如何解决包 brms
我正在尝试对我的数据进行线性回归，以计算出海平面变化的速度。但是，简单的线性回归不起作用，因为我同时有 x（年龄）和 y（RSL）错误，例如：
我一直在做一些研究，看起来变量误差方法或贝叶斯测量模型都行得通https://www.r-bloggers.com/2021/04/how-to-estimate-models-with-measurement-error-for-our-covid-19-indices/
我决定从贝叶斯测量模型开始，因为作者将其描述为更有利且更易于实施的模型。
我尝试用我自己的数据复制他们的示例，但是我收到以下错误 Error: The following variables can neither be found in 'data' nor in 'data2': 'Wap'
有谁知道我哪里出错了以及如何让模型运行？
注意在我的数据框中，我有 Ageupper 和 Agelower 以及 RSLupper Rsllower 但它们是高斯的，所以我只在代码中使用 Ageupper RSLupper 等。
谢谢
## Load packages
library(brms)
### Load csv
Wap<-read.csv("Wapengo.csv",header=TRUE)
### Set errors
brms_formula<-bf(Wap~
me(RSL,RSLupper)+
me(Age,Ageupper),center=TRUE)+
set_mecor(FALSE)
### Run model
model <- brm(brms_formula,data=Wap,silent=0,chains=1,save_pars = save_pars(),iter=500,warmup=250,backend='rstan')
## Wap Data
structure(list(Site = structure(c(1L,1L,1L),.Label = "Wapengo",class = "factor"),RSL = c(-0.068238463,-0.073155693,-0.02581141,-0.017379805,-0.014178649,0.026706959),RSLupper = c(0.16795545,0.168146638,0.16916378,0.16951953,0.168921232,0.168238356),Rsllower = c(0.16795545,0.168238356
),Age = c(1832L,1860L,1881L,1894L,1906L,1913L),Ageupper = c(14.09253495,13.7156267,12.99997671,12.25404364,10.13081851,10.19587526
),Agelower = c(14.09253495,10.19587526),Rate = c(-0.037244426,-0.174854911,2.332632731,0.61776332,0.279128313,5.371978495)),row.names = c(NA,6L),class = "data.frame")
### Session info
> sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.16
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics Grdevices utils datasets methods base
other attached packages:
[1] brms_2.15.0 Rcpp_1.0.6 patchwork_1.1.1 dplyr_1.0.7 tidypaleo_0.1.1
[6] ggplot2_3.3.5
loaded via a namespace (and not attached):
[1] nlme_3.1-144 matrixStats_0.60.0 xts_0.12.1 threejs_0.3.3
[5] rstan_2.21.2 backports_1.1.7 tools_3.6.3 utf8_1.2.1
[9] R6_2.5.0 DT_0.13 DBI_1.1.0 mgcv_1.8-36
[13] projpred_2.0.2 colorspace_2.0-2 withr_2.4.2 prettyunits_1.1.1
[17] processx_3.4.5 tidyselect_1.1.1 gridExtra_2.3 brobdingnag_1.2-6
[21] curl_4.3 compiler_3.6.3 cli_2.5.0 shinyjs_2.0.0
[25] labeling_0.4.2 colourpicker_1.1.0 scales_1.1.1 dygraphs_1.1.1.6
[29] mvtnorm_1.1-1 callr_3.5.1 ggridges_0.5.2 StanHeaders_2.21.0-7
[33] stringr_1.4.0 digest_0.6.27 minqa_1.2.4 base64enc_0.1-3
[37] pkgconfig_2.0.3 htmltools_0.5.1.1 sessioninfo_1.1.1 lme4_1.1-23
[41] fastmap_1.1.0 htmlwidgets_1.5.3 rlang_0.4.11 rstudioapi_0.13
[45] shiny_1.6.0 farver_2.1.0 generics_0.1.0 jsonlite_1.7.2
[49] zoo_1.8-8 crosstalk_1.1.0.1 gtools_3.9.2 inline_0.3.19
[53] magrittr_2.0.1 loo_2.4.1 bayesplot_1.8.1 Matrix_1.2-18
[57] munsell_0.5.0 fansi_0.5.0 abind_1.4-5 lifecycle_1.0.0
[61] stringi_1.6.2 MASS_7.3-51.5 pkgbuild_1.2.0 plyr_1.8.6
[65] ggstance_0.3.4 grid_3.6.3 blob_1.2.1 parallel_3.6.3
[69] promises_1.1.0 Crayon_1.4.1 miniui_0.1.1.1 lattice_0.20-38
[73] splines_3.6.3 ps_1.5.0 pillar_1.6.1 igraph_1.2.6
[77] boot_1.3-24 markdown_1.1 shinystan_2.5.0 codetools_0.2-16
[81] reshape2_1.4.4 stats4_3.6.3 rstantools_2.1.1 glue_1.4.2
[85] V8_3.4.2 RcppParallel_5.1.4 vctrs_0.3.8 nloptr_1.2.2.1
[89] httpuv_1.6.1 gtable_0.3.0 purrr_0.3.4 tidyr_1.1.3
[93] assertthat_0.2.1 mime_0.9 xtable_1.8-4 coda_0.19-4
[97] later_1.0.0 rsconnect_0.8.18 tibble_3.1.2 shinythemes_1.2.0
[101] gamm4_0.2-6 statmod_1.4.34 ellipsis_0.3.2 bridgesampling_1.1-2
解决方法
我认为您需要的信息可以在这里找到：https://github.com/paul-buerkner/brms/issues/643
为了确保其他一切正常，我使用下面的代码拟合模型。我遇到了许多不同的转换，以及一些超过最大树深度的转换。我不确定您发布的数据是否是您的全部数据，但如果您遇到相同的问题，brms 和 Stan 警告消息通常非常有助于为您指明正确的方向。我也会考虑设置比这更好的先验，但模型本身的公式应该保持不变。
m1 <- brm(RSL | mi(RSLupper) ~ me(Age,Ageupper),data = Wap,save_mevars = TRUE)
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 dio@foxmail.com 举报，一经查实，本站将立刻删除。
