R nlme重复测量中的错误

我试图运行一个线性混合模型，在57个不同的时间点重复测量。但我一直收到错误信息：

Error in solve.default(estimates[dimE[1L] - (p:1), dimE[2L] - (p:1), drop = FALSE]) : 

系统在计算上是奇异的：倒数条件数=7.7782e-18

这是什么意思

我的代码是这样的：

model.dataset = data.frame(TimepointM=timepoint,SubjectM=sample,GeneM=gene)
library("nlme")   
model = lme(score ~ TimepointM + GeneM,data=model.dataset,random = ~1|SubjectM)    

以下是数据：

score = c(2,-3,11,14,1,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,6,7,2,-3,11,14,1,7,6,7,2,-3,11,14,1,7,6,2,-3,11,14,1,7,7,2,-3,11,14,1,7,6,2,-3,11,14,1,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,2,-3,11,14,1,7,6,2,-3,11,14,1,7,7,2,-3,11,14,1,7,6,7,2,-3,11,14,1,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,6,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,6,7,2,-3,11,14,1,7,6,2,-3,11,14,1,7,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7,2,-3,11,14,1,7)
timepoint = c(1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6,7,7,7,7,7,7,8,8,8,8,8,8,8,9,9,9,9,9,9,10,10,10,10,10,10,10,12,12,12,12,12,12,12,12,13,13,13,13,13,13,13,13,14,14,14,14,14,14,14,14,15,15,15,15,15,15,16,16,16,16,16,16,16,16,17,17,17,17,17,17,18,18,18,18,18,18,19,19,19,19,19,19,19,20,20,20,20,20,20,21,21,21,21,21,21,24,24,24,24,24,24,24,25,25,25,25,25,25,25,27,27,27,27,27,27,28,28,28,28,28,28,29,29,29,29,29,29,30,30,30,30,30,30,30,31,31,31,31,31,31,31,32,32,32,32,32,32,33,33,33,33,33,33,33,33,34,34,34,34,34,34,34,35,35,35,35,35,35,36,36,36,36,36,36,36,37,37,37,37,37,37,38,38,38,38,38,38,39,39,39,39,39,39,39,40,40,40,40,40,40,40,41,41,41,41,41,41,41,41,42,42,42,42,42,42,42,42,43,43,43,43,43,43,44,44,44,44,44,44,44,45,45,45,45,45,45,46,46,46,46,46,46,47,47,47,47,47,47,48,48,48,48,48,48,49,49,49,49,49,49,49,50,50,50,50,50,50,51,51,51,51,51,51,52,52,52,52,52,52,52,53,53,53,53,53,53,53,54,54,54,54,54,54,55,55,55,55,55,55,56,56,56,56,56,56,57,57,57,57,57,57)
sample = c("S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S13T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S13T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S13T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S13T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S13T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S13T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S13T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S13T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S13T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S01T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0","S02T0","S03T0","S07T0","S09T0","S10T0","S12T0")
gene =c(24.1215870,-18.8771658,-27.3747309,-41.5740199,26.1561877,-2.7836332,20.8322796,36.5745088,-24.1541743,-11.2362216,4.9042852,7.4230219,155.8663563,16.4465366,-11.7982286,-1.6102783,-35.9559091,27.7909495,-13.9181661,-29.6037658,-68.4297261,-45.0877920,-48.3157529,17.1649982,-26.9084544,19.7358439,-5.8991143,-24.1541743,-23.5960654,13.0780939,-2.7836332,18.6394081,-28.3157487,-49.9186269,-33.7086648,41.6864242,-30.6199654,36.1823804,-36.5745088,-49.9186269,-44.9448864,-4.9042852,-34.3314764,62.3465425,-42.7609951,-11.7982286,-32.2055657,-56.1811080,5.7216661,-17.6296771,4.3857431,-43.6534459,9.6616697,-44.9448864,18.7997599,-12.9902884,109.1064494,7.6750504,-43.6534459,-17.7130611,-25.8433097,5.7216661,-18.5575548,35.2750175,36.1823804,2.3596457,-25.7644526,-55.0574858,15.5302365,-19.4854325,73.3687689,63.1668918,20.8322796,16.5175201,-22.5438960,-28.0905540,15.5302365,7.4230219,39.5062602,107.4657509,36.1823804,-23.5964573,-45.0877920,-43.8212642,4.0869043,-40.8266205,26.3375068,13.1572292,-25.9561030,-40.2569571,-52.8102415,2.4521426,-49.1775202,246.1047731,36.1823804,11.7982286,-35.4261223,-26.9669318,-2.4521426,-38.0429873,38.5656349,9.8679219,16.5175201,8.0513914,-42.6976421,26.9735686,-26.9084544,4.3857431,12.9780515,-32.2055657,-33.7086648,9.8085704,-36.2800196,215.7518511,6.5786146,-9.4385829,-19.3233394,-40.4503978,17.1649982,-7.4230219,14.2536650,-23.5964573,-53.1391834,-52.8102415,22.0692834,-54.7447866,24.1215870,-44.8332688,-24.1541743,-42.6976421,26.9735686,-40.8266205,191.1413737,17.5429723,-70.7893718,-37.0364006,-39.3267756,-4.9042852,-0.9278777,93.5198138,-6.5786146,-24.7762801,-28.9850091,-39.3267756,22.0692834,-50.1053979,14.2536650,23.5964573,-20.9336177,-53.9338637,14.7128556,-39.8987428,4.3857431,-64.8902575,-59.5802966,-33.7086648,22.0692834,2.7836332,46.0503024,-35.3946859,-43.4775137,-53.9338637,30.2430921,-34.3314764,80.3942259,28.5073300,-87.3068919,-24.1541743,-62.9228410,13.0780939,-25.0526990,35.0859447,-24.7762801,-38.6466789,-58.4283523,31.0604729,0.0000000,24.4562563,1.0964358,-27.1359259,-75.6830794,-16.8543324,20.4345217,-11.1345329,74.1390629,18.2282447,-27.3044720,-45.2890768,-46.7707724,15.3258912,-27.9523169,-6.9763039,117.3099418,18.6394081,-21.2368115,-38.6466789,-34.8322870,22.0692834,-48.2496425,6.5786146,-64.8902575,-51.5289052,-80.9007955,23.7040451,-26.9084544,223.1349942,8.7714862,10.6184058,-127.2119846,-31.4614205,0.8173809,-16.7017993,9.8679219,-35.3946859,-54.7494617,-44.9448864,14.7128556,-18.5575548,97.5827836,-166.3550237,-95.0064189,-123.5984376,104.6247509,-121.5519839,33.9895089,-44.8332688,-40.2569571,-56.1811080,51.4949946,0.0000000,-16.9312544,95.9808615,6.5786146,-21.2368115,-9.6616697,-13.4834659,10.6259513,-25.9805767,116.4895926,-1.0964358,-16.5175201,-56.3597400,-44.9448864,13.8954747,-12.9902884,-5.6437515,71.3703842,25.2180227,-41.2938002,-53.1391834,-32.5850426,8.9911895,12.9902884,31.9812582,1.0964358,-70.7893718,-33.8158440,-38.2031534,-15.5302365,-25.0526990,153.4053085,36.1823804,-34.2148630,-41.8672354,-19.1015767,22.8866643,0.9278777,20.8322796,-29.4955716,-43.4775137,-69.6645739,33.5126155,-45.4660092,26.3144585,-33.0350402,24.1541743,-42.6976421,0.0000000,-28.7642099,38.3752520,-7.0789372,-22.5438960,-20.2251989,34.3299964,19.4854325,4.3857431,-61.3507889,-33.8158440,-64.0464631,39.2342816,-28.7642099,183.7582306,-4.3857431,-22.4166344,-28.9850091,-57.3047302,25.3388069,-26.9084544,35.0859447,7.0789372,-33.8158440,-43.8212642,-1.6347617,5.5672664,-35.0859447,-40.1139773,-14.4925046,-12.3598438,21.2519025,-14.8460438,119.7709896,30.7002016,-22.4166344,-46.6980703,-43.8212642,5.7216661,-10.2066551,203.4466124,116.2221917,-83.7674233,-109.4989234,-38.2031534,78.4685632,-56.6005421,21.9287154,-63.7104346,-56.3597400,-4.4944886,25.3388069,-73.3023414,29.6037658,-31.8552173,-46.6980703,-79.7771734,21.2519025,-18.5575548,16.4465366,-27.1359259,-43.4775137,-41.5740199,-11.4433321,-23.1969435,27.4108943,-84.9472461,-53.1391834,-40.4503978,22.8866643,16.7017993)

---

tl；dr我认为你的问题在于每个个体在每个时间点都有完全相同的回答值（分数）（即个体内部的完全同质性），因此随机效应项完全解释了数据；固定效果已经没有了。您确定不想使用

gene

作为响应变量吗？？（经过一系列建模尝试后发现，通过绘制该死的数据，每个人都应该首先做的事情…）

不，那很好

让我们在lme4中试一试：

library(lme4)
m1 <- lmer(score ~ timepoint + gene + (1|sample), data=dd)
## Error in fn(x, ...) : Downdated VtV is not positive definite

lmer

可以工作——有点

m1sc <- lmer(score ~ timepoint + gene + (1|sample), data=ddsc)
## Warning message:
## In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
##   Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

在这一点上，我只能想到几个可能性：

• 实验设计中有一些东西意味着随机效应在某种程度上与一个或两个固定效应完全混淆
• 这些都是人工构造的模拟数据，完全平衡

---

啊哈

为了让R求解矩阵，它需要在计算上是可逆的。返回的错误告诉您，出于计算目的，您的矩阵是奇异的，这意味着它没有逆矩阵

由于这个错误更多地涉及统计理论方面，它可能更适合交叉验证。有关更多信息，请参阅

---

检查您的数据，确保您没有完全相关的自变量。

请参阅中的德克·埃德尔布埃特尔的答案。仅供参考，在发布问题之前用谷歌搜索R给你的错误信息是个好主意，因为通常情况下，你不是第一个遇到错误的人。@nrussell，这是真的，但这是一个不同的问题（我认为）@Ben Bolker你是对的，它们不是完全相同的问题+下面是一个非常彻底的答案。他们没有。但是他们的一个自变量（个体）和回答（分数）是完全相关的。这绝对是问题所在。我一定会非常彻底地更新你的答案。

library(lme4)
m1 <- lmer(score ~ timepoint + gene + (1|sample), data=dd)
## Error in fn(x, ...) : Downdated VtV is not positive definite

ddsc <- transform(dd,
     timepoint=scale(timepoint),
     gene=scale(gene))

m0sc <- lme(score ~ timepoint + gene, data=ddsc,
     random = ~1|sample)

m1sc <- lmer(score ~ timepoint + gene + (1|sample), data=ddsc)
## Warning message:
## In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
##   Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

## m1sc
## Linear mixed model fit by REML ['lmerMod']
## Formula: score ~ timepoint + gene + (1 | sample)
##    Data: ddsc
## REML criterion at convergence: -9062.721
## Random effects:
##  Groups   Name        Std.Dev. 
##  sample   (Intercept) 7.838e-01
##  Residual             3.344e-07
## Number of obs: 348, groups:  sample, 8
## Fixed Effects:
## (Intercept)    timepoint         gene  
##   5.714e+00   -4.194e-16   -1.032e-14  

library(ggplot2); theme_set(theme_bw())
ggplot(dd,aes(timepoint,score,group=sample,colour=gene))+
      geom_point(size=4)+
      geom_line(colour="red",alpha=0.5)