Last updated: 2021-03-23
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Map 3d fields of the predicted target variable (either tco2, cstar or cstar_tref) based on fitted MLRs and climatological fields of predictor variables. In contrast to the direct Cant calculation based on coefficient differences, we apply here the fitted coefficients directly. Cant could also be estimates as the differences between the target variable fields mapped here.
Currently, we use following combined predictor fields:
predictors <-
read_csv(paste(path_version_data,
"W18_st_G16_opsn.csv",
sep = ""))lm_best_target <-
read_csv(paste(path_version_data,
"lm_best_target.csv",
sep = ""))# remove predictor variable from model
lm_best_target <- lm_best_target %>%
mutate(model = str_remove(model, paste(params_local$MLR_target, "~ ")))
# join predictors and MLR
target <- full_join(predictors, lm_best_target)
rm(predictors, lm_best_target)target <- b_target_model(target)
target <- target %>%
select(lon,
lat,
depth,
era,
eras,
basin,
basin_AIP,
gamma,
params_local$MLR_target)target_average <- m_target_model_average(target)
target_average <- m_cut_gamma(target_average, "gamma")
rm(target)For each basin and era combination, the zonal mean of the target variable is calculated. Likewise, sd is calculated for the averaging of the mean basin fields.
target_average_zonal <- m_target_zonal_mean(target_average)
target_average_zonal <- m_cut_gamma(target_average_zonal, "gamma_mean")target_average %>%
write_csv(paste(path_version_data,
"target_3d.csv",
sep = ""))
target_average_zonal %>%
write_csv(paste(path_version_data,
"target_zonal.csv",
sep = ""))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] metR_0.9.0 scico_1.2.0 patchwork_1.1.1 collapse_1.5.0
[5] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[9] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
[13] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 here_0.1 lubridate_1.7.9
[4] lattice_0.20-41 assertthat_0.2.1 rprojroot_2.0.2
[7] digest_0.6.27 R6_2.5.0 cellranger_1.1.0
[10] backports_1.1.10 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.2 pillar_1.4.7 rlang_0.4.9
[16] readxl_1.3.1 data.table_1.13.2 rstudioapi_0.13
[19] whisker_0.4 blob_1.2.1 Matrix_1.2-18
[22] checkmate_2.0.0 rmarkdown_2.5 RcppEigen_0.3.3.7.0
[25] munsell_0.5.0 broom_0.7.2 compiler_4.0.3
[28] httpuv_1.5.4 modelr_0.1.8 xfun_0.18
[31] pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0
[34] fansi_0.4.1 crayon_1.3.4 dbplyr_1.4.4
[37] withr_2.3.0 later_1.1.0.1 grid_4.0.3
[40] jsonlite_1.7.1 gtable_0.3.0 lifecycle_0.2.0
[43] DBI_1.1.0 git2r_0.27.1 magrittr_1.5
[46] scales_1.1.1 cli_2.1.0 stringi_1.5.3
[49] fs_1.5.0 promises_1.1.1 RcppArmadillo_0.10.1.2.0
[52] xml2_1.3.2 ellipsis_0.3.1 generics_0.0.2
[55] vctrs_0.3.5 tools_4.0.3 glue_1.4.2
[58] hms_0.5.3 parallel_4.0.3 yaml_2.2.1
[61] colorspace_1.4-1 rvest_0.3.6 knitr_1.30
[64] haven_2.3.1