16.36777295
1822 1767.248 6.17E+04 -5.83889532 -10.21052706
1822 2225.575 6.39E+04 -4.79626232 -2.438027055
1822 2007.595 6.59E+04 -2.00391232 -4.103427055
1822 2460.827 6.84E+04 -0.25860332 -5.483427055
1822 2169.772 7.06E+04 -0.82212032 -0.431927055
1822 1939.393 7.25E+04 -4.11975332 11.14317295
1822 1839.968 7.43E+04 -1.11702832 -10.03882706
1822 1911.487 7.62E+04 -0.57976232 -15.99062706
1822 2103.234 7.83E+04 -2.62102032 -5.665827055
1822 1894.019 8.02E+04 -2.52459532 8.237672945
1822 1900.976 8.21E+04 -1.05139532 3.891872945
1822 1923.652 8.41E+04 -0.54103732 -3.840127055
1822 2189.481 8.63E+04 -3.15074532 7.494872945
1822 3133.193 8.94E+04 -1.32373732 -3.874627055
1822 2840.912 9.22E+04 -0.15873732 -6.964527055
1822 2453.992 9.47E+04 -0.17384532 -4.425127055
1822 2116.707 9.68E+04 0.15636268 -12.93612706
1822 2572.732 9.94E+04 -0.95784532 -8.085427055
1822 1791.071 1.01E+05 -1.23557832 -6.776027055
1822 2640.578 1.04E+05 -2.54494532 -3.026927055
1822 3905.958 1.08E+05 0.08290468 -10.25792706
1822 3030.905 1.11E+05 -1.18086232 6.724372945
1822 2180.843 1.13E+05 -0.41478732 9.966672945
1822 2949.961 1.16E+05 -0.36606232 -12.79502706
1822 2123.581 1.18E+05 -3.25817832 1.413172945
1822 2190.637 1.20E+05 -1.54525332 -5.029327055
1822 2568.083 1.23E+05 -2.23234532 -4.825927055
1822 2310.152 1.25E+05 -3.18713732 7.569272945
1822 2455.807 1.28E+05 -1.74045332 -6.766027055
1822 2393.918 1.30E+05 -3.97016232 10.25977295
1822 2752.022 1.33E+05 -4.17411232 -5.978727055
1822 3097.308 1.36E+05 -3.83764532 -0.768227055
1822 2130.477 1.38E+05 -2.44955332 -2.951827055
1822 2233.559 1.40E+05 -2.00573732 9.120172945
1822 2395.441 1.43E+05 -4.33248732 8.190272945
1822 2584.954 1.45E+05 -0.70032032 -12.86992706
1822 1680.365 1.47E+05 -2.97705332 -0.922627055
1822 1928.463 1.49E+05 -3.69305332 6.553472945
1822 975.4539 1.50E+05 -4.79113732 11.17317295
1822 1595.284 1.51E+05 -1.38713732 20.42707295
1822 2313.053 1.54E+05 -4.95961232 10.84617295
1822 2299.346 1.56E+05 -1.97567832 1.962672945
1822 2084.111 1.58E+05 -1.78690332 -13.97662706
1822 1762.053 1.60E+05 -2.08870332 -5.256527055
1822 1498.806 1.61E+05 -3.47517032 -6.847927055
1822 1441.977 1.63E+05 -0.93724532 -6.746327055
1822 1250.551 1.64E+05 -1.41612832 -13.15312706
1822 1797.025 1.66E+05 -1.69650332 -2.344927055
1822 2195.586 1.68E+05 -3.24574532 -11.43942706
1822 1946.757 1.70E+05 -1.57494532 -17.32502706
1822 1573.032 1.71E+05 -0.61749532 -18.26172706
1822 1296.121 1.73E+05 -3.86612832 -1.943527055
1822 1514.312 1.74E+05 -2.15214532 -3.785027055
1822 1970.279 1.76E+05 -1.37000332 0.107872945
1822 1792.646 1.78E+05 -3.88349532 2.305472945
1822 2198.977 1.80E+05 -3.14851232 -17.17662706
1822 1545.574 1.82E+05 -1.78811232 -9.729927055
1822 1535.827 1.83E+05 -3.13195332 -7.044927055
1822 1328.625 1.85E+05 -2.12005332 -5.122827055
1822 1402.548 1.86E+05 -2.12696232 -5.865827055
1822 1505.996 1.88E+05 0.31811268 -34.11432706
1822 1535.659 1.89E+05 -0.31787832 -31.05032706
1822 1391.383 1.90E+05 -1.57599532 -34.63242706
提前致谢。
答案 0 :(得分：1)
我在使用lme函数时遇到了同样的问题，我发现我使用的权重变量不是矢量。
当使用as.vector（）或c（）将其更改为向量时，问题就消失了。
答案 1 :(得分：0)
nlme :: nlme函数和varFixed权重似乎存在一个错误。
一种解决方案是将固定权重与另一组等于1的权重组合在一起。
weights=varComb(varExp(fixed=0.000000001), varFixed(~CUMU)))
之所以可行，是因为：
all.equal(varWeights(Initialize(varExp(fixed=0.000000001), dataEg)), rep(1, nrow(dataEg)))
或
mod2 <- nlme(INC ~ (a*(CUMU)^(b) - c*CUMU)*fctmod(d,e,tpt)*fctmod(f,g,rdan),
fixed = a + b + c + d + e + f + g ~ 1,
random = a ~ 1,
start = list(fixed = c(a = 1, b = 0.1, c = 0.1, d=0, e=0, f=0, g=0)),
groups = ~ ID,
data = dataEg,
weights=varExp(fixed=0.000000001))
all.equal(varWeights(mod2$modelStruct$varStruct), rep(1, nrow(dataEg)))