> dput(mydat)
structure(list(ID = c(31L, 35L, 115L, 48L, 36L, 73L, 111L, 51L,
113L, 20L, 16L, 51L, 59L, 79L, 107L, 90L, 60L, 72L, 21L, 28L,
104L, 65L, 63L, 132L, 99L, 52L, 93L, 57L, 87L, 83L, 57L, 69L,
110L, 12L, 78L, 125L, 80L, 80L, 126L, 74L, 48L, 135L, 7L, 5L,
66L, 51L, 136L, 46L, 3L, 80L, 130L, 6L, 129L, 63L, 88L, 49L,
60L, 71L, 42L, 89L, 106L, 128L, 114L, 82L, 103L, 8L, 67L, 130L,
118L, 130L, 48L, 13L, 51L, 100L, 85L, 21L, 87L, 67L, 39L, 8L,
18L, 29L, 74L, 103L, 98L, 135L, 88L, 10L, 93L, 128L, 2L, 90L,
12L, 10L, 66L, 52L, 25L, 128L, 123L, 75L, 13L, 3L, 37L, 85L,
53L, 13L, 10L, 76L, 93L, 68L, 40L, 36L, 29L, 109L, 96L, 120L,
4L, 75L, 81L, 119L, 45L, 11L, 77L, 136L, 33L, 17L, 15L, 126L,
99L, 45L, 26L, 37L, 42L, 2L, 105L, 98L, 62L, 42L, 27L, 124L,
47L, 85L, 115L, 122L, 120L, 100L, 136L, 62L, 99L, 99L, 78L, 71L,
93L, 118L, 28L, 103L, 43L, 64L, 56L, 124L, 128L, 103L, 82L, 138L,
110L, 60L, 49L, 12L, 92L, 89L, 123L, 44L, 108L, 71L, 3L, 26L,
125L, 6L, 113L, 117L, 97L, 6L, 17L, 91L, 109L, 126L, 32L, 90L,
114L, 66L, 104L, 12L, 1L, 98L, 76L, 60L, 23L, 69L, 84L, 111L),
Y = c(1.50403545875011, 0.786396740696073, 4.47452220273871,
4.38068147273783, 3.12839926871781, 3.71525102887885, 4.91099771631064,
5.6099549267089, 2.56348108539441, 3.19948091486236, 2.08635983067475,
1.0763458953491, 1.51606413703901, 5.24654043255577, 4.52424029343984,
4.20774205260695, 4.12910958622073, 0.633743240652555, 4.77971190302622,
3.93816934639032, 1.49484285995404, 1.56126305852856, 4.46548695066214,
1.0084930673158, 3.04727486738418, 3.35888620440587, 3.40432046722173,
3.76440032295639, 4.07871050532828, 4.19226071864204, 1.7160033436348,
1.03724192908934, 1.58238166258979, 3.68196445899468, 3.94299959336604,
4.1723985779393, 1.48656664161404, 2.82216807936802, 4.29307514012286,
1.56346766351964, 2.82672252016899, 2.88817949391832, 1.579432355962,
2.75587485567249, 4.52577018572453, 4.78804103487477, 3.76900787094377,
4.59964294342393, 1.20237162906479, 1.54913073517381, 2.36361197989214,
5.29470645462496, 4.37803432245733, 1.53760300072777, 4.68198252411321,
4.24868424001548, 2.70586371228392, 0.795680498261033, 2.86864443839483,
5.05097104595277, 1.75587485567249, 1.4190891773674, 4.60685410171667,
2.06818586174616, 0.965391506489986, 1.64857793561105, 5.11577022280303,
3.23527587668705, 3.70722941932729, 1.59578845118596, 3.48826861549546,
4.15706370038262, 0.487678450889512, 3.22814360759774, 4.77382300021727,
3.69583177282669, 4.62949114909487, 4.4704545944726, 2.69108149212297,
3.4379090355395, 4.8963222496021, 2.03342375548695, 4.28386634847347,
2.83569057149243, 1.69219063796734, 4.30362797638389, 3.03981055414835,
5.36239952864841, 1.58185218466613, 4.36789612311481, 2.85064623518307,
0.684841449866386, 4.95956127990689, 4.73626101314068, 3.74036268949424,
4.58490763903562, 2.04139268515822, 4.33829709023269, 4.43218332439469,
4.84305827754328, 1.81291335664286, 1.3818767641537, 1.23195309926451,
3.45651785780526, 3.28375338333253, 4.76814952267996, 4.21208101599211,
4.61628642071719, 3.52930199778798, 3.87926795682461, 0.152221483815728,
3.37621185028267, 1.05830921980416, 4.56712051193916, 4.01973923267471,
4.52527809662657, 3.55762748842683, 6.16255544574663, 3.3392526340327,
4.9800761268684, 4.67728750108277, 2.77305469336426, 4.37963179601937,
5.08042444249764, 4.41390299750444, 1.13552331082485, 4.83799224531426,
0.949064591865248, 1.67706978322635, 4.95286990226433, 1.25024436906661,
1.66401619974254, 5.82804345699154, 3.19728055812562, 4.28768978393608,
3.66913084737333, 1.61566819024883, 2.77959649125782, 1.46691039598072,
1.3041916833582, 2.99475694458763, 1.02667774806713, 3.63346845557959,
4.97170714505069, 4.92332693090275, 4.34570692127843, 1.48434160170634,
2.78175537465247, 4.30446898485417, 3.35621713421974, 2,
2.01703333929878, 2.71180722904119, 4.74145100905455, 3.49262072204319,
4.93047527477251, 4.47468237035325, 1.79239168949825, 4.8662518850263,
1.49607694983225, 1.60572117915046, 4.3945392313722, 2.07918124604762,
1.22862414376523, 1.68741633267633, 4.06740565843782, 4.09537859956006,
3.53617953213723, 3.67089495352021, 4.00436437110775, 2.10720996964787,
3.90167623132638, 6.60281435674245, 6.51005266486288, 4.60659630917929,
1.6845945840158, 4.9596613702735, 1.69603672384819, 2.68841982200271,
1.48366771663978, 1.5218420041367, 4.65083185753557, 1.83884909073726,
3.05766610390983, 3.61151088712666, 3.78290240597464, 2.01283722470517,
3.34084054981233, 1.27334439191564, 2.32837960343874, 4.10859884597357,
0.864516798738721, 1.22753724273698, 3.99100444033076, 0.752257491617398,
5.11358574735257, 4.13624480174614, 3.87128097285797, 3.99690551069567,
3.05766610390983), X = c(5.7, 6.3, 4.7, 17, 0.9, 0.6, 3,
4.1, 6.9, 2, 11.1, 3.7, 2, 12, 1, 3.4, 8.9, 12, 12, 0, 3.9,
3.9, 7.9, 17, 19, 1.7, 16, 13.9, 7, 9.9, 0.9, 0, 3.6, 17.3,
11.7, 5, 1.7, 5.6, 8.1, 11, 3.9, 16.3, 2.1, 19.7, 19.4, 2.7,
0.9, 2, 15.9, 15.9, 12.1, 16.6, 14, 7.1, 1.9, 1, 18.7, 0,
3.9, 0.9, 8.1, 11.9, 0, 4.1, 7, 2, 2.9, 9.7, 3.6, 3.4, 9.1,
8.1, 13.9, 2.4, 6.9, 11.1, 4.9, 0, 9.1, 18.9, 1.9, 12, 1.1,
2.9, 4, 7.7, 3.9, 1.7, 2.9, 2, 6.1, 2, 5, 10.7, 14.3, 2.9,
2.4, 17.9, 2, 0.9, 10, 2.9, 2.9, 1.9, 8.7, 1.9, 13.4, 0,
14, 0.9, 2.4, 2, 8.9, 1.9, 0, 9.9, 6.9, 3.7, 0, 15.1, 0,
12.1, 8.9, 10.1, 0.9, 11.7, 2.7, 11.9, 10.9, 9, 0.9, 0.9,
9.9, 17.9, 18.9, 15, 8, 15.7, 10.9, 9.7, 13.9, 3.9, 1.9,
1.9, 2, 3.1, 5.1, 0.7, 15.9, 7.9, 10.1, 6.1, 5, 4.9, 6.3,
2, 1.7, 2.9, 13.3, 2.1, 1, 3, 0.7, 7.9, 10, 4, 4, 4, 3.9,
1.9, 3.9, 4.9, 4.1, 11.9, 7, 13, 7.9, 7.1, 2.6, 8.7, 10.9,
6.1, 16.9, 2.7, 3.3, 0.3, 16.9, 0, 6, 16.3, 1.4, 9.1, 1.9,
3.9, 8, 6, 8, 11.9, 0, 0.9)), .Names = c("ID", "Y", "X"), row.names = c(NA,
-200L), class = "data.frame")
我有一个简单的数据集，包含3个变量，X，Y和主题ID。
> head(mydat)
ID Y X
1 31 1.5040355 5.7
2 35 0.7863967 6.3
3 115 4.4745222 4.7
4 48 4.3806815 17.0
5 36 3.1283993 0.9
6 73 3.7152510 0.6
我运行以下非线性混合模型，它运行得很好。
library(nlme)
model <- nlme(Y ~ a*exp(X) + b,
data = mydat,
fixed = list(a ~ 1, b ~ 1),
random = list(ID = pdDiag(list(a ~ 1, b ~ 1))),
start = list(fixed = c(a = 1, b = 1)))
现在我在我的数据集上使用groupedData命令。但是，当我在groupedData对象上运行相同的精确分析时，模型不再适合。
mydat2 <- groupedData(Y ~ X | ID, data = mydat)
model2 <- nlme(Y ~ a*exp(X) + b,
data = mydat2,
fixed = list(a ~ 1, b ~ 1),
random = list(ID = pdDiag(list(a ~ 1, b ~ 1))),
start = list(fixed = c(a = 1, b = 1)))
Error in nlme.formula(Y ~ a * exp(X) + b, data = mydat2, fixed = list(a ~ :
step halving factor reduced below minimum in PNLS step
我不明白为什么，因为groupedData调用不应该更改mydat的内容。出了什么问题？
答案 0 :(得分：0)
mydat2 <- groupedData(Y ~ X | ID, data = mydat,order.groups=FALSE)
作为order.groups = TRUE的默认值（请参阅groupsData的帮助）。