我们正在尝试在 R 包“brms”中建立具有空间自相关的结构方程模型。当我将其用于线性模型时，它效果非常好： NPP.model =bf(NPP~climate1+海拔+地形+sar(plot.nb,type ="lag")) NPP.lm=brm(NPP.model，data=myco.res，data2=list(plot.nb=plot.nb)，种子=13，iter=15000，核心=3，链=3，控制=列表(adapt_delta =0.98))
但是，当我们在结构方程模型中使用它时，它遇到了问题： NPP.model =bf(NPP~climate1+多样性+海拔+地形+sar(plot.nb,type ="lag")) 多样性.model=bf(多样性~气候1+海拔+地形+sar(plot.nb,type ="lag")) NPP.lm=brm(NPP.model+diversity.model++set_rescor(FALSE)，data=myco.res，data2=list(plot.nb=plot.nb)，seed =13，iter =15000，cores=3 ，链=3，控制=列表（adapt_delta=0.98））
程序有问题： 编译斯坦程序... stanc 中的错误（文件 = 文件，模型代码 = 模型代码，模型名称 = 模型名称，： 0 “string”中的语义错误，第 86 行，第 39 列到第 58 列：
84: vector[Kc_NPP] b_NPP; // regression coefficients
85: real Intercept_NPP; // temporary intercept for centered predictors
86: real<lower=min_eigenMsarNPP,upper=max_eigenMsarNPP> errorsar_NPP; // error-SAR correlation parameter
^
87: real<lower=0> sigma_NPP; // dispersion parameter
88: vector[Kc_diversity] b_diversity; // regression coefficients
标识符“max_eigenMsarNPP”不在范围内。您的意思是“max_eigenMsar_NPP”吗？
请帮助我们解决这个问题。
感谢您的快速回复。请参阅以下代码作为示例： 库（spdep） 图书馆（brms）
#reading data
plot.data=read.csv("sar.data.csv");
#set neighboring weights
plot.coord=data.frame(x=plot.data$x,y=plot.data$y);
coordinates(plot.coord)=c("x","y")
plot.nb=tri2nb(plot.coord)
mod.NPP=bf(log(NPP)~richness+convex+sar(plot.nb,type="lag"))
mod.richness=bf(richness~convex+sar(plot.nb,type="lag"))
sem_brms_NPP=brm(mod.NPP+mod.richness+set_rescor(FALSE), data=plot.data, seed=13,iter=15000,cores=3,chains=3,data2=list(plot.nb=plot.nb),control=list(adapt_delta=0.98))
Here are the data of the example:
x y NPP richness convex
25 25 2.8568483 1.645074569 0.551449376
25 75 3.684366858 1.251241256 -1.337039999
25 125 3.812329981 0.069741316 -0.378752143
25 175 3.684699962 0.069741316 -0.272764288
25 225 3.509855693 0.660491286 -0.932703142
25 275 2.513996864 1.448157912 -0.987947194
25 325 2.74408847 1.841991226 -1.056747372
25 375 3.086323335 -0.521008654 -0.05671217
75 25 3.021667807 0.857407942 -0.947907831
75 75 3.505487797 1.645074569 -1.603287099
75 125 3.623973839 0.660491286 -1.586534176
75 175 5.670239667 -0.324091997 -0.964366237
75 225 3.420870045 -1.308675281 -0.765198026
75 275 8.268396407 -0.324091997 -0.159296043
75 325 4.128392893 -0.127175341 0.153874742
75 375 5.269113443 0.266657973 0.531723429
125 25 4.827352586 -0.914841967 1.404480423
125 75 5.268117927 -1.308675281 0.751703044
125 125 3.246826461 -0.127175341 0.591023624
125 175 4.586711654 -0.914841967 0.953414278
125 225 5.193110239 -1.702508594 0.806861658
125 275 4.240922052 -0.717925311 0.876459776
125 325 5.349005287 -1.111758624 1.142539619
125 375 5.79756553 -2.293258564 1.054840332
175 25 2.959063217 1.251241256 -1.663552086
175 75 5.153407404 -0.717925311 -0.011391671
175 125 5.361819719 -0.717925311 1.518184632
175 175 3.854002855 -1.111758624 0.532527229
175 225 2.694341396 0.463574629 -0.616505954
175 275 3.116381287 -0.127175341 0.152850644
175 325 3.096591984 0.069741316 0.410093912
175 375 4.005730227 -0.521008654 -0.803968288
225 25 4.878031569 1.645074569 0.000571537
225 75 3.775624482 1.054324599 -1.896424687
225 125 5.294353953 -0.324091997 -0.605502025
225 175 4.170131345 1.645074569 -0.824918419
225 225 7.402201938 -0.717925311 -0.914107682
225 275 7.096840757 -0.717925311 0.634250503
225 325 5.74439573 0.857407942 0.52751286
225 375 5.499097105 -0.717925311 2.308275987
275 25 6.220425675 0.266657973 1.59767398
275 75 2.400888897 1.251241256 -1.838173159
275 125 3.151753202 0.857407942 -2.021730195
275 175 5.106932843 1.054324599 -1.16256155
275 225 6.245960869 0.463574629 -0.838709922
275 275 5.682026301 -0.324091997 0.450291841
275 325 6.073772029 -0.521008654 0.847852698
275 375 3.697971779 0.266657973 -0.612746778
325 25 5.584248255 -1.702508594 2.293159011
325 75 5.874086696 1.054324599 0.097973551
325 125 4.930650129 -0.127175341 -0.756119465
325 175 4.605712213 0.857407942 -0.535799465
325 225 4.314188238 1.054324599 -1.368537483
325 275 4.042674268 0.857407942 -1.087210637
325 325 7.697667826 0.463574629 -0.344297714
325 375 5.45130729 -1.505591937 0.236676688
375 25 5.23767512 -0.717925311 -1.165736981
375 75 5.442370489 -1.702508594 1.734883277
375 125 4.153752117 -0.521008654 0.3688539
375 175 6.374074782 -0.521008654 0.537166354
375 225 4.361307428 0.463574629 0.748832645
375 275 4.600662313 1.251241256 -0.192271925
375 325 5.080090968 0.266657973 -0.063292719
375 375 2.909932186 -1.111758624 -1.278466557
425 25 4.692823205 -1.111758624 1.289452769
425 75 6.153898227 -0.717925311 1.081077307
425 125 4.948797315 -0.521008654 0.823857118
425 175 6.372011398 -0.914841967 0.83894609
425 225 8.226823544 -0.324091997 0.767451459
425 275 6.222034842 0.463574629 0.737614124
425 325 4.311067676 -1.505591937 1.042908587
425 375 4.263529094 -1.899425251 1.49031374
475 25 3.826527987 0.660491286 0.157430715
475 75 4.919966251 0.266657973 -0.500063723
475 125 4.09028079 -1.308675281 0.297725268
475 175 4.179978887 -0.324091997 0.121092474
475 225 5.524244786 -0.324091997 -0.408000105
475 275 5.674296718 0.463574629 0.946985094
475 325 3.954364867 0.660491286 0.520687853
475 375 4.14473555 -0.914841967 -0.43683558
525 25 3.634319813 0.266657973 1.667616148
525 75 3.428459518 -1.308675281 0.436487489
525 125 5.232819421 -0.914841967 -0.117766758
525 175 3.20637336 0.463574629 -0.101015395
525 225 5.627480433 0.069741316 0.163543884
525 275 4.498335733 -0.717925311 1.000020217
525 325 3.242990092 2.235824539 0.137041247
525 375 4.707353169 -1.308675281 0.751381917
575 25 3.861840468 2.038907882 0.686154178
575 75 2.557074157 0.857407942 -0.90534462
575 125 4.12845393 0.266657973 -0.12855679
575 175 4.266981397 0.266657973 0.818059117
575 225 4.369247136 0.660491286 -0.141970395
575 275 3.192727925 1.448157912 -1.464466616
575 325 2.999182439 0.266657973 -1.592244015
575 375 4.063909442 1.054324599 -2.144303272