Last updated: 2021-01-14

Checks: 7 0

Knit directory: emlr_mod_v_XXX/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20200707) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 88db051. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  code/analysis_this_study.Rmd
    Untracked:  code/analysis_this_study_vs_model_truth.Rmd

Unstaged changes:
    Modified:   analysis/_site.yml
    Deleted:    analysis/analysis_this_study.Rmd
    Deleted:    analysis/analysis_this_study_vs_model_truth.Rmd
    Modified:   code/Workflowr_project_managment.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/config_parameterization_local.Rmd) and HTML (docs/config_parameterization_local.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 88db051 jens-daniel-mueller 2021-01-14 local rebuild after revision
html 8d032c3 jens-daniel-mueller 2021-01-14 Build site.
Rmd 8931f13 Donghe-Zhu 2021-01-13 rebuild web
html 022871c Donghe-Zhu 2021-01-13 Build site.
Rmd d44f36f Donghe-Zhu 2021-01-13 reorder analysis final
html 17dee1d jens-daniel-mueller 2021-01-13 Build site.
html a076226 Donghe-Zhu 2021-01-11 Build site.
Rmd 52eff18 Donghe-Zhu 2021-01-09 Implemet model_run and subsetting
html 7cdea0c jens-daniel-mueller 2021-01-06 Build site.
html fa85b93 jens-daniel-mueller 2021-01-06 Build site.
html e5cb81a Donghe-Zhu 2021-01-05 Build site.
Rmd 608cc45 Donghe-Zhu 2021-01-05 modification of analysis
html a499f10 Donghe-Zhu 2021-01-05 Build site.
Rmd 715bdb4 Donghe-Zhu 2021-01-02 model modification
html fb8a752 Donghe-Zhu 2020-12-23 Build site.
html 8fae0b2 Donghe-Zhu 2020-12-21 Build site.
html c8b76b3 jens-daniel-mueller 2020-12-19 Build site.
Rmd b5fedce jens-daniel-mueller 2020-12-19 first build after creating model template
Rmd 8e8abf5 Jens Müller 2020-12-18 Initial commit

1 Definition

The following local parametrisations (i.e. relevant for this sensitivity run) were defined to run the analysis:

# neutral density thresholds to cut the Atlantic ocean into slabs
slabs_Atl <-
  c(
    -Inf,
    26.00,
    26.50,
    26.75,
    27.00,
    27.25,
    27.50,
    27.75,
    27.85,
    27.95,
    28.05,
    28.10,
    28.15,
    28.20,
    Inf
  )

# neutral density thresholds to cut the Indo-Pacific ocean into slabs
slabs_Ind_Pac <-
  c(-Inf,
    26.00,
    26.50,
    26.75,
    27.00,
    27.25,
    27.50,
    27.75,
    27.85,
    27.95,
    28.05,
    28.10,
    Inf)

# Predictors for MLR model
MLR_predictors <- c(
                "sal",
                "temp",
                "aou",
                "oxygen",
                "silicate",
                "phosphate",
                "phosphate_star")

params_local <-
  lst(
    # model climate forcing with options variable ("AD") or constant ("CB")
    model_runs = "AD",
    # model subsetting with options of "GLODAP" or "random"
    subsetting = "GLODAP",
    # ID of current sensitivity run
    Version_ID = "v_XXX",
    # f flags accepted for GLODAP data
    flag_f = c(2),
    # qc flags accepted for GLODAP data
    flag_qc = c(1),
    # Should A16 cruise from 2013/14 be included in middle era (y/n)
    A16_GO_SHIP = "y",
    # Shallowest depth for data to be included in MLR fitting
    depth_min = 150,
    # Shallowest water depth for data to be included in MLR fitting
    bottomdepth_min = 0,
    # Lowest neutral density to map Cant with eMLR approach
    gamma_min = 26,
    # break years for eras, numbers indicate the upper end of the respective era
    era_breaks = c(1981, 1999, 2012, Inf),
    # ID for basins for MLR fits
    MLR_basins = "2",
    # Select the target variable for MLR, either "tco2", "cstar" or "cstar_tref"
    MLR_target = "cstar_tref",
    # see above
    MLR_predictors = MLR_predictors,
    # Maxmimum number of MLR predictors
    MLR_predictors_max = 5,
    # Minimum number of MLR predictors
    MLR_predictors_min = 2,
    # Total number of MLR fits taken into account
    MLR_number = 10,
    # Criterion to select best MLR fits, either "rmse" or "aic"
    MLR_criterion = "rmse",
    # see above
    slabs_Atl = slabs_Atl,
    # see above
    slabs_Ind_Pac = slabs_Ind_Pac,
    # Stoichiometric ratio of C to P
    rCP = 117,
    # Stoichiometric ratio of N to P
    rNP = 16,
    # Stoichiometric ratio of P to O (PO4* calculation)
    rPO = 170,
    # Offset P to O (PO4* calculation)
    rPO_offset = 1.95,
    # Preindustrial atmospheric pCO2
    preind_atm_pCO2 = 280,
    # generate a high number of diagnostic plots while running the analysis (y/n)
    plot_all_figures = "n"
  )

2 Write file

Parameterization criteria are locally stored and used throughout this sensitivty case.

params_local %>%
  write_rds(here::here("data/auxillary",
                       "params_local.rds"))

3 Create folders

Folders for each new sensitivity run are automatically created.


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] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2     purrr_0.3.4    
 [5] readr_1.4.0     tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.2  
 [9] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0 xfun_0.18        haven_2.3.1      colorspace_1.4-1
 [5] vctrs_0.3.5      generics_0.0.2   htmltools_0.5.0  yaml_2.2.1      
 [9] blob_1.2.1       rlang_0.4.9      later_1.1.0.1    pillar_1.4.7    
[13] withr_2.3.0      glue_1.4.2       DBI_1.1.0        dbplyr_1.4.4    
[17] modelr_0.1.8     readxl_1.3.1     lifecycle_0.2.0  munsell_0.5.0   
[21] gtable_0.3.0     cellranger_1.1.0 rvest_0.3.6      evaluate_0.14   
[25] knitr_1.30       httpuv_1.5.4     fansi_0.4.1      broom_0.7.2     
[29] Rcpp_1.0.5       promises_1.1.1   backports_1.1.10 scales_1.1.1    
[33] jsonlite_1.7.1   fs_1.5.0         hms_0.5.3        digest_0.6.27   
[37] stringi_1.5.3    rprojroot_2.0.2  grid_4.0.3       here_0.1        
[41] cli_2.1.0        tools_4.0.3      magrittr_1.5     crayon_1.3.4    
[45] whisker_0.4      pkgconfig_2.0.3  ellipsis_0.3.1   xml2_1.3.2      
[49] reprex_0.3.0     lubridate_1.7.9  assertthat_0.2.1 rmarkdown_2.5   
[53] httr_1.4.2       rstudioapi_0.13  R6_2.5.0         git2r_0.27.1    
[57] compiler_4.0.3