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，简单的线性回归将不起作用，因为我同时有 x（年龄）和 y（RSL）错误，例如：
| 限制性物质清单 | RSL 错误 | 年龄 | 年龄错误 |
|---|---|---|---|
| -0.31 | 0.05 | 1815 | 1 |
| -0.29 | 0.07 | 1880 | 5 |
| -0.29 | 0.05 | 1895年 | 5 |
| -0.2 | 0.05 | 1935年 | 1 |
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可以使用https://www.r-bloggers.com/2021/04/how-to-estimate-models-with -我们的 covid-19 指数的测量误差/
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现的模型。
我试图用我自己的数据复制他们的例子但是我得到了以下错误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, 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,
0.168146638, 0.16916378, 0.16951953, 0.168921232, 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, 13.7156267, 12.99997671, 12.25404364,
10.13081851, 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