Last updated: 2021-03-04

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.

R Markdown file: up-to-date

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.

Environment: empty

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.

Seed: set.seed(20200707)

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.

Session information: recorded

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

Cache: none

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

File paths: relative

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

Repository version: 698d743

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 698d743. 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/

Unstaged changes:
    Modified:   data/auxillary/params_local.rds

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/eMLR_model_fitting.Rmd) and HTML (docs/eMLR_model_fitting.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
html e2a5a33 Donghe-Zhu 2021-03-04 Build site.
html c7892c1 Donghe-Zhu 2021-03-04 Build site.
html 924430b Donghe-Zhu 2021-03-03 Build site.
html 0d0bca1 Donghe-Zhu 2021-03-03 Build site.
html cb63c16 Donghe-Zhu 2021-03-03 Build site.
html ffda45a Donghe-Zhu 2021-03-03 Build site.
html 691ba81 Donghe-Zhu 2021-03-03 Build site.
html c5e45a2 Donghe-Zhu 2021-03-03 Build site.
html 89c3e58 Donghe-Zhu 2021-03-03 Build site.
html c407a50 Donghe-Zhu 2021-03-03 Build site.
html c911669 Donghe-Zhu 2021-03-03 Build site.
Rmd e1f9d21 Donghe-Zhu 2021-03-03 local rebuild
html b71c719 Donghe-Zhu 2021-03-01 Build site.
html 13666ca Donghe-Zhu 2021-03-01 Build site.
Rmd c3195f4 Donghe-Zhu 2021-03-01 local rebuild
html c6e60fe Donghe-Zhu 2021-03-01 Build site.
Rmd 8ef147d Donghe-Zhu 2021-03-01 local rebuild
html 7a388f7 Donghe-Zhu 2021-03-01 Build site.
html 799e913 Donghe-Zhu 2021-03-01 Build site.
Rmd aa780a7 Donghe-Zhu 2021-03-01 local rebuild
html 66ff99f Donghe-Zhu 2021-03-01 Build site.
html ac9bb7a Donghe-Zhu 2021-02-28 Build site.
html efdc047 Donghe-Zhu 2021-02-28 Build site.
html e9a7418 Donghe-Zhu 2021-02-28 Build site.
Rmd eac1b0e Donghe-Zhu 2021-02-28 local rebuild
html e152917 Donghe-Zhu 2021-02-28 Build site.
html feb991c Donghe-Zhu 2021-02-27 Build site.
Rmd 4563314 Donghe-Zhu 2021-02-27 local rebuild
html 287123c Donghe-Zhu 2021-02-27 Build site.
Rmd 764a9c2 Donghe-Zhu 2021-02-27 local rebuild
html 54d5b5b Donghe-Zhu 2021-02-27 Build site.
Rmd 2e836bf Donghe-Zhu 2021-02-27 local rebuild
html 330f064 Donghe-Zhu 2021-02-27 Build site.
Rmd f360282 Donghe-Zhu 2021-02-27 local rebuild
html adbc9bc Donghe-Zhu 2021-02-27 Build site.
Rmd a8a8f9c Donghe-Zhu 2021-02-27 local rebuild
html 5937141 Donghe-Zhu 2021-02-27 Build site.
Rmd 5f0bfca Donghe-Zhu 2021-02-27 local rebuild
html 4414bbf Donghe-Zhu 2021-02-27 Build site.
html a265efb Donghe-Zhu 2021-02-27 Build site.
html 19edd1e Donghe-Zhu 2021-02-27 Build site.
Rmd 1ae5bb4 Donghe-Zhu 2021-02-27 local rebuild
html f20483f Donghe-Zhu 2021-02-26 Build site.
html 6a2c7b3 Donghe-Zhu 2021-02-25 Build site.
html 02b976d Donghe-Zhu 2021-02-24 Build site.
html 354c224 Donghe-Zhu 2021-02-24 Build site.
Rmd d910d39 Donghe-Zhu 2021-02-24 local rebuild
html 1a0a88a Donghe-Zhu 2021-02-24 Build site.
Rmd 663e891 Donghe-Zhu 2021-02-24 local rebuild
html 57f701e Donghe-Zhu 2021-02-24 Build site.
html 06f3149 Donghe-Zhu 2021-02-16 Build site.
html 401eab3 Donghe-Zhu 2021-02-15 Build site.
html e3bba84 Donghe-Zhu 2021-02-15 Build site.
html 5dce4b1 Donghe-Zhu 2021-02-15 Build site.
Rmd 35b0f2e Donghe-Zhu 2021-02-15 local rebuild
html 4469a0c Donghe-Zhu 2021-02-13 Build site.
Rmd 8186d57 Donghe-Zhu 2021-02-10 apply nr_obs
html 5ae6a69 Donghe-Zhu 2021-02-10 Build site.
Rmd eeb6557 Donghe-Zhu 2021-02-10 local rebuild
html 05385dc Donghe-Zhu 2021-02-10 Build site.
html f791ae4 Donghe-Zhu 2021-02-09 Build site.
html f71ae34 Donghe-Zhu 2021-02-09 Build site.
html c011832 Donghe-Zhu 2021-02-09 Build site.
html a145fa7 Donghe-Zhu 2021-02-09 Build site.
Rmd 37a41c8 Donghe-Zhu 2021-02-09 local rebuild
html c344e42 Donghe-Zhu 2021-02-08 Build site.
Rmd 8f5fa79 Donghe-Zhu 2021-02-08 local rebuild
html 2f095d7 Donghe-Zhu 2021-02-07 Build site.
html 2305044 Donghe-Zhu 2021-02-07 Build site.
Rmd c3638c1 Donghe-Zhu 2021-02-07 local rebuild
html 1fad5f1 Donghe-Zhu 2021-02-07 Build site.
html ca03c39 Donghe-Zhu 2021-02-07 Build site.
html e2ffc14 Donghe-Zhu 2021-02-05 Build site.
html cd7c52c Donghe-Zhu 2021-02-04 Build site.
Rmd bcf91a8 jens-daniel-mueller 2021-02-04 separate MLR tables, added plot residuals vs location
html bcf84f4 Donghe-Zhu 2021-02-02 Build site.
html a518739 Donghe-Zhu 2021-02-01 Build site.
html 61666de Donghe-Zhu 2021-01-31 Build site.
html 865b582 Donghe-Zhu 2021-01-31 Build site.
html 3e68089 Donghe-Zhu 2021-01-31 Build site.
html ecf335c Donghe-Zhu 2021-01-31 Build site.
html a618965 Donghe-Zhu 2021-01-31 Build site.
html 59e006e Donghe-Zhu 2021-01-31 Build site.
html a1c8f87 Donghe-Zhu 2021-01-31 Build site.
html ae5c18f Donghe-Zhu 2021-01-31 Build site.
html b50fe52 Donghe-Zhu 2021-01-31 Build site.
Rmd ac99ae5 jens-daniel-mueller 2021-01-29 code review
html ac99ae5 jens-daniel-mueller 2021-01-29 code review
html b5bdcaf Donghe-Zhu 2021-01-29 Build site.
Rmd b234505 Donghe-Zhu 2021-01-29 MLR approach across all depth
html 442010d Donghe-Zhu 2021-01-29 Build site.
Rmd e67e7dd Donghe-Zhu 2021-01-29 surface equilibrium approach across all latitudes irrespective of gamma
html 372adf5 Donghe-Zhu 2021-01-29 Build site.
html af8788e Donghe-Zhu 2021-01-29 Build site.
html 21c91c9 Donghe-Zhu 2021-01-29 Build site.
html eded038 Donghe-Zhu 2021-01-29 Build site.
html 541d4dd Donghe-Zhu 2021-01-29 Build site.
html 6a75576 Donghe-Zhu 2021-01-28 Build site.
html 16fba40 Donghe-Zhu 2021-01-28 Build site.
Rmd aecf0c6 Donghe-Zhu 2021-01-28 diagnostic ploting
Rmd a02684e Donghe-Zhu 2021-01-28 error
Rmd 4584be9 Donghe-Zhu 2021-01-28 latest
html 12bc567 Donghe-Zhu 2021-01-27 Build site.
html ceed31b Donghe-Zhu 2021-01-27 Build site.
html 342402d Donghe-Zhu 2021-01-27 Build site.
html 5bad5c2 Donghe-Zhu 2021-01-27 Build site.
Rmd c2c9529 Donghe-Zhu 2021-01-27 random subsetting based on lat
html 61efb56 Donghe-Zhu 2021-01-25 Build site.
html 48f638e Donghe-Zhu 2021-01-25 Build site.
html c1cec47 Donghe-Zhu 2021-01-25 Build site.
html 05ffb0c Donghe-Zhu 2021-01-25 Build site.
html 8b97165 Donghe-Zhu 2021-01-25 Build site.
html c569946 Donghe-Zhu 2021-01-24 Build site.
html a2f0d56 Donghe-Zhu 2021-01-23 Build site.
html 28509fc Donghe-Zhu 2021-01-23 Build site.
html 4c28e4a Donghe-Zhu 2021-01-22 Build site.
html 24cc264 jens-daniel-mueller 2021-01-22 cleaned /docs before creating copies
html 88eb28f Donghe-Zhu 2021-01-21 Build site.
html 2679490 Donghe-Zhu 2021-01-21 Build site.
html 7891955 Donghe-Zhu 2021-01-21 Build site.
html d4cf1cb Donghe-Zhu 2021-01-21 Build site.
Rmd 167eeec Donghe-Zhu 2021-01-21 surface DIC calculation with atmospheric equilibrium option
html 1f3e5b6 jens-daniel-mueller 2021-01-20 Build site.
html 0e7bdf1 jens-daniel-mueller 2021-01-15 cleaning template repository
html 73cbef3 jens-daniel-mueller 2021-01-15 Build site.
html 4571843 jens-daniel-mueller 2021-01-14 revision and html deleted for template copying
html 23151cd jens-daniel-mueller 2021-01-14 Build site.
html b3564aa jens-daniel-mueller 2021-01-14 Build site.
html 8d032c3 jens-daniel-mueller 2021-01-14 Build site.
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.
Rmd 9e04fd7 jens-daniel-mueller 2021-01-13 local rebuild after revision
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.
Rmd b5934dd jens-daniel-mueller 2021-01-06 local rebuild after revision
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.
Rmd 82e3c9c Donghe-Zhu 2020-12-23 first build after creating model template
html 8fae0b2 Donghe-Zhu 2020-12-21 Build site.
Rmd 00a1322 Donghe-Zhu 2020-12-21 first build after creating model template
Rmd d73ae35 Donghe-Zhu 2020-12-21 first version with lm error
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 Required data

Required are:

  • cleaned and prepared GLODAP-based synthetic model subsetting file
GLODAP <-
  read_csv(paste(path_version_data,
                 "GLODAPv2.2020_MLR_fitting_ready.csv",
                 sep = ""))

2 Predictor combinations

Find all possible combinations of following considered predictor variables:

  • sal, temp, aou, nitrate, silicate, phosphate, phosphate_star
# the following code is a workaround to find all predictor combinations
# using the olsrr package and fit all models for one era, slab, and basin

i_basin <- unique(GLODAP$basin)[1]
i_era   <- unique(GLODAP$era)[1]

# subset one basin and era for fitting
GLODAP_basin_era <- GLODAP %>%
  filter(basin == i_basin, era == i_era)

i_gamma_slab <- unique(GLODAP_basin_era$gamma_slab)[1]
print(i_gamma_slab)

# subset one gamma slab
GLODAP_basin_era_slab <- GLODAP_basin_era %>%
  filter(gamma_slab == i_gamma_slab)

# fit the full linear model, i.e. all predictor combinations
lm_full <- lm(paste(
  params_local$MLR_target,
  paste(params_local$MLR_predictors, collapse = " + "),
  sep = " ~ "
),
data = GLODAP_basin_era_slab)

# fit linear models for all possible predictor combinations
# unfortunately, this functions does not provide model coefficients (yet)
lm_all <- ols_step_all_possible(lm_full)

# convert to tibble
lm_all <- as_tibble(lm_all)

# extract relevant columns and format model formula
lm_all <- lm_all %>% 
  select(n, predictors) %>% 
  mutate(lm_coeff = str_replace_all(predictors, " ", " + "),
         lm_coeff = paste(params_local$MLR_target, "~", lm_coeff))

# remove certain predictor combinations
# lm_rm_ph <- lm_all %>%
#   filter(str_detect(lm_coeff, "phosphate_star")) %>%
#   mutate(lm_coeff_filter = str_remove(lm_coeff, "phosphate_star")) %>%
#   filter(
#     str_detect(lm_coeff_filter, "oxygen") &
#       str_detect(lm_coeff_filter, "phosphate")
#   )

# lm_rm_si <- lm_all %>%
#   filter(str_detect(lm_coeff, "silicate_star")) %>%
#   mutate(lm_coeff_filter = str_remove(lm_coeff, "silicate_star")) %>%
#   filter(str_detect(lm_coeff_filter, "silicate"))

# lm_rm_o2 <- lm_all %>%
#   filter(str_detect(lm_coeff, "phosphate_star")) %>%
#   mutate(lm_coeff_filter = str_remove(lm_coeff, "phosphate_star")) %>%
#   filter(
#     str_detect(lm_coeff_filter, "phosphate") &
#       str_detect(lm_coeff_filter, "oxygen")
#   )

# lm_rm <- bind_rows(lm_rm_ph, lm_rm_o2) %>%
#   select(-lm_coeff_filter) %>%
#   unique()


# remove temp sal predictor combination
lm_all <- lm_all %>%
  # filter(!(
  #   str_detect(lm_coeff, "temp") &
  #     str_detect(lm_coeff, "phosphate_star")
  # )) %>%
  mutate(lm_coeff_filter = str_remove(lm_coeff, "phosphate_star")) %>%
  filter(!(str_detect(lm_coeff_filter, "nitrate") &
             str_detect(lm_coeff_filter, "phosphate")
  )) %>%
  filter(!(
    str_detect(lm_coeff_filter, "temp") &
      str_detect(lm_coeff_filter, "sal")
  )) %>%
  filter(!(
    str_detect(lm_coeff_filter, "oxygen") &
      str_detect(lm_coeff_filter, "aou")
  )) %>%
  select(-lm_coeff_filter)

# lm_rm <- lm_rm_ph %>%
#   select(-lm_coeff_filter) %>%
#   unique()
# 
# lm_all <- anti_join(lm_all, lm_rm)

# remove helper objects
rm(
  i_gamma_slab,
  i_era,
  i_basin,
  GLODAP_basin_era,
  GLODAP_basin_era_slab,
  lm_full,
  lm_rm_ph,
  lm_rm_si,
  lm_rm_o2,
  lm_rm
)

3 Apply predictor threshold

Select combinations with a total number of predictors in the range:

  • Minimum: 3
  • Maximum: 9
lm_all <- lm_all %>% 
  filter(n >= params_local$MLR_predictors_min,
         n <= params_local$MLR_predictors_max)

This results in a total number of MLR models of:

  • 45

4 Fit all models

Individual linear regression models were fitted for the chosen target variable:

  • cstar_tref

as a function of each predictor combination. Fitting was performed separately within each basin, era, and slab. Model diagnostics, such as the root mean squared error (RMSE), were calculated for each fitted model.

# loop across all basins, era, gamma slabs, and MLRs
# fit all MLR models
for (i_basin in unique(GLODAP$basin)) {
  for (i_era in unique(GLODAP$era)) {
    #i_basin <- unique(GLODAP$basin)[1]
    #i_era   <- unique(GLODAP$era)[1]
    print(i_basin)
    print(i_era)
    
    GLODAP_basin_era <- GLODAP %>%
      filter(basin == i_basin, era == i_era)
    
    for (i_gamma_slab in unique(GLODAP_basin_era$gamma_slab)) {
      #i_gamma_slab <- unique(GLODAP_basin_era$gamma_slab)[1]
      print(i_gamma_slab)
      
      GLODAP_basin_era_slab <- GLODAP_basin_era %>%
        filter(gamma_slab == i_gamma_slab)
      
      # number of observations used for each fitting model
      i_nr_obs = nrow(GLODAP_basin_era_slab)
      
      for (i_predictors in unique(lm_all$predictors)) {
        #i_predictors <- unique(lm_all$predictors)[1]
        
        # extract one model definition
        i_lm <- lm_all %>%
          filter(predictors == i_predictors) %>%
          select(lm_coeff) %>%
          pull()
        
        # extract number of predictors
        i_n_predictors <- lm_all %>%
          filter(predictors == i_predictors) %>%
          select(n) %>%
          pull()
        
        if (i_nr_obs > i_n_predictors) {
          # fit model
          if (params_local$MLR_type == "rlm") {
            i_lm_fit <- MASS::rlm(as.formula(i_lm),
                                  data = GLODAP_basin_era_slab)
          }
          
          if (params_local$MLR_type == "lm") {
            i_lm_fit <- lm(as.formula(i_lm),
                           data = GLODAP_basin_era_slab)
          }
          
          # find max predictor correlation
          i_cor_max <- GLODAP_basin_era_slab %>%
            select(!!!syms(str_split(i_predictors, " ",
                                     simplify = TRUE))) %>%
            correlate(quiet = TRUE) %>%
            select(-term) %>%
            abs() %>%
            max(na.rm = TRUE)
          
          # calculate root mean squared error
          i_rmse <- sqrt(c(crossprod(i_lm_fit$residuals)) /
                           length(i_lm_fit$residuals))
          
          # calculate maximum residual
          i_resid_max <- max(abs(i_lm_fit$residuals))
          
          # calculate Akaike information criterion aic
          i_aic <- AIC(i_lm_fit)
          
          # collect model coefficients and diagnostics
          coefficients <- tidy(i_lm_fit)
          
          coefficients <- coefficients %>%
            mutate(
              basin = i_basin,
              era = i_era,
              gamma_slab = i_gamma_slab,
              model = i_lm,
              nr_obs = i_nr_obs,
              rmse = i_rmse,
              aic = i_aic,
              resid_max = i_resid_max,
              n_predictors = i_n_predictors,
              na_predictor = anyNA(coefficients$estimate),
              cor_max = i_cor_max
            )
          
          if (exists("lm_all_fitted")) {
            lm_all_fitted <- bind_rows(lm_all_fitted, coefficients)
          }
          
          if (!exists("lm_all_fitted")) {
            lm_all_fitted <- coefficients
          }
        }
      }
    }
  }
}

rm(
  i_lm_fit,
  coefficients,
  i_rmse,
  GLODAP_basin_era,
  GLODAP_basin_era_slab,
  i_lm,
  i_basin,
  i_era,
  i_gamma_slab,
  i_nr_obs,
  i_predictors,
  i_aic,
  i_n_predictors,
  i_resid_max
)

5 Prepare coeffcients

Coefficients are prepared for the mapping of Cant and the chosen target variable.

5.1 Formatting

# select relevant columns
lm_all_fitted <- lm_all_fitted %>%
  select(
    basin,
    gamma_slab,
    era,
    model,
    nr_obs,
    n_predictors,
    term,
    estimate,
    rmse,
    aic,
    resid_max,
    na_predictor,
    cor_max
  )

# set coefficient to zero if not fitted (=NA)
lm_all_fitted <- lm_all_fitted %>%
  mutate(estimate = if_else(is.na(estimate), 0, estimate))

# Prepare model coefficients for mapping of target variable
lm_all_fitted_wide <- lm_all_fitted %>%
  pivot_wider(
    values_from = estimate,
    names_from = term,
    names_prefix = "coeff_",
    values_fill = 0
  )

5.2 Predictor selection

Within each basin and slab, the following number of best linear regression models was selected:

  • 5

The criterion used to select the best models was:

  • aic

The criterion was summed up for two adjacent eras, and the models with lowest summed values were selected.

Please note, that currently the lm() function produces NAs for some predictors. It is not yet entirely clear when this happens, but presumably it is caused by some form of collinearity between predictors, such that including another predictor does not help to explain the target variable any better. The issues also expresses as exactly identical rmse values of different models. As an interim solution, models with fitted NA predictors were not included.

# remove models with predictors fitted as NA

lm_all_fitted_wide <- lm_all_fitted_wide %>%
  filter(na_predictor == FALSE)
# calculate RMSE sum for adjacent eras
lm_all_fitted_wide_eras <- lm_all_fitted_wide  %>%
  select(basin, gamma_slab, model, era, nr_obs, rmse, aic, resid_max) %>%
  arrange(era) %>%
  group_by(basin, gamma_slab, model) %>%
  mutate(
    eras = paste(lag(era), era, sep = " --> "),
    rmse_sum = rmse + lag(rmse),
    aic_sum = aic + lag(aic)
  ) %>%
  ungroup() %>%
  select(-c(era)) %>%
  drop_na()

# subset models with lowest summed criterion
# chose which criterion is applied

if (params_local$MLR_criterion == "aic") {
  lm_best <- lm_all_fitted_wide_eras %>%
    group_by(basin, gamma_slab, eras) %>%
    slice_min(order_by = aic_sum,
              with_ties = FALSE,
              n = params_local$MLR_number) %>%
    ungroup() %>%
    arrange(basin, gamma_slab, eras, model)
} else {
  lm_best <- lm_all_fitted_wide_eras %>%
    group_by(basin, gamma_slab, eras) %>%
    slice_min(order_by = rmse_sum,
              with_ties = FALSE,
              n = params_local$MLR_number) %>%
    ungroup() %>%
    arrange(basin, gamma_slab, eras, model)
}

5.3 RMSE tables

5.3.1 per model

lm_best %>%
  kable() %>%
  add_header_above() %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "400px")
basin gamma_slab model nr_obs rmse aic resid_max eras rmse_sum aic_sum
Atlantic (-Inf,26] cstar_tref ~ sal + aou + nitrate + phosphate_star 102 1.5396084 389.4952 5.056734 1996-2003 –> 2004-2011 3.0837535 738.8156
Atlantic (-Inf,26] cstar_tref ~ sal + aou + phosphate + phosphate_star 102 0.9376198 288.3237 2.991891 1996-2003 –> 2004-2011 1.9577692 562.2012
Atlantic (-Inf,26] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 102 0.9371346 290.2181 2.956722 1996-2003 –> 2004-2011 1.9565786 565.9697
Atlantic (-Inf,26] cstar_tref ~ temp + aou + phosphate + phosphate_star 102 1.3319903 359.9450 5.203741 1996-2003 –> 2004-2011 2.7827378 697.9102
Atlantic (-Inf,26] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 102 1.3215255 360.3360 4.920476 1996-2003 –> 2004-2011 2.7715589 700.2115
Atlantic (-Inf,26] cstar_tref ~ sal + aou + phosphate + phosphate_star 82 1.2840476 285.7088 3.468902 2004-2011 –> 2012-2019 2.2216674 574.0324
Atlantic (-Inf,26] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 82 1.2129111 278.3618 3.397844 2004-2011 –> 2012-2019 2.1500458 568.5798
Atlantic (-Inf,26] cstar_tref ~ sal + aou + silicate + phosphate_star 82 1.7068972 332.3930 4.683081 2004-2011 –> 2012-2019 3.2552533 723.0440
Atlantic (-Inf,26] cstar_tref ~ temp + aou + phosphate + phosphate_star 82 1.5524482 316.8386 5.853442 2004-2011 –> 2012-2019 2.8844385 676.7836
Atlantic (-Inf,26] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 82 1.4983606 313.0229 5.297641 2004-2011 –> 2012-2019 2.8198861 673.3588
Atlantic (26,26.5] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 664 3.6393864 3613.8808 11.876622 1996-2003 –> 2004-2011 7.0138838 6990.3768
Atlantic (26,26.5] cstar_tref ~ temp + aou + nitrate + phosphate_star 664 4.2900486 3830.3142 10.720670 1996-2003 –> 2004-2011 8.2844909 7420.0172
Atlantic (26,26.5] cstar_tref ~ temp + aou + nitrate + silicate 664 4.0977833 3769.4229 15.110053 1996-2003 –> 2004-2011 7.9771673 7321.8315
Atlantic (26,26.5] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 664 4.0831077 3766.6583 16.751780 1996-2003 –> 2004-2011 7.9607012 7320.4779
Atlantic (26,26.5] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 664 4.4059507 3867.7160 11.444175 1996-2003 –> 2004-2011 8.4710149 7481.7817
Atlantic (26,26.5] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 642 3.5760088 3472.0506 11.057217 2004-2011 –> 2012-2019 7.2153952 7085.9314
Atlantic (26,26.5] cstar_tref ~ temp + aou + nitrate + phosphate_star 642 4.0632818 3634.0735 12.196928 2004-2011 –> 2012-2019 8.3533304 7464.3877
Atlantic (26,26.5] cstar_tref ~ temp + aou + nitrate + silicate 642 3.8774323 3573.9594 13.739832 2004-2011 –> 2012-2019 7.9752156 7343.3823
Atlantic (26,26.5] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 642 3.8490724 3566.5336 14.540291 2004-2011 –> 2012-2019 7.9321800 7333.1919
Atlantic (26,26.5] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 642 4.1762163 3671.2739 10.819553 2004-2011 –> 2012-2019 8.5821670 7538.9899
Atlantic (26.5,26.75] cstar_tref ~ aou + silicate + phosphate + phosphate_star 882 3.3994981 4673.4870 10.653347 1996-2003 –> 2004-2011 6.4906555 8797.1264
Atlantic (26.5,26.75] cstar_tref ~ sal + aou + silicate + phosphate 882 3.3572772 4651.4414 10.088371 1996-2003 –> 2004-2011 6.5269471 8815.5629
Atlantic (26.5,26.75] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 882 3.3157636 4631.4931 10.441468 1996-2003 –> 2004-2011 6.3671600 8736.2372
Atlantic (26.5,26.75] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 882 3.3609199 4655.3543 11.640723 1996-2003 –> 2004-2011 6.5034032 8807.5727
Atlantic (26.5,26.75] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 882 3.2766498 4610.5607 10.002822 1996-2003 –> 2004-2011 6.2360899 8665.9176
Atlantic (26.5,26.75] cstar_tref ~ aou + silicate + phosphate + phosphate_star 771 3.5657200 4160.4496 11.283833 2004-2011 –> 2012-2019 6.9652180 8833.9366
Atlantic (26.5,26.75] cstar_tref ~ sal + aou + silicate + phosphate 771 3.5017280 4132.5248 10.918434 2004-2011 –> 2012-2019 6.8590052 8783.9662
Atlantic (26.5,26.75] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 771 3.4689237 4120.0112 11.149837 2004-2011 –> 2012-2019 6.7846873 8751.5044
Atlantic (26.5,26.75] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 771 3.5011678 4134.2781 9.539906 2004-2011 –> 2012-2019 6.8620877 8789.6324
Atlantic (26.5,26.75] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 771 3.4651525 4118.3340 10.981655 2004-2011 –> 2012-2019 6.7418024 8728.8947
Atlantic (26.75,27] cstar_tref ~ aou + silicate + phosphate + phosphate_star 1466 2.1385786 6401.0624 15.181590 1996-2003 –> 2004-2011 3.9494931 12165.5605
Atlantic (26.75,27] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1466 2.0490550 6277.6822 14.538962 1996-2003 –> 2004-2011 3.8099289 11964.0941
Atlantic (26.75,27] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1466 2.2365283 6534.3673 14.860634 1996-2003 –> 2004-2011 4.1791090 12501.4551
Atlantic (26.75,27] cstar_tref ~ temp + aou + silicate + phosphate 1466 2.2338416 6528.8430 15.674224 1996-2003 –> 2004-2011 4.1615394 12471.9504
Atlantic (26.75,27] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 1466 2.1384087 6402.8294 15.160969 1996-2003 –> 2004-2011 3.7803210 11889.3283
Atlantic (26.75,27] cstar_tref ~ aou + silicate + phosphate + phosphate_star 1457 2.6444949 6980.5939 27.742154 2004-2011 –> 2012-2019 4.7830735 13381.6563
Atlantic (26.75,27] cstar_tref ~ sal + aou + silicate + phosphate 1457 2.5578379 6883.5059 25.034893 2004-2011 –> 2012-2019 4.8260521 13457.1206
Atlantic (26.75,27] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1457 2.5186128 6840.4728 26.017198 2004-2011 –> 2012-2019 4.5676678 13118.1550
Atlantic (26.75,27] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1457 2.6602672 6999.9219 22.647914 2004-2011 –> 2012-2019 4.8967955 13534.2892
Atlantic (26.75,27] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 1457 2.2419740 6501.4244 22.647793 2004-2011 –> 2012-2019 4.3803827 12904.2538
Atlantic (27,27.25] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 1254 2.1371321 5477.4355 9.973983 1996-2003 –> 2004-2011 4.1965279 10767.7248
Atlantic (27,27.25] cstar_tref ~ sal + aou + silicate + phosphate 1254 1.9795194 5283.2959 9.085510 1996-2003 –> 2004-2011 3.7168026 10152.4831
Atlantic (27,27.25] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1254 1.9572514 5256.9231 9.500465 1996-2003 –> 2004-2011 3.6123135 10008.6458
Atlantic (27,27.25] cstar_tref ~ sal + aou + silicate + phosphate_star 1254 2.1909716 5537.8353 9.208046 1996-2003 –> 2004-2011 4.2835323 10865.4893
Atlantic (27,27.25] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1254 2.0836483 5413.8717 13.784630 1996-2003 –> 2004-2011 3.9117319 10410.5891
Atlantic (27,27.25] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 1298 2.2471173 5799.4112 11.995770 2004-2011 –> 2012-2019 4.3842494 11276.8467
Atlantic (27,27.25] cstar_tref ~ sal + aou + silicate + phosphate 1298 2.2932948 5850.2173 13.873295 2004-2011 –> 2012-2019 4.2728142 11133.5132
Atlantic (27,27.25] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1298 2.2892043 5847.5828 14.138705 2004-2011 –> 2012-2019 4.2464557 11104.5058
Atlantic (27,27.25] cstar_tref ~ sal + aou + silicate + phosphate_star 1298 2.3406581 5903.2862 15.156280 2004-2011 –> 2012-2019 4.5316297 11441.1216
Atlantic (27,27.25] cstar_tref ~ sal + silicate + phosphate + phosphate_star 1298 2.3605302 5925.2332 15.297978 2004-2011 –> 2012-2019 4.6039780 11522.4298
Atlantic (27.25,27.5] cstar_tref ~ sal + aou + nitrate + silicate 1223 2.6291175 5847.1453 13.485962 1996-2003 –> 2004-2011 5.0565586 11891.0557
Atlantic (27.25,27.5] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 1223 2.5481317 5772.6152 12.492082 1996-2003 –> 2004-2011 4.9010903 11737.0003
Atlantic (27.25,27.5] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1223 2.8357972 6034.2459 21.498590 1996-2003 –> 2004-2011 5.1636418 11970.5595
Atlantic (27.25,27.5] cstar_tref ~ temp + aou + nitrate + silicate 1223 2.5201179 5743.5754 10.292548 1996-2003 –> 2004-2011 4.5026690 11257.8683
Atlantic (27.25,27.5] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1223 2.3604356 5585.4615 14.135697 1996-2003 –> 2004-2011 4.2760401 11011.8916
Atlantic (27.25,27.5] cstar_tref ~ aou + nitrate + silicate + phosphate_star 1246 2.9723866 6262.6929 21.419028 2004-2011 –> 2012-2019 5.8183775 12303.7156
Atlantic (27.25,27.5] cstar_tref ~ sal + aou + nitrate + silicate 1246 2.9522486 6245.7521 16.304970 2004-2011 –> 2012-2019 5.5813661 12092.8973
Atlantic (27.25,27.5] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 1246 2.7555028 6075.8858 17.253040 2004-2011 –> 2012-2019 5.3036345 11848.5011
Atlantic (27.25,27.5] cstar_tref ~ temp + aou + nitrate + silicate 1246 3.1088320 6374.5385 12.174720 2004-2011 –> 2012-2019 5.6289499 12118.1139
Atlantic (27.25,27.5] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1246 2.7712552 6090.0913 17.125138 2004-2011 –> 2012-2019 5.1316908 11675.5527
Atlantic (27.5,27.75] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 1695 2.8530270 8378.2117 13.288480 1996-2003 –> 2004-2011 5.6125387 17008.5443
Atlantic (27.5,27.75] cstar_tref ~ sal + aou + phosphate 1695 2.8538009 8375.1311 13.146342 1996-2003 –> 2004-2011 5.4845560 16832.3125
Atlantic (27.5,27.75] cstar_tref ~ sal + aou + phosphate + phosphate_star 1695 2.8501372 8372.7762 13.404536 1996-2003 –> 2004-2011 5.4222167 16752.1088
Atlantic (27.5,27.75] cstar_tref ~ sal + aou + silicate + phosphate 1695 2.4931053 7919.0650 13.463322 1996-2003 –> 2004-2011 4.9244982 16099.2712
Atlantic (27.5,27.75] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1695 2.4620279 7878.5421 14.190925 1996-2003 –> 2004-2011 4.7848922 15899.1000
Atlantic (27.5,27.75] cstar_tref ~ sal + aou + nitrate + silicate 1726 3.2376201 8965.7184 16.642443 2004-2011 –> 2012-2019 6.0907234 17342.0207
Atlantic (27.5,27.75] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 1726 3.1871045 8913.4333 14.760993 2004-2011 –> 2012-2019 6.0401315 17291.6450
Atlantic (27.5,27.75] cstar_tref ~ sal + aou + phosphate + phosphate_star 1726 3.3729075 9107.0316 14.289022 2004-2011 –> 2012-2019 6.2230447 17479.8078
Atlantic (27.5,27.75] cstar_tref ~ sal + aou + silicate + phosphate 1726 2.8194714 8488.3444 15.764243 2004-2011 –> 2012-2019 5.3125766 16407.4094
Atlantic (27.5,27.75] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1726 2.8147730 8484.5871 15.664867 2004-2011 –> 2012-2019 5.2768009 16363.1292
Atlantic (27.75,27.85] cstar_tref ~ sal + aou + nitrate + silicate 672 1.8512417 2746.7646 11.971423 1996-2003 –> 2004-2011 3.8931766 5143.2757
Atlantic (27.75,27.85] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 672 1.8029543 2713.2428 12.732280 1996-2003 –> 2004-2011 3.8019318 5087.9828
Atlantic (27.75,27.85] cstar_tref ~ sal + aou + phosphate + phosphate_star 672 2.0368365 2875.1721 11.562845 1996-2003 –> 2004-2011 3.9043401 5171.8511
Atlantic (27.75,27.85] cstar_tref ~ sal + aou + silicate + phosphate 672 1.6872294 2622.0833 11.189542 1996-2003 –> 2004-2011 3.5199664 4897.7527
Atlantic (27.75,27.85] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 672 1.5204477 2484.1959 11.910203 1996-2003 –> 2004-2011 3.1852248 4654.4040
Atlantic (27.75,27.85] cstar_tref ~ sal + aou + nitrate + silicate 602 1.9833461 2544.8836 12.656555 2004-2011 –> 2012-2019 3.8345878 5291.6482
Atlantic (27.75,27.85] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 602 1.9719012 2539.9158 12.990383 2004-2011 –> 2012-2019 3.7748555 5253.1585
Atlantic (27.75,27.85] cstar_tref ~ sal + aou + phosphate + phosphate_star 602 2.3831850 2766.0011 11.437040 2004-2011 –> 2012-2019 4.4200215 5641.1733
Atlantic (27.75,27.85] cstar_tref ~ sal + aou + silicate + phosphate 602 1.8120780 2436.1490 11.729179 2004-2011 –> 2012-2019 3.4993073 5058.2323
Atlantic (27.75,27.85] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 602 1.7012350 2362.1527 12.070578 2004-2011 –> 2012-2019 3.2216827 4846.3487
Atlantic (27.85,27.95] cstar_tref ~ aou + silicate + phosphate + phosphate_star 619 3.8257986 3429.7537 34.749795 1996-2003 –> 2004-2011 6.3090326 6249.9251
Atlantic (27.85,27.95] cstar_tref ~ sal + aou + phosphate + phosphate_star 619 3.7977577 3420.6465 27.454467 1996-2003 –> 2004-2011 6.0084340 6100.6044
Atlantic (27.85,27.95] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 619 3.7781437 3416.2361 24.960797 1996-2003 –> 2004-2011 5.9761174 6091.2442
Atlantic (27.85,27.95] cstar_tref ~ temp + aou + phosphate + phosphate_star 619 3.3550355 3267.1983 23.106927 1996-2003 –> 2004-2011 5.8576024 6096.7224
Atlantic (27.85,27.95] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 619 3.3425875 3264.5965 22.953713 1996-2003 –> 2004-2011 5.8073011 6077.7396
Atlantic (27.85,27.95] cstar_tref ~ aou + silicate + phosphate + phosphate_star 653 3.6892519 3570.0171 23.336325 2004-2011 –> 2012-2019 7.5150505 6999.7708
Atlantic (27.85,27.95] cstar_tref ~ sal + aou + phosphate + phosphate_star 653 3.3316251 3436.8527 30.681417 2004-2011 –> 2012-2019 7.1293828 6857.4992
Atlantic (27.85,27.95] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 653 3.2441871 3404.1191 29.458995 2004-2011 –> 2012-2019 7.0223308 6820.3553
Atlantic (27.85,27.95] cstar_tref ~ temp + aou + phosphate + phosphate_star 653 3.6176780 3544.4308 22.662902 2004-2011 –> 2012-2019 6.9727135 6811.6291
Atlantic (27.85,27.95] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 653 3.5991948 3539.7412 23.185387 2004-2011 –> 2012-2019 6.9417823 6804.3376
Atlantic (27.95,28.05] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 747 4.5838524 4408.5686 24.850444 1996-2003 –> 2004-2011 9.1442596 8727.2584
Atlantic (27.95,28.05] cstar_tref ~ temp + aou + nitrate + phosphate_star 747 4.1690019 4264.8431 22.092955 1996-2003 –> 2004-2011 8.1096223 8367.3888
Atlantic (27.95,28.05] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 747 3.9845789 4199.2470 23.156269 1996-2003 –> 2004-2011 7.5284047 8148.2042
Atlantic (27.95,28.05] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 747 4.5055827 4382.8381 21.552137 1996-2003 –> 2004-2011 9.1819218 8738.3298
Atlantic (27.95,28.05] cstar_tref ~ temp + aou + silicate + phosphate_star 747 4.5845260 4406.7881 23.952898 1996-2003 –> 2004-2011 9.2685599 8762.6901
Atlantic (27.95,28.05] cstar_tref ~ sal + aou + phosphate + phosphate_star 758 4.5296823 4453.2589 24.317834 2004-2011 –> 2012-2019 9.1711908 8878.5020
Atlantic (27.95,28.05] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 758 4.4609561 4432.0813 24.324706 2004-2011 –> 2012-2019 9.0448085 8840.6499
Atlantic (27.95,28.05] cstar_tref ~ temp + aou + nitrate + phosphate_star 758 3.8756111 4216.8411 23.407864 2004-2011 –> 2012-2019 8.0446130 8481.6842
Atlantic (27.95,28.05] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 758 3.4363974 4036.4971 28.154805 2004-2011 –> 2012-2019 7.4209764 8235.7441
Atlantic (27.95,28.05] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 758 4.5917064 4475.8764 22.721516 2004-2011 –> 2012-2019 9.0972891 8858.7145
Atlantic (28.05,28.1] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 515 1.1086232 1581.7192 7.210190 1996-2003 –> 2004-2011 2.2347438 3031.9469
Atlantic (28.05,28.1] cstar_tref ~ sal + aou + phosphate + phosphate_star 515 1.0356115 1509.5485 8.565342 1996-2003 –> 2004-2011 2.0685471 2877.1032
Atlantic (28.05,28.1] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 515 1.0124597 1488.2609 7.748834 1996-2003 –> 2004-2011 2.0321077 2845.7228
Atlantic (28.05,28.1] cstar_tref ~ temp + aou + phosphate + phosphate_star 515 1.0655591 1538.9113 11.894478 1996-2003 –> 2004-2011 2.1671256 2966.5488
Atlantic (28.05,28.1] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 515 0.9974088 1472.8342 12.850742 1996-2003 –> 2004-2011 1.9947969 2809.6801
Atlantic (28.05,28.1] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 487 1.1661680 1545.7725 7.287488 2004-2011 –> 2012-2019 2.2747912 3127.4916
Atlantic (28.05,28.1] cstar_tref ~ sal + aou + phosphate + phosphate_star 487 1.1138068 1499.0275 9.229555 2004-2011 –> 2012-2019 2.1494183 3008.5760
Atlantic (28.05,28.1] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 487 1.0562257 1449.3258 7.784371 2004-2011 –> 2012-2019 2.0686854 2937.5866
Atlantic (28.05,28.1] cstar_tref ~ temp + aou + phosphate + phosphate_star 487 1.1262979 1509.8899 13.017734 2004-2011 –> 2012-2019 2.1918570 3048.8012
Atlantic (28.05,28.1] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 487 1.0832019 1473.8895 13.763728 2004-2011 –> 2012-2019 2.0806106 2946.7238
Atlantic (28.1,28.15] cstar_tref ~ aou + silicate + phosphate + phosphate_star 573 0.8631024 1469.3873 10.383400 1996-2003 –> 2004-2011 1.6232253 2770.2782
Atlantic (28.1,28.15] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 573 0.8578585 1464.4033 7.494117 1996-2003 –> 2004-2011 1.6186171 2768.2356
Atlantic (28.1,28.15] cstar_tref ~ sal + aou + phosphate + phosphate_star 573 0.7780934 1350.5622 6.506697 1996-2003 –> 2004-2011 1.4817795 2564.5848
Atlantic (28.1,28.15] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 573 0.7682971 1338.0422 6.378014 1996-2003 –> 2004-2011 1.4427000 2506.2046
Atlantic (28.1,28.15] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 573 0.7791692 1354.1456 10.474171 1996-2003 –> 2004-2011 1.4647761 2540.8606
Atlantic (28.1,28.15] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 569 0.9382474 1556.2141 6.997127 2004-2011 –> 2012-2019 1.7961059 3020.6174
Atlantic (28.1,28.15] cstar_tref ~ sal + aou + phosphate + phosphate_star 569 0.8565784 1450.5789 6.288709 2004-2011 –> 2012-2019 1.6346718 2801.1411
Atlantic (28.1,28.15] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 569 0.8563060 1452.2170 6.289861 2004-2011 –> 2012-2019 1.6246031 2790.2593
Atlantic (28.1,28.15] cstar_tref ~ temp + aou + phosphate + phosphate_star 569 0.9406853 1557.1672 11.672325 2004-2011 –> 2012-2019 1.7897713 3007.7911
Atlantic (28.1,28.15] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 569 0.8571402 1453.3250 12.136010 2004-2011 –> 2012-2019 1.6363094 2807.4706
Atlantic (28.15,28.2] cstar_tref ~ aou + nitrate + silicate + phosphate_star 967 0.5660113 1655.5081 2.429376 1996-2003 –> 2004-2011 1.1029265 3169.1000
Atlantic (28.15,28.2] cstar_tref ~ sal + aou + nitrate + phosphate_star 967 0.6067239 1789.8432 2.322388 1996-2003 –> 2004-2011 1.1620107 3366.8217
Atlantic (28.15,28.2] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 967 0.5537337 1615.0951 2.082601 1996-2003 –> 2004-2011 1.0724252 3065.6308
Atlantic (28.15,28.2] cstar_tref ~ temp + aou + nitrate + silicate 967 0.5788676 1698.9453 2.207702 1996-2003 –> 2004-2011 1.1214951 3232.4754
Atlantic (28.15,28.2] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 967 0.5627321 1646.2706 2.335057 1996-2003 –> 2004-2011 1.0931362 3138.8761
Atlantic (28.15,28.2] cstar_tref ~ sal + aou + nitrate + phosphate_star 924 0.7413603 2081.1502 4.624216 2004-2011 –> 2012-2019 1.3480842 3870.9933
Atlantic (28.15,28.2] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 924 0.7350201 2067.2778 5.145468 2004-2011 –> 2012-2019 1.2887538 3682.3729
Atlantic (28.15,28.2] cstar_tref ~ temp + aou + nitrate + phosphate_star 924 0.8638898 2363.8173 8.269842 2004-2011 –> 2012-2019 1.4745733 4166.2413
Atlantic (28.15,28.2] cstar_tref ~ temp + aou + nitrate + silicate 924 0.8548926 2344.4700 8.893373 2004-2011 –> 2012-2019 1.4337602 4043.4152
Atlantic (28.15,28.2] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 924 0.8547203 2346.0975 8.806949 2004-2011 –> 2012-2019 1.4174524 3992.3681
Atlantic (28.2, Inf] cstar_tref ~ sal + aou + nitrate 1671 0.3747922 1472.3092 1.912993 1996-2003 –> 2004-2011 0.7135446 2587.2190
Atlantic (28.2, Inf] cstar_tref ~ sal + aou + nitrate + phosphate_star 1671 0.3682319 1415.2927 1.887519 1996-2003 –> 2004-2011 0.7012141 2475.7835
Atlantic (28.2, Inf] cstar_tref ~ sal + aou + nitrate + silicate 1671 0.3739238 1466.5562 1.921002 1996-2003 –> 2004-2011 0.7104304 2561.6230
Atlantic (28.2, Inf] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 1671 0.3612110 1352.9570 1.795777 1996-2003 –> 2004-2011 0.6917452 2391.2152
Atlantic (28.2, Inf] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1671 0.5491399 2752.8908 3.338939 1996-2003 –> 2004-2011 1.0993348 5464.5520
Atlantic (28.2, Inf] cstar_tref ~ sal + aou + nitrate 1737 0.4168056 1899.1725 2.459212 2004-2011 –> 2012-2019 0.7915979 3371.4817
Atlantic (28.2, Inf] cstar_tref ~ sal + aou + nitrate + phosphate_star 1737 0.3969747 1731.8238 2.393150 2004-2011 –> 2012-2019 0.7652066 3147.1165
Atlantic (28.2, Inf] cstar_tref ~ sal + aou + nitrate + silicate 1737 0.4096138 1840.7069 2.447629 2004-2011 –> 2012-2019 0.7835376 3307.2631
Atlantic (28.2, Inf] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 1737 0.3868448 1644.0243 2.501055 2004-2011 –> 2012-2019 0.7480557 2996.9813
Atlantic (28.2, Inf] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1737 0.6964164 3686.4733 8.753566 2004-2011 –> 2012-2019 1.2455563 6439.3641
Indo-Pacific (-Inf,26] cstar_tref ~ sal + aou + phosphate + phosphate_star 3246 6.2169603 21086.4578 29.998933 1996-2003 –> 2004-2011 12.6111638 42152.4024
Indo-Pacific (-Inf,26] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3246 6.1757726 21045.3049 29.574708 1996-2003 –> 2004-2011 12.5245126 42067.3680
Indo-Pacific (-Inf,26] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 3246 7.1448675 21991.5805 31.601162 1996-2003 –> 2004-2011 14.6048158 44050.7521
Indo-Pacific (-Inf,26] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 3246 7.2782698 22111.6754 29.310737 1996-2003 –> 2004-2011 14.7679713 44196.4412
Indo-Pacific (-Inf,26] cstar_tref ~ temp + silicate + phosphate + phosphate_star 3246 7.3523279 22175.3992 28.684722 1996-2003 –> 2004-2011 14.9855462 44380.2104
Indo-Pacific (-Inf,26] cstar_tref ~ sal + aou + phosphate + phosphate_star 3219 5.9807533 20661.7889 24.830732 2004-2011 –> 2012-2019 12.1977137 41748.2467
Indo-Pacific (-Inf,26] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3219 5.9073729 20584.3097 24.068418 2004-2011 –> 2012-2019 12.0831455 41629.6146
Indo-Pacific (-Inf,26] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 3219 6.9702974 21649.5198 25.784987 2004-2011 –> 2012-2019 14.1151649 43641.1004
Indo-Pacific (-Inf,26] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 3219 7.0194967 21694.8023 25.324686 2004-2011 –> 2012-2019 14.2977665 43806.4777
Indo-Pacific (-Inf,26] cstar_tref ~ temp + silicate + phosphate + phosphate_star 3219 7.1485766 21810.1138 24.329390 2004-2011 –> 2012-2019 14.5009045 43985.5130
Indo-Pacific (26,26.5] cstar_tref ~ aou + silicate + phosphate + phosphate_star 3405 4.8249802 20392.5945 41.667551 1996-2003 –> 2004-2011 9.4509401 40161.9624
Indo-Pacific (26,26.5] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3405 4.4335420 19818.4153 43.609672 1996-2003 –> 2004-2011 8.7433514 39115.7719
Indo-Pacific (26,26.5] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 3405 4.6683171 20169.8101 48.745517 1996-2003 –> 2004-2011 9.1225327 39687.8487
Indo-Pacific (26,26.5] cstar_tref ~ temp + aou + silicate + phosphate 3405 4.8417002 20416.1524 39.815708 1996-2003 –> 2004-2011 9.4493128 40158.9100
Indo-Pacific (26,26.5] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 3405 4.8206875 20388.5331 41.229304 1996-2003 –> 2004-2011 9.4267659 40131.0608
Indo-Pacific (26,26.5] cstar_tref ~ aou + silicate + phosphate + phosphate_star 3368 4.9356511 20323.8902 41.884845 2004-2011 –> 2012-2019 9.7606312 40716.4847
Indo-Pacific (26,26.5] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3368 4.5984850 19849.2664 44.859613 2004-2011 –> 2012-2019 9.0320270 39667.6817
Indo-Pacific (26,26.5] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 3368 4.7388554 20051.8093 47.895267 2004-2011 –> 2012-2019 9.4071724 40221.6194
Indo-Pacific (26,26.5] cstar_tref ~ temp + aou + silicate + phosphate 3368 4.9272249 20312.3807 40.695631 2004-2011 –> 2012-2019 9.7689251 40728.5331
Indo-Pacific (26,26.5] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 3368 4.9223774 20307.7504 41.255551 2004-2011 –> 2012-2019 9.7430649 40696.2835
Indo-Pacific (26.5,26.75] cstar_tref ~ aou + silicate + phosphate + phosphate_star 2770 4.1070067 15699.2468 19.087530 1996-2003 –> 2004-2011 8.0884866 31517.7888
Indo-Pacific (26.5,26.75] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 2770 3.9831778 15531.6424 21.172611 1996-2003 –> 2004-2011 7.8642850 31208.0754
Indo-Pacific (26.5,26.75] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 2770 4.0637672 15642.6112 26.160786 1996-2003 –> 2004-2011 8.0944428 31532.4639
Indo-Pacific (26.5,26.75] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 2770 4.0070221 15564.7074 26.432026 1996-2003 –> 2004-2011 7.9047392 31265.2433
Indo-Pacific (26.5,26.75] cstar_tref ~ temp + nitrate + silicate + phosphate_star 2770 4.0639349 15640.8399 25.886384 1996-2003 –> 2004-2011 8.1025298 31539.7707
Indo-Pacific (26.5,26.75] cstar_tref ~ aou + silicate + phosphate + phosphate_star 2801 4.3875969 16245.0285 25.341958 2004-2011 –> 2012-2019 8.4946035 31944.2754
Indo-Pacific (26.5,26.75] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 2801 4.2692216 16093.8131 25.376737 2004-2011 –> 2012-2019 8.2523995 31625.4555
Indo-Pacific (26.5,26.75] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 2801 4.2830499 16111.9290 33.319452 2004-2011 –> 2012-2019 8.3468171 31754.5402
Indo-Pacific (26.5,26.75] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 2801 4.2711513 16096.3446 23.617529 2004-2011 –> 2012-2019 8.2781735 31661.0520
Indo-Pacific (26.5,26.75] cstar_tref ~ temp + nitrate + silicate + phosphate_star 2801 4.2945217 16124.9134 35.819690 2004-2011 –> 2012-2019 8.3584566 31765.7532
Indo-Pacific (26.75,27] cstar_tref ~ aou + silicate + phosphate + phosphate_star 3325 4.5163120 19474.1178 17.207648 1996-2003 –> 2004-2011 8.8348432 38258.5140
Indo-Pacific (26.75,27] cstar_tref ~ sal + aou + phosphate + phosphate_star 3325 4.3003531 19148.2772 19.640974 1996-2003 –> 2004-2011 8.3516258 37516.5312
Indo-Pacific (26.75,27] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3325 4.2816123 19121.2334 18.664319 1996-2003 –> 2004-2011 8.3102750 37455.0312
Indo-Pacific (26.75,27] cstar_tref ~ temp + aou + phosphate + phosphate_star 3325 4.1819106 18962.5500 22.725512 1996-2003 –> 2004-2011 8.1913481 37263.1879
Indo-Pacific (26.75,27] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 3325 4.1448608 18905.3714 21.060911 1996-2003 –> 2004-2011 8.1081443 37132.5896
Indo-Pacific (26.75,27] cstar_tref ~ aou + silicate + phosphate + phosphate_star 3314 4.9456929 20011.6958 18.894312 2004-2011 –> 2012-2019 9.4620048 39485.8136
Indo-Pacific (26.75,27] cstar_tref ~ sal + aou + phosphate + phosphate_star 3314 4.7219635 19704.8699 20.566407 2004-2011 –> 2012-2019 9.0223165 38853.1471
Indo-Pacific (26.75,27] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3314 4.7126127 19693.7316 19.807901 2004-2011 –> 2012-2019 8.9942250 38814.9650
Indo-Pacific (26.75,27] cstar_tref ~ temp + aou + phosphate + phosphate_star 3314 4.6234829 19565.1755 16.992111 2004-2011 –> 2012-2019 8.8053935 38527.7255
Indo-Pacific (26.75,27] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 3314 4.6021825 19536.5697 17.063160 2004-2011 –> 2012-2019 8.7470433 38441.9411
Indo-Pacific (27,27.25] cstar_tref ~ aou + silicate + phosphate + phosphate_star 3970 4.4686124 23165.1708 41.271441 1996-2003 –> 2004-2011 8.6155851 45890.6600
Indo-Pacific (27,27.25] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3970 4.4240340 23087.5645 43.717964 1996-2003 –> 2004-2011 8.5129481 45702.3449
Indo-Pacific (27,27.25] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 3970 4.1572592 22593.7287 52.572683 1996-2003 –> 2004-2011 8.0500166 44815.5106
Indo-Pacific (27,27.25] cstar_tref ~ temp + aou + phosphate + phosphate_star 3970 4.3873371 23019.4282 47.420987 1996-2003 –> 2004-2011 8.5012727 45680.9777
Indo-Pacific (27,27.25] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 3970 3.9999301 22287.4105 32.769229 1996-2003 –> 2004-2011 7.6931370 44088.5274
Indo-Pacific (27,27.25] cstar_tref ~ aou + silicate + phosphate + phosphate_star 3936 5.1405561 24069.6177 31.772046 2004-2011 –> 2012-2019 9.6091685 47234.7884
Indo-Pacific (27,27.25] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3936 5.0888247 23991.9973 34.174619 2004-2011 –> 2012-2019 9.5128587 47079.5618
Indo-Pacific (27,27.25] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 3936 4.9601373 23790.3681 36.685769 2004-2011 –> 2012-2019 9.1173965 46384.0967
Indo-Pacific (27,27.25] cstar_tref ~ temp + aou + phosphate + phosphate_star 3936 5.1174445 24034.1459 40.301141 2004-2011 –> 2012-2019 9.5047816 47053.5741
Indo-Pacific (27,27.25] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 3936 4.4789113 22987.0035 26.047903 2004-2011 –> 2012-2019 8.4788414 45274.4140
Indo-Pacific (27.25,27.5] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 3581 3.4779711 19103.5064 27.203170 1996-2003 –> 2004-2011 6.6535748 37416.5994
Indo-Pacific (27.25,27.5] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3581 3.7770567 19694.3423 28.090962 1996-2003 –> 2004-2011 7.1543851 38445.1974
Indo-Pacific (27.25,27.5] cstar_tref ~ sal + aou + silicate + phosphate_star 3581 3.7958157 19727.8247 27.185663 1996-2003 –> 2004-2011 7.1864464 38504.6213
Indo-Pacific (27.25,27.5] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 3581 3.6750679 19498.2936 26.983314 1996-2003 –> 2004-2011 7.0451017 38233.7798
Indo-Pacific (27.25,27.5] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 3581 3.3920535 18924.3588 27.734478 1996-2003 –> 2004-2011 6.5040791 37093.7003
Indo-Pacific (27.25,27.5] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 3582 3.9500323 20020.6327 31.182518 2004-2011 –> 2012-2019 7.4280035 39124.1390
Indo-Pacific (27.25,27.5] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3582 4.2679798 20575.2470 32.259680 2004-2011 –> 2012-2019 8.0450365 40269.5893
Indo-Pacific (27.25,27.5] cstar_tref ~ sal + aou + silicate + phosphate_star 3582 4.3103114 20643.9524 30.664904 2004-2011 –> 2012-2019 8.1061271 40371.7772
Indo-Pacific (27.25,27.5] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 3582 4.2315288 20513.7994 31.412523 2004-2011 –> 2012-2019 7.9065967 40012.0930
Indo-Pacific (27.25,27.5] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 3582 3.8146830 19770.8513 33.298616 2004-2011 –> 2012-2019 7.2067365 38695.2101
Indo-Pacific (27.5,27.75] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 3544 3.3241279 18585.5941 24.721524 1996-2003 –> 2004-2011 6.0998949 36600.9228
Indo-Pacific (27.5,27.75] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3544 3.2865514 18505.0138 9.677618 1996-2003 –> 2004-2011 6.0823296 36573.3416
Indo-Pacific (27.5,27.75] cstar_tref ~ sal + aou + silicate + phosphate_star 3544 3.4335122 18813.0776 23.338107 1996-2003 –> 2004-2011 6.2937741 37047.6437
Indo-Pacific (27.5,27.75] cstar_tref ~ sal + nitrate + silicate + phosphate_star 3544 3.4601440 18867.8430 30.038374 1996-2003 –> 2004-2011 6.3294739 37125.7628
Indo-Pacific (27.5,27.75] cstar_tref ~ sal + silicate + phosphate + phosphate_star 3544 3.4856987 18919.9987 26.291220 1996-2003 –> 2004-2011 6.3770339 37234.2855
Indo-Pacific (27.5,27.75] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 3535 4.0235914 19888.5719 25.349596 2004-2011 –> 2012-2019 7.3477193 38474.1660
Indo-Pacific (27.5,27.75] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3535 3.8516337 19579.7720 11.061950 2004-2011 –> 2012-2019 7.1381851 38084.7857
Indo-Pacific (27.5,27.75] cstar_tref ~ sal + aou + silicate + phosphate_star 3535 4.1866572 20167.4468 23.819884 2004-2011 –> 2012-2019 7.6201693 38980.5244
Indo-Pacific (27.5,27.75] cstar_tref ~ sal + nitrate + silicate + phosphate_star 3535 4.2516312 20276.3256 31.828042 2004-2011 –> 2012-2019 7.7117752 39144.1687
Indo-Pacific (27.5,27.75] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 3535 4.0611428 19954.2488 20.454813 2004-2011 –> 2012-2019 7.4868735 38753.2444
Indo-Pacific (27.75,27.85] cstar_tref ~ aou + silicate + phosphate + phosphate_star 1459 2.8123408 7169.7248 19.195162 1996-2003 –> 2004-2011 5.1555708 13480.0053
Indo-Pacific (27.75,27.85] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1459 2.8123031 7171.6856 19.238877 1996-2003 –> 2004-2011 5.1534905 13481.5471
Indo-Pacific (27.75,27.85] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1459 2.8041919 7163.2574 19.600865 1996-2003 –> 2004-2011 5.1511784 13479.9815
Indo-Pacific (27.75,27.85] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 1459 2.7000998 7052.8792 19.002396 1996-2003 –> 2004-2011 4.9876213 13298.4134
Indo-Pacific (27.75,27.85] cstar_tref ~ temp + nitrate + silicate + phosphate_star 1459 2.8048872 7161.9808 19.994460 1996-2003 –> 2004-2011 5.1589615 13485.0697
Indo-Pacific (27.75,27.85] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 1444 3.0956977 7375.3729 33.597007 2004-2011 –> 2012-2019 5.8998958 14538.6368
Indo-Pacific (27.75,27.85] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1444 3.1266486 7404.1039 33.211547 2004-2011 –> 2012-2019 5.9389517 14575.7895
Indo-Pacific (27.75,27.85] cstar_tref ~ sal + nitrate + silicate + phosphate_star 1444 3.1142113 7390.5930 30.542144 2004-2011 –> 2012-2019 5.9192286 14552.7091
Indo-Pacific (27.75,27.85] cstar_tref ~ temp + aou + phosphate + phosphate_star 1444 3.0748662 7353.8733 33.660796 2004-2011 –> 2012-2019 5.8891759 14525.6402
Indo-Pacific (27.75,27.85] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 1444 3.0368913 7319.9842 30.964406 2004-2011 –> 2012-2019 5.7369911 14372.8634
Indo-Pacific (27.85,27.95] cstar_tref ~ temp + aou + nitrate 1759 2.8051567 8630.4999 27.808805 1996-2003 –> 2004-2011 5.2588816 16729.8778
Indo-Pacific (27.85,27.95] cstar_tref ~ temp + aou + nitrate + phosphate_star 1759 2.8051304 8632.4669 27.795859 1996-2003 –> 2004-2011 5.2388348 16705.2358
Indo-Pacific (27.85,27.95] cstar_tref ~ temp + aou + nitrate + silicate 1759 2.7974245 8622.7894 27.287070 1996-2003 –> 2004-2011 5.2508176 16723.6950
Indo-Pacific (27.85,27.95] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1759 2.7972309 8624.5460 27.243655 1996-2003 –> 2004-2011 5.2309272 16699.3033
Indo-Pacific (27.85,27.95] cstar_tref ~ temp + nitrate + silicate + phosphate_star 1759 2.8157307 8645.7360 30.252435 1996-2003 –> 2004-2011 5.2553601 16726.9962
Indo-Pacific (27.85,27.95] cstar_tref ~ temp + aou + nitrate + silicate 1812 3.2205266 9392.6640 33.333169 2004-2011 –> 2012-2019 6.0179511 18015.4534
Indo-Pacific (27.85,27.95] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1812 3.2136045 9386.8662 33.484631 2004-2011 –> 2012-2019 6.0108354 18011.4122
Indo-Pacific (27.85,27.95] cstar_tref ~ temp + aou + phosphate + phosphate_star 1812 3.1725957 9338.3228 38.862206 2004-2011 –> 2012-2019 6.0243852 18028.8249
Indo-Pacific (27.85,27.95] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 1812 3.1694384 9336.7144 38.949644 2004-2011 –> 2012-2019 6.0168535 18023.8162
Indo-Pacific (27.85,27.95] cstar_tref ~ temp + nitrate + silicate + phosphate_star 1812 3.2136983 9384.9720 33.720573 2004-2011 –> 2012-2019 6.0294290 18030.7080
Indo-Pacific (27.95,28.05] cstar_tref ~ sal + aou + nitrate + phosphate_star 1547 2.1755392 6807.0894 7.584486 1996-2003 –> 2004-2011 4.2948423 13225.0057
Indo-Pacific (27.95,28.05] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 1547 2.1573488 6783.1107 8.114973 1996-2003 –> 2004-2011 4.2418243 13154.1122
Indo-Pacific (27.95,28.05] cstar_tref ~ sal + aou + phosphate + phosphate_star 1547 2.2315786 6885.7782 7.827359 1996-2003 –> 2004-2011 4.3597179 13315.9769
Indo-Pacific (27.95,28.05] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1547 2.1942146 6835.5357 8.543078 1996-2003 –> 2004-2011 4.2637684 13185.3294
Indo-Pacific (27.95,28.05] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 1547 2.1767066 6810.7493 7.848859 1996-2003 –> 2004-2011 4.2181205 13120.1289
Indo-Pacific (27.95,28.05] cstar_tref ~ sal + aou + nitrate + phosphate_star 1574 2.4168227 7256.7829 8.253149 2004-2011 –> 2012-2019 4.5923619 14063.8723
Indo-Pacific (27.95,28.05] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 1574 2.4128027 7253.5423 8.385594 2004-2011 –> 2012-2019 4.5701515 14036.6530
Indo-Pacific (27.95,28.05] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1574 2.4832668 7344.1604 8.436764 2004-2011 –> 2012-2019 4.6774813 14179.6961
Indo-Pacific (27.95,28.05] cstar_tref ~ temp + aou + phosphate + phosphate_star 1574 2.4695639 7324.7414 9.082783 2004-2011 –> 2012-2019 4.7071864 14218.8879
Indo-Pacific (27.95,28.05] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 1574 2.4349796 7282.3445 8.876878 2004-2011 –> 2012-2019 4.6116862 14093.0938
Indo-Pacific (28.05,28.1] cstar_tref ~ aou + silicate + phosphate + phosphate_star 1039 1.9123548 4307.7952 9.704475 1996-2003 –> 2004-2011 3.7367227 8752.0527
Indo-Pacific (28.05,28.1] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1039 1.8759043 4269.8050 9.371780 1996-2003 –> 2004-2011 3.6268570 8625.9473
Indo-Pacific (28.05,28.1] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1039 1.8889727 4284.2312 10.159261 1996-2003 –> 2004-2011 3.6697719 8677.4568
Indo-Pacific (28.05,28.1] cstar_tref ~ temp + aou + silicate + phosphate 1039 1.9219128 4318.1552 9.743117 1996-2003 –> 2004-2011 3.7570123 8775.2809
Indo-Pacific (28.05,28.1] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 1039 1.8356696 4224.7509 9.230081 1996-2003 –> 2004-2011 3.5639082 8552.2454
Indo-Pacific (28.05,28.1] cstar_tref ~ aou + silicate + phosphate + phosphate_star 1098 2.0974951 4754.6624 10.083571 2004-2011 –> 2012-2019 4.0098499 9062.4576
Indo-Pacific (28.05,28.1] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 1098 2.0366777 4692.0476 9.840781 2004-2011 –> 2012-2019 3.9125820 8961.8526
Indo-Pacific (28.05,28.1] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 1098 2.0808011 4739.1145 10.602723 2004-2011 –> 2012-2019 3.9697739 9023.3457
Indo-Pacific (28.05,28.1] cstar_tref ~ temp + aou + silicate + phosphate 1098 2.1096274 4767.3280 10.132349 2004-2011 –> 2012-2019 4.0315403 9085.4832
Indo-Pacific (28.05,28.1] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 1098 1.9943958 4645.9782 9.232960 2004-2011 –> 2012-2019 3.8300654 8870.7291
Indo-Pacific (28.1, Inf] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 9051 1.5273499 33366.4405 8.189701 1996-2003 –> 2004-2011 2.9951231 65817.9536
Indo-Pacific (28.1, Inf] cstar_tref ~ sal + aou + silicate + phosphate 9051 1.4439279 32347.7043 10.164733 1996-2003 –> 2004-2011 2.8148145 63568.4297
Indo-Pacific (28.1, Inf] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 9051 1.3485292 31112.3860 12.462561 1996-2003 –> 2004-2011 2.6239889 61036.8266
Indo-Pacific (28.1, Inf] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 9051 1.3895570 31654.9120 11.877794 1996-2003 –> 2004-2011 2.6488296 61349.5247
Indo-Pacific (28.1, Inf] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 9051 1.4043774 31846.9576 13.304360 1996-2003 –> 2004-2011 2.7414261 62619.9599
Indo-Pacific (28.1, Inf] cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 8916 1.6403384 34141.6140 9.423402 2004-2011 –> 2012-2019 3.1676883 67508.0546
Indo-Pacific (28.1, Inf] cstar_tref ~ sal + aou + silicate + phosphate 8916 1.5758512 33424.4266 10.841591 2004-2011 –> 2012-2019 3.0197791 65772.1309
Indo-Pacific (28.1, Inf] cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 8916 1.4964702 32504.7536 12.917281 2004-2011 –> 2012-2019 2.8449994 63617.1396
Indo-Pacific (28.1, Inf] cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 8916 1.5915922 33603.6641 11.305841 2004-2011 –> 2012-2019 2.9811492 65258.5761
Indo-Pacific (28.1, Inf] cstar_tref ~ temp + aou + silicate + phosphate + phosphate_star 8916 1.5541645 33179.3201 14.028433 2004-2011 –> 2012-2019 2.9585419 65026.2777

5.3.2 per fitting unit

lm_best %>%
  group_by(basin, gamma_slab, eras) %>% 
  summarise(rmse_sum_mean = mean(rmse_sum),
            ais_sum_mean = mean(aic_sum)) %>% 
  ungroup() %>% 
  kable() %>%
  add_header_above() %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "400px")
basin gamma_slab eras rmse_sum_mean ais_sum_mean
Atlantic (-Inf,26] 1996-2003 –> 2004-2011 2.5104796 653.0217
Atlantic (-Inf,26] 2004-2011 –> 2012-2019 2.6662582 643.1597
Atlantic (26,26.5] 1996-2003 –> 2004-2011 7.9414516 7306.8970
Atlantic (26,26.5] 2004-2011 –> 2012-2019 8.0116576 7353.1767
Atlantic (26.5,26.75] 1996-2003 –> 2004-2011 6.4248511 8764.4834
Atlantic (26.5,26.75] 2004-2011 –> 2012-2019 6.8425601 8777.5869
Atlantic (26.75,27] 1996-2003 –> 2004-2011 3.9760783 12198.4777
Atlantic (26.75,27] 2004-2011 –> 2012-2019 4.6907943 13279.0950
Atlantic (27,27.25] 1996-2003 –> 2004-2011 3.9441817 10440.9864
Atlantic (27,27.25] 2004-2011 –> 2012-2019 4.4078254 11295.6834
Atlantic (27.25,27.5] 1996-2003 –> 2004-2011 4.7800000 11573.6751
Atlantic (27.25,27.5] 2004-2011 –> 2012-2019 5.4928037 12007.7561
Atlantic (27.5,27.75] 1996-2003 –> 2004-2011 5.2457404 16518.2673
Atlantic (27.5,27.75] 2004-2011 –> 2012-2019 5.7886554 16976.8024
Atlantic (27.75,27.85] 1996-2003 –> 2004-2011 3.6609279 4991.0533
Atlantic (27.75,27.85] 2004-2011 –> 2012-2019 3.7500910 5218.1122
Atlantic (27.85,27.95] 1996-2003 –> 2004-2011 5.9916975 6123.2471
Atlantic (27.85,27.95] 2004-2011 –> 2012-2019 7.1162520 6858.7184
Atlantic (27.95,28.05] 1996-2003 –> 2004-2011 8.6465537 8548.7742
Atlantic (27.95,28.05] 2004-2011 –> 2012-2019 8.5557755 8659.0589
Atlantic (28.05,28.1] 1996-2003 –> 2004-2011 2.0994642 2906.2004
Atlantic (28.05,28.1] 2004-2011 –> 2012-2019 2.1530725 3013.8359
Atlantic (28.1,28.15] 1996-2003 –> 2004-2011 1.5262196 2630.0328
Atlantic (28.1,28.15] 2004-2011 –> 2012-2019 1.6962923 2885.4559
Atlantic (28.15,28.2] 1996-2003 –> 2004-2011 1.1103987 3194.5808
Atlantic (28.15,28.2] 2004-2011 –> 2012-2019 1.3925248 3951.0782
Atlantic (28.2, Inf] 1996-2003 –> 2004-2011 0.7832538 3096.0785
Atlantic (28.2, Inf] 2004-2011 –> 2012-2019 0.8667908 3852.4414
Indo-Pacific (-Inf,26] 1996-2003 –> 2004-2011 13.8988019 43369.4348
Indo-Pacific (-Inf,26] 2004-2011 –> 2012-2019 13.4389390 42962.1905
Indo-Pacific (26,26.5] 1996-2003 –> 2004-2011 9.2385806 39851.1108
Indo-Pacific (26,26.5] 2004-2011 –> 2012-2019 9.5423642 40406.1205
Indo-Pacific (26.5,26.75] 1996-2003 –> 2004-2011 8.0108967 31412.6684
Indo-Pacific (26.5,26.75] 2004-2011 –> 2012-2019 8.3460900 31750.2152
Indo-Pacific (26.75,27] 1996-2003 –> 2004-2011 8.3592473 37525.1708
Indo-Pacific (26.75,27] 2004-2011 –> 2012-2019 9.0061966 38824.7185
Indo-Pacific (27,27.25] 1996-2003 –> 2004-2011 8.2745919 45235.6041
Indo-Pacific (27,27.25] 2004-2011 –> 2012-2019 9.2446094 46605.2870
Indo-Pacific (27.25,27.5] 1996-2003 –> 2004-2011 6.9087174 37938.7796
Indo-Pacific (27.25,27.5] 2004-2011 –> 2012-2019 7.7385001 39694.5617
Indo-Pacific (27.5,27.75] 1996-2003 –> 2004-2011 6.2365013 36916.3913
Indo-Pacific (27.5,27.75] 2004-2011 –> 2012-2019 7.4609445 38687.3778
Indo-Pacific (27.75,27.85] 1996-2003 –> 2004-2011 5.1213645 13445.0034
Indo-Pacific (27.75,27.85] 2004-2011 –> 2012-2019 5.8768486 14513.1278
Indo-Pacific (27.85,27.95] 1996-2003 –> 2004-2011 5.2469642 16717.0216
Indo-Pacific (27.85,27.95] 2004-2011 –> 2012-2019 6.0198908 18022.0429
Indo-Pacific (27.95,28.05] 1996-2003 –> 2004-2011 4.2756547 13200.1106
Indo-Pacific (27.95,28.05] 2004-2011 –> 2012-2019 4.6317735 14118.4406
Indo-Pacific (28.05,28.1] 1996-2003 –> 2004-2011 3.6708544 8676.5966
Indo-Pacific (28.05,28.1] 2004-2011 –> 2012-2019 3.9507623 9000.7736
Indo-Pacific (28.1, Inf] 1996-2003 –> 2004-2011 2.7648364 62878.5389
Indo-Pacific (28.1, Inf] 2004-2011 –> 2012-2019 2.9944316 65436.4358

5.4 Target variable coefficients

A data frame to map the target variable is prepared.

# create table with two era belonging to one eras
eras_forward <- lm_all_fitted_wide %>%
  arrange(era) %>% 
  group_by(basin, gamma_slab, model) %>% 
  mutate(eras = paste(era, lead(era), sep = " --> ")) %>% 
  ungroup() %>% 
  select(era, eras) %>% 
  unique()

eras_backward <- lm_all_fitted_wide %>%
  arrange(era) %>% 
  group_by(basin, gamma_slab, model) %>% 
  mutate(eras = paste(lag(era), era, sep = " --> ")) %>% 
  ungroup() %>% 
  select(era, eras) %>% 
  unique()

eras_era <- full_join(eras_backward, eras_forward) %>% 
  filter(str_detect(eras, "NA") == FALSE)

# extend best model selection from eras to era
lm_best_target <- full_join(
  lm_best %>% select(basin, gamma_slab, model, eras),
  eras_era)

lm_best_target <- left_join(lm_best_target, lm_all_fitted_wide)

rm(eras_era, eras_forward, eras_backward,
   lm_all_fitted)

5.5 Plot selected model residuals

# plot model diagnostics, if activated
if (params_local$plot_all_figures == "y") {
  # mutate predictors column
  lm_best_plot <- lm_best_target %>%
    select(basin, gamma_slab, model, eras, era) %>%
    mutate(
      predictors = str_remove(model, paste(params_local$MLR_target, "~ ")),
      predictors = str_replace_all(predictors, "\\+ ", "")
    )
  
  # loop across all basins, era, gamma slabs, and MLRs
  # fit all MLR models
  for (i_basin in unique(GLODAP$basin)) {
    for (i_era in unique(GLODAP$era)) {
      #i_basin <- unique(GLODAP$basin)[1]
      #i_era   <- unique(GLODAP$era)[2]
      print(i_basin)
      print(i_era)
      
      GLODAP_basin_era <- GLODAP %>%
        filter(basin == i_basin, era == i_era)
      
      for (i_gamma_slab in unique(GLODAP_basin_era$gamma_slab)) {
        #i_gamma_slab <- unique(GLODAP_basin_era$gamma_slab)[1]
        print(i_gamma_slab)
        
        GLODAP_basin_era_slab <- GLODAP_basin_era %>%
          filter(gamma_slab == i_gamma_slab)
        
        lm_best_basin_era_slab <- lm_best_plot %>%
          filter(basin == i_basin, era == i_era, gamma_slab == i_gamma_slab)
        
        for (i_eras in unique(lm_best_basin_era_slab$eras)) {
          #i_eras <- unique(lm_best_basin_era_slab$eras)[1]
          print(i_eras)
          
          lm_best_basin_era_slab_eras <- lm_best_basin_era_slab %>%
            filter(eras == i_eras)
          
          for (i_predictors in unique(lm_best_basin_era_slab_eras$predictors)) {
            #i_predictors <- unique(lm_best_basin_era_slab$predictors)[1]
            print(i_predictors)
            # extract one model definition
            i_lm <- lm_all %>%
              filter(predictors == i_predictors) %>%
              select(lm_coeff) %>%
              pull()
            
             # fit model
          if (params_local$MLR_type == "rlm") {
            i_lm_fit <- MASS::rlm(as.formula(i_lm),
                                  data = GLODAP_basin_era_slab)
          }
          
          if (params_local$MLR_type == "lm") {
            i_lm_fit <- lm(as.formula(i_lm),
                           data = GLODAP_basin_era_slab)
          }
            
            # plot model diagnostics vs predictors
            
            p_model_predictors <- ggnostic(
              i_lm_fit,
              columnsY = c(params_local$MLR_target, ".fitted", ".resid"),
              title = paste(
                "era:",
                i_era,
                "| eras:",
                i_eras,
                "| basin:",
                i_basin,
                "| gamma slab:",
                i_gamma_slab,
                "| predictors:",
                i_predictors
              )
            )
            
            ggsave(
              plot = p_model_predictors,
              path = paste(path_version_figures, "eMLR_diagnostics/", sep = ""),
              filename = paste(
                "MLR_residuals",
                i_era,
                i_eras,
                i_basin,
                i_gamma_slab,
                i_predictors,
                "predictors.png",
                sep = "_"
              ),
              width = 14,
              height = 8
            )

            rm(p_model_predictors)
            
            
            # plot model diagnostics vs location
            
            GLODAP_basin_era_slab <- GLODAP_basin_era_slab %>%
              mutate(fitted = i_lm_fit$fitted.values,
                     residuals = i_lm_fit$residuals)
            
            GLODAP_basin_era_slab_long <- GLODAP_basin_era_slab %>% 
              pivot_longer(cols = c(params_local$MLR_target , fitted, residuals),
                           names_to = "estimate",
                           values_to = "value"
              ) %>% 
              pivot_longer(cols = c(lat, lon, depth),
                           names_to = "coordinate_type",
                           values_to = "coordinate_value"
              )

             p_model_coordinate <- GLODAP_basin_era_slab_long %>%
              ggplot(aes(coordinate_value, value)) +
              geom_bin2d() +
              scale_fill_viridis_c() +
              labs(
                title = paste(
                  "era:",
                  i_era,
                  "| eras:",
                  i_eras,
                  "| basin:",
                  i_basin,
                  "| gamma slab:",
                  i_gamma_slab,
                  "| predictors:",
                  i_predictors
                )
              ) +
              facet_grid(estimate~coordinate_type,
                         scales = "free")

            ggsave(
              plot = p_model_coordinate,
              path = paste(path_version_figures, "eMLR_diagnostics", sep = ""),
              filename = paste(
                "Location_MLR_residuals",
                i_era,
                i_eras,
                i_basin,
                i_gamma_slab,
                i_predictors,
                "predictors.png",
                sep = "_"
              ),
              width = 14,
              height = 8
            )

            rm(p_model_coordinate)
            
          }
          
        }
        
      }
    }
  }
  
  rm(
    lm_best_plot,
    lm_best_basin_era_slab,
    i_rmse,
    GLODAP_basin_era,
    GLODAP_basin_era_slab,
    i_lm,
    lm_all_fitted,
    i_basin,
    i_era,
    i_gamma_slab,
    i_predictors,
    lm_all,
    i_aic,
    i_n_predictors,
    i_resid_max
  )
  
}

Individual residual plots of the MLR models for each basin, era, eras and neutral density (gamma) slab are available at:

/nfs/kryo/work/jenmueller/emlr_cant/model/v_XXX/figures/eMLR_diagnostics/

5.6 Cant coeffcients

A data frame of coefficient offsets is prepared to facilitate the direct mapping of Cant.

# pivot long format
lm_best_long <- lm_best_target %>%
  pivot_longer(cols = starts_with("coeff_"),
               names_to = "term",
               values_to = "estimate",
               names_prefix = "coeff_")

# subtract coefficients of adjacent era  
lm_best_long <- lm_best_long %>%
  arrange(era) %>%
  group_by(basin, gamma_slab, eras, model, term) %>%
  mutate(delta_coeff = estimate - lag(estimate)) %>%
  ungroup() %>%
  arrange(basin, gamma_slab, model, term, eras) %>%
  drop_na() %>%
  select(-c(era,estimate))

# pivot back to wide format
lm_best_cant <- lm_best_long %>%
  pivot_wider(values_from = delta_coeff,
              names_from = term,
              names_prefix = "delta_coeff_",
              values_fill = 0)

5.7 Write files

lm_best_target %>%
  select(
    basin,
    gamma_slab,
    model,
    eras,
    era,
    starts_with("coeff_")
  ) %>%
  write_csv(paste(path_version_data,
                  "lm_best_target.csv",
                  sep = ""))

lm_best_cant %>%
  select(
    basin,
    gamma_slab,
    model,
    eras,
    starts_with("delta_coeff_")
  ) %>%
  write_csv(paste(path_version_data,
                  "lm_best_cant.csv",
                  sep = ""))

6 Model diagnotics

6.1 Selection criterion vs predictors

The selection criterion (aic) was plotted against the number of predictors (limited to 3 - 9).

6.1.1 All models

lm_all_fitted_wide %>%
  ggplot(aes(as.factor(n_predictors),
             !!sym(params_local$MLR_criterion),
             col = basin)) +
  geom_hline(yintercept = 10) +
  geom_boxplot() +
  facet_grid(gamma_slab~era) +
  scale_color_brewer(palette = "Set1") +
  labs(x="Number of predictors")

Version Author Date
e2a5a33 Donghe-Zhu 2021-03-04
c7892c1 Donghe-Zhu 2021-03-04
924430b Donghe-Zhu 2021-03-03
0d0bca1 Donghe-Zhu 2021-03-03
cb63c16 Donghe-Zhu 2021-03-03
ffda45a Donghe-Zhu 2021-03-03
691ba81 Donghe-Zhu 2021-03-03
c5e45a2 Donghe-Zhu 2021-03-03
89c3e58 Donghe-Zhu 2021-03-03
c911669 Donghe-Zhu 2021-03-03
b71c719 Donghe-Zhu 2021-03-01
13666ca Donghe-Zhu 2021-03-01
c6e60fe Donghe-Zhu 2021-03-01
7a388f7 Donghe-Zhu 2021-03-01
799e913 Donghe-Zhu 2021-03-01
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
e9a7418 Donghe-Zhu 2021-02-28
e152917 Donghe-Zhu 2021-02-28
287123c Donghe-Zhu 2021-02-27
54d5b5b Donghe-Zhu 2021-02-27
330f064 Donghe-Zhu 2021-02-27
adbc9bc Donghe-Zhu 2021-02-27
5937141 Donghe-Zhu 2021-02-27
4414bbf Donghe-Zhu 2021-02-27
a265efb Donghe-Zhu 2021-02-27
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
02b976d Donghe-Zhu 2021-02-24
354c224 Donghe-Zhu 2021-02-24
1a0a88a Donghe-Zhu 2021-02-24
57f701e Donghe-Zhu 2021-02-24
06f3149 Donghe-Zhu 2021-02-16
401eab3 Donghe-Zhu 2021-02-15
e3bba84 Donghe-Zhu 2021-02-15
5dce4b1 Donghe-Zhu 2021-02-15
4469a0c Donghe-Zhu 2021-02-13
5ae6a69 Donghe-Zhu 2021-02-10
05385dc Donghe-Zhu 2021-02-10
f791ae4 Donghe-Zhu 2021-02-09
f71ae34 Donghe-Zhu 2021-02-09
c011832 Donghe-Zhu 2021-02-09
a145fa7 Donghe-Zhu 2021-02-09
c344e42 Donghe-Zhu 2021-02-08
2f095d7 Donghe-Zhu 2021-02-07
1fad5f1 Donghe-Zhu 2021-02-07
ca03c39 Donghe-Zhu 2021-02-07
cd7c52c Donghe-Zhu 2021-02-04
bcf84f4 Donghe-Zhu 2021-02-02
a518739 Donghe-Zhu 2021-02-01
61666de Donghe-Zhu 2021-01-31
865b582 Donghe-Zhu 2021-01-31
3e68089 Donghe-Zhu 2021-01-31
ecf335c Donghe-Zhu 2021-01-31
a618965 Donghe-Zhu 2021-01-31
59e006e Donghe-Zhu 2021-01-31
a1c8f87 Donghe-Zhu 2021-01-31
ae5c18f Donghe-Zhu 2021-01-31
b50fe52 Donghe-Zhu 2021-01-31
ac99ae5 jens-daniel-mueller 2021-01-29
b5bdcaf Donghe-Zhu 2021-01-29
442010d Donghe-Zhu 2021-01-29
372adf5 Donghe-Zhu 2021-01-29
af8788e Donghe-Zhu 2021-01-29
21c91c9 Donghe-Zhu 2021-01-29
eded038 Donghe-Zhu 2021-01-29
541d4dd Donghe-Zhu 2021-01-29
6a75576 Donghe-Zhu 2021-01-28
16fba40 Donghe-Zhu 2021-01-28
12bc567 Donghe-Zhu 2021-01-27
ceed31b Donghe-Zhu 2021-01-27
342402d Donghe-Zhu 2021-01-27
5bad5c2 Donghe-Zhu 2021-01-27
61efb56 Donghe-Zhu 2021-01-25
48f638e Donghe-Zhu 2021-01-25
c1cec47 Donghe-Zhu 2021-01-25
05ffb0c Donghe-Zhu 2021-01-25
8b97165 Donghe-Zhu 2021-01-25
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
17dee1d jens-daniel-mueller 2021-01-13
7cdea0c jens-daniel-mueller 2021-01-06
fa85b93 jens-daniel-mueller 2021-01-06
e5cb81a Donghe-Zhu 2021-01-05
a499f10 Donghe-Zhu 2021-01-05
fb8a752 Donghe-Zhu 2020-12-23
8fae0b2 Donghe-Zhu 2020-12-21
c8b76b3 jens-daniel-mueller 2020-12-19

6.1.2 Best models

lm_best_target %>%
  ggplot(aes("",
             !!sym(params_local$MLR_criterion),
             col = basin)) +
  geom_hline(yintercept = 10) +
  geom_boxplot() +
  facet_grid(gamma_slab~era) +
  scale_color_brewer(palette = "Set1") +
  labs(x="Number of predictors pooled")

Version Author Date
e2a5a33 Donghe-Zhu 2021-03-04
c7892c1 Donghe-Zhu 2021-03-04
924430b Donghe-Zhu 2021-03-03
0d0bca1 Donghe-Zhu 2021-03-03
cb63c16 Donghe-Zhu 2021-03-03
ffda45a Donghe-Zhu 2021-03-03
691ba81 Donghe-Zhu 2021-03-03
c5e45a2 Donghe-Zhu 2021-03-03
89c3e58 Donghe-Zhu 2021-03-03
c407a50 Donghe-Zhu 2021-03-03
c911669 Donghe-Zhu 2021-03-03
b71c719 Donghe-Zhu 2021-03-01
13666ca Donghe-Zhu 2021-03-01
c6e60fe Donghe-Zhu 2021-03-01
7a388f7 Donghe-Zhu 2021-03-01
799e913 Donghe-Zhu 2021-03-01
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
e9a7418 Donghe-Zhu 2021-02-28
e152917 Donghe-Zhu 2021-02-28
287123c Donghe-Zhu 2021-02-27
54d5b5b Donghe-Zhu 2021-02-27
330f064 Donghe-Zhu 2021-02-27
adbc9bc Donghe-Zhu 2021-02-27
5937141 Donghe-Zhu 2021-02-27
4414bbf Donghe-Zhu 2021-02-27
a265efb Donghe-Zhu 2021-02-27
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
354c224 Donghe-Zhu 2021-02-24
1a0a88a Donghe-Zhu 2021-02-24
57f701e Donghe-Zhu 2021-02-24
06f3149 Donghe-Zhu 2021-02-16
401eab3 Donghe-Zhu 2021-02-15
e3bba84 Donghe-Zhu 2021-02-15
5dce4b1 Donghe-Zhu 2021-02-15
4469a0c Donghe-Zhu 2021-02-13
5ae6a69 Donghe-Zhu 2021-02-10
05385dc Donghe-Zhu 2021-02-10
f791ae4 Donghe-Zhu 2021-02-09
f71ae34 Donghe-Zhu 2021-02-09
c011832 Donghe-Zhu 2021-02-09
a145fa7 Donghe-Zhu 2021-02-09
c344e42 Donghe-Zhu 2021-02-08
2f095d7 Donghe-Zhu 2021-02-07
1fad5f1 Donghe-Zhu 2021-02-07
ca03c39 Donghe-Zhu 2021-02-07
e2ffc14 Donghe-Zhu 2021-02-05
cd7c52c Donghe-Zhu 2021-02-04
bcf84f4 Donghe-Zhu 2021-02-02
a518739 Donghe-Zhu 2021-02-01
61666de Donghe-Zhu 2021-01-31
865b582 Donghe-Zhu 2021-01-31
3e68089 Donghe-Zhu 2021-01-31
ecf335c Donghe-Zhu 2021-01-31
a618965 Donghe-Zhu 2021-01-31
59e006e Donghe-Zhu 2021-01-31
a1c8f87 Donghe-Zhu 2021-01-31
ae5c18f Donghe-Zhu 2021-01-31
b50fe52 Donghe-Zhu 2021-01-31
ac99ae5 jens-daniel-mueller 2021-01-29
b5bdcaf Donghe-Zhu 2021-01-29
442010d Donghe-Zhu 2021-01-29
372adf5 Donghe-Zhu 2021-01-29
af8788e Donghe-Zhu 2021-01-29
21c91c9 Donghe-Zhu 2021-01-29
eded038 Donghe-Zhu 2021-01-29
541d4dd Donghe-Zhu 2021-01-29
6a75576 Donghe-Zhu 2021-01-28
16fba40 Donghe-Zhu 2021-01-28
12bc567 Donghe-Zhu 2021-01-27
ceed31b Donghe-Zhu 2021-01-27
342402d Donghe-Zhu 2021-01-27
5bad5c2 Donghe-Zhu 2021-01-27
61efb56 Donghe-Zhu 2021-01-25
48f638e Donghe-Zhu 2021-01-25
c1cec47 Donghe-Zhu 2021-01-25
05ffb0c Donghe-Zhu 2021-01-25
8b97165 Donghe-Zhu 2021-01-25
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
17dee1d jens-daniel-mueller 2021-01-13
7cdea0c jens-daniel-mueller 2021-01-06
fa85b93 jens-daniel-mueller 2021-01-06
e5cb81a Donghe-Zhu 2021-01-05
a499f10 Donghe-Zhu 2021-01-05
fb8a752 Donghe-Zhu 2020-12-23
8fae0b2 Donghe-Zhu 2020-12-21
c8b76b3 jens-daniel-mueller 2020-12-19

6.2 RMSE correlation between eras

RMSE was plotted to compare the agreement for one model applied to two adjacent eras (ie check whether the same predictor combination performs equal in both eras).

6.2.1 All models

# find max rmse to scale axis
max_rmse <-
  max(c(lm_all_fitted_wide_eras$rmse,
        lm_all_fitted_wide_eras$rmse_sum - lm_all_fitted_wide_eras$rmse))

lm_all_fitted_wide_eras %>%
  ggplot(aes(rmse, rmse_sum - rmse, col = gamma_slab)) +
  geom_point() +
  scale_color_viridis_d() +
  coord_equal(xlim = c(0,max_rmse),
              ylim = c(0,max_rmse)) +
  facet_grid(eras ~ basin)

Version Author Date
e2a5a33 Donghe-Zhu 2021-03-04
c7892c1 Donghe-Zhu 2021-03-04
924430b Donghe-Zhu 2021-03-03
0d0bca1 Donghe-Zhu 2021-03-03
cb63c16 Donghe-Zhu 2021-03-03
ffda45a Donghe-Zhu 2021-03-03
691ba81 Donghe-Zhu 2021-03-03
c5e45a2 Donghe-Zhu 2021-03-03
89c3e58 Donghe-Zhu 2021-03-03
c911669 Donghe-Zhu 2021-03-03
13666ca Donghe-Zhu 2021-03-01
c6e60fe Donghe-Zhu 2021-03-01
7a388f7 Donghe-Zhu 2021-03-01
799e913 Donghe-Zhu 2021-03-01
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
e9a7418 Donghe-Zhu 2021-02-28
287123c Donghe-Zhu 2021-02-27
54d5b5b Donghe-Zhu 2021-02-27
330f064 Donghe-Zhu 2021-02-27
adbc9bc Donghe-Zhu 2021-02-27
5937141 Donghe-Zhu 2021-02-27
4414bbf Donghe-Zhu 2021-02-27
a265efb Donghe-Zhu 2021-02-27
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
02b976d Donghe-Zhu 2021-02-24
354c224 Donghe-Zhu 2021-02-24
1a0a88a Donghe-Zhu 2021-02-24
57f701e Donghe-Zhu 2021-02-24
06f3149 Donghe-Zhu 2021-02-16
e3bba84 Donghe-Zhu 2021-02-15
5dce4b1 Donghe-Zhu 2021-02-15
4469a0c Donghe-Zhu 2021-02-13
5ae6a69 Donghe-Zhu 2021-02-10
05385dc Donghe-Zhu 2021-02-10
f791ae4 Donghe-Zhu 2021-02-09
f71ae34 Donghe-Zhu 2021-02-09
c011832 Donghe-Zhu 2021-02-09
a145fa7 Donghe-Zhu 2021-02-09
c344e42 Donghe-Zhu 2021-02-08
2f095d7 Donghe-Zhu 2021-02-07
1fad5f1 Donghe-Zhu 2021-02-07
ca03c39 Donghe-Zhu 2021-02-07
cd7c52c Donghe-Zhu 2021-02-04
bcf84f4 Donghe-Zhu 2021-02-02
61666de Donghe-Zhu 2021-01-31
865b582 Donghe-Zhu 2021-01-31
3e68089 Donghe-Zhu 2021-01-31
ecf335c Donghe-Zhu 2021-01-31
a618965 Donghe-Zhu 2021-01-31
59e006e Donghe-Zhu 2021-01-31
a1c8f87 Donghe-Zhu 2021-01-31
ae5c18f Donghe-Zhu 2021-01-31
b50fe52 Donghe-Zhu 2021-01-31
ac99ae5 jens-daniel-mueller 2021-01-29
b5bdcaf Donghe-Zhu 2021-01-29
442010d Donghe-Zhu 2021-01-29
372adf5 Donghe-Zhu 2021-01-29
af8788e Donghe-Zhu 2021-01-29
21c91c9 Donghe-Zhu 2021-01-29
eded038 Donghe-Zhu 2021-01-29
541d4dd Donghe-Zhu 2021-01-29
6a75576 Donghe-Zhu 2021-01-28
16fba40 Donghe-Zhu 2021-01-28
12bc567 Donghe-Zhu 2021-01-27
ceed31b Donghe-Zhu 2021-01-27
342402d Donghe-Zhu 2021-01-27
5bad5c2 Donghe-Zhu 2021-01-27
61efb56 Donghe-Zhu 2021-01-25
48f638e Donghe-Zhu 2021-01-25
c1cec47 Donghe-Zhu 2021-01-25
05ffb0c Donghe-Zhu 2021-01-25
8b97165 Donghe-Zhu 2021-01-25
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
17dee1d jens-daniel-mueller 2021-01-13
7cdea0c jens-daniel-mueller 2021-01-06
fa85b93 jens-daniel-mueller 2021-01-06
e5cb81a Donghe-Zhu 2021-01-05
a499f10 Donghe-Zhu 2021-01-05
fb8a752 Donghe-Zhu 2020-12-23
8fae0b2 Donghe-Zhu 2020-12-21
c8b76b3 jens-daniel-mueller 2020-12-19 
rm(max_rmse)

6.2.2 Best models

# find max rmse to scale axis
max_rmse <-
  max(c(lm_best$rmse,
        lm_best$rmse_sum - lm_best$rmse))

lm_best %>%
  ggplot(aes(rmse, rmse_sum - rmse, col = gamma_slab)) +
  geom_point() +
  scale_color_viridis_d() +
  coord_equal(xlim = c(0,max_rmse),
              ylim = c(0,max_rmse)) +
  facet_grid(eras ~ basin)

Version Author Date
e2a5a33 Donghe-Zhu 2021-03-04
c7892c1 Donghe-Zhu 2021-03-04
924430b Donghe-Zhu 2021-03-03
0d0bca1 Donghe-Zhu 2021-03-03
cb63c16 Donghe-Zhu 2021-03-03
ffda45a Donghe-Zhu 2021-03-03
691ba81 Donghe-Zhu 2021-03-03
c5e45a2 Donghe-Zhu 2021-03-03
89c3e58 Donghe-Zhu 2021-03-03
c407a50 Donghe-Zhu 2021-03-03
c911669 Donghe-Zhu 2021-03-03
b71c719 Donghe-Zhu 2021-03-01
13666ca Donghe-Zhu 2021-03-01
c6e60fe Donghe-Zhu 2021-03-01
7a388f7 Donghe-Zhu 2021-03-01
799e913 Donghe-Zhu 2021-03-01
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
e9a7418 Donghe-Zhu 2021-02-28
e152917 Donghe-Zhu 2021-02-28
287123c Donghe-Zhu 2021-02-27
54d5b5b Donghe-Zhu 2021-02-27
330f064 Donghe-Zhu 2021-02-27
adbc9bc Donghe-Zhu 2021-02-27
5937141 Donghe-Zhu 2021-02-27
4414bbf Donghe-Zhu 2021-02-27
a265efb Donghe-Zhu 2021-02-27
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
354c224 Donghe-Zhu 2021-02-24
1a0a88a Donghe-Zhu 2021-02-24
57f701e Donghe-Zhu 2021-02-24
06f3149 Donghe-Zhu 2021-02-16
401eab3 Donghe-Zhu 2021-02-15
e3bba84 Donghe-Zhu 2021-02-15
5dce4b1 Donghe-Zhu 2021-02-15
4469a0c Donghe-Zhu 2021-02-13
5ae6a69 Donghe-Zhu 2021-02-10
05385dc Donghe-Zhu 2021-02-10
f791ae4 Donghe-Zhu 2021-02-09
f71ae34 Donghe-Zhu 2021-02-09
c011832 Donghe-Zhu 2021-02-09
a145fa7 Donghe-Zhu 2021-02-09
c344e42 Donghe-Zhu 2021-02-08
2f095d7 Donghe-Zhu 2021-02-07
1fad5f1 Donghe-Zhu 2021-02-07
ca03c39 Donghe-Zhu 2021-02-07
e2ffc14 Donghe-Zhu 2021-02-05
cd7c52c Donghe-Zhu 2021-02-04
bcf84f4 Donghe-Zhu 2021-02-02
a518739 Donghe-Zhu 2021-02-01
61666de Donghe-Zhu 2021-01-31
865b582 Donghe-Zhu 2021-01-31
3e68089 Donghe-Zhu 2021-01-31
ecf335c Donghe-Zhu 2021-01-31
a618965 Donghe-Zhu 2021-01-31
59e006e Donghe-Zhu 2021-01-31
a1c8f87 Donghe-Zhu 2021-01-31
ae5c18f Donghe-Zhu 2021-01-31
b50fe52 Donghe-Zhu 2021-01-31
ac99ae5 jens-daniel-mueller 2021-01-29
b5bdcaf Donghe-Zhu 2021-01-29
442010d Donghe-Zhu 2021-01-29
372adf5 Donghe-Zhu 2021-01-29
af8788e Donghe-Zhu 2021-01-29
21c91c9 Donghe-Zhu 2021-01-29
eded038 Donghe-Zhu 2021-01-29
541d4dd Donghe-Zhu 2021-01-29
6a75576 Donghe-Zhu 2021-01-28
16fba40 Donghe-Zhu 2021-01-28
12bc567 Donghe-Zhu 2021-01-27
ceed31b Donghe-Zhu 2021-01-27
342402d Donghe-Zhu 2021-01-27
5bad5c2 Donghe-Zhu 2021-01-27
61efb56 Donghe-Zhu 2021-01-25
48f638e Donghe-Zhu 2021-01-25
c1cec47 Donghe-Zhu 2021-01-25
05ffb0c Donghe-Zhu 2021-01-25
8b97165 Donghe-Zhu 2021-01-25
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
17dee1d jens-daniel-mueller 2021-01-13
7cdea0c jens-daniel-mueller 2021-01-06
fa85b93 jens-daniel-mueller 2021-01-06
e5cb81a Donghe-Zhu 2021-01-05
a499f10 Donghe-Zhu 2021-01-05
fb8a752 Donghe-Zhu 2020-12-23
8fae0b2 Donghe-Zhu 2020-12-21
c8b76b3 jens-daniel-mueller 2020-12-19 
rm(max_rmse)

6.3 Predictor counts

The number of models where a particular predictor was included were counted for each basin, density slab and compared eras

# calculate cases of predictor used
lm_all_stats <- lm_best_long %>% 
  filter(term != "(Intercept)",
         delta_coeff != 0) %>% 
  group_by(basin, eras, gamma_slab) %>% 
  count(term) %>% 
  ungroup() %>% 
  pivot_wider(values_from = n,
              names_from = term,
              values_fill = 0)

# print table
lm_all_stats %>%
  gt(rowname_col = "gamma_slab",
     groupname_col = c("basin", "eras")) %>% 
  summary_rows(
    groups = TRUE,
    fns = list(total = "sum")
  )
aou nitrate phosphate phosphate_star sal silicate temp
Atlantic - 1996-2003 --> 2004-2011
(-Inf,26] 5 1 4 5 3 2 2
(26,26.5] 5 3 2 4 1 4 4
(26.5,26.75] 5 1 4 4 2 5 2
(26.75,27] 5 1 4 4 1 5 3
(27,27.25] 5 2 2 4 4 5 1
(27.25,27.5] 5 4 1 3 3 5 2
(27.5,27.75] 5 1 4 3 5 3 0
(27.75,27.85] 5 2 3 3 5 4 0
(27.85,27.95] 5 0 5 5 2 3 2
(27.95,28.05] 5 2 2 5 1 4 4
(28.05,28.1] 5 1 4 5 3 3 2
(28.1,28.15] 5 1 4 5 3 4 1
(28.15,28.2] 5 5 0 4 2 4 2
(28.2, Inf] 5 5 0 3 4 3 1
total 70.00 29.00 39.00 57.00 39.00 54.00 26.00
Atlantic - 2004-2011 --> 2012-2019
(-Inf,26] 5 0 4 5 3 3 2
(26,26.5] 5 3 2 4 1 4 4
(26.5,26.75] 5 1 4 4 2 5 2
(26.75,27] 5 1 4 4 2 5 2
(27,27.25] 4 1 3 4 5 5 0
(27.25,27.5] 5 5 0 3 2 5 2
(27.5,27.75] 5 2 3 3 5 4 0
(27.75,27.85] 5 2 3 3 5 4 0
(27.85,27.95] 5 0 5 5 2 3 2
(27.95,28.05] 5 2 3 5 2 3 3
(28.05,28.1] 5 1 4 5 3 3 2
(28.1,28.15] 5 1 4 5 3 3 2
(28.15,28.2] 5 5 0 4 2 3 3
(28.2, Inf] 5 5 0 3 4 3 1
total 69.00 29.00 39.00 57.00 41.00 53.00 25.00
Indo-Pacific - 1996-2003 --> 2004-2011
(-Inf,26] 4 1 4 5 2 4 3
(26,26.5] 5 1 4 4 1 5 3
(26.5,26.75] 4 2 3 5 1 5 3
(26.75,27] 5 0 5 5 2 3 2
(27,27.25] 5 1 4 5 1 4 3
(27.25,27.5] 5 2 2 5 3 5 2
(27.5,27.75] 3 2 2 5 5 5 0
(27.75,27.85] 4 2 3 5 1 5 3
(27.85,27.95] 4 5 0 3 0 3 5
(27.95,28.05] 5 2 3 5 4 3 1
(28.05,28.1] 5 1 4 4 1 5 3
(28.1, Inf] 5 2 3 4 3 5 2
total 54.00 21.00 37.00 55.00 24.00 52.00 30.00
Indo-Pacific - 2004-2011 --> 2012-2019
(-Inf,26] 4 1 4 5 2 4 3
(26,26.5] 5 1 4 4 1 5 3
(26.5,26.75] 4 2 3 5 1 5 3
(26.75,27] 5 0 5 5 2 3 2
(27,27.25] 5 1 4 5 1 4 3
(27.25,27.5] 5 2 2 5 3 5 2
(27.5,27.75] 4 2 2 5 4 5 1
(27.75,27.85] 4 2 3 5 3 4 2
(27.85,27.95] 4 3 2 4 0 4 5
(27.95,28.05] 5 2 3 5 3 3 2
(28.05,28.1] 5 1 4 4 1 5 3
(28.1, Inf] 5 2 3 4 3 5 2
total 55.00 19.00 39.00 56.00 24.00 52.00 31.00

6.4 RMSE alternatives

AIC is an alternative criterion to RMSE to judge model quality, but not (yet) taken into account.

lm_all_fitted_wide_eras %>% 
  ggplot(aes(rmse, aic, col = gamma_slab)) +
  geom_point() +
  scale_color_viridis_d() +
  facet_grid(eras~basin)

Version Author Date
e2a5a33 Donghe-Zhu 2021-03-04
c7892c1 Donghe-Zhu 2021-03-04
924430b Donghe-Zhu 2021-03-03
0d0bca1 Donghe-Zhu 2021-03-03
cb63c16 Donghe-Zhu 2021-03-03
ffda45a Donghe-Zhu 2021-03-03
691ba81 Donghe-Zhu 2021-03-03
c5e45a2 Donghe-Zhu 2021-03-03
89c3e58 Donghe-Zhu 2021-03-03
c911669 Donghe-Zhu 2021-03-03
13666ca Donghe-Zhu 2021-03-01
c6e60fe Donghe-Zhu 2021-03-01
7a388f7 Donghe-Zhu 2021-03-01
799e913 Donghe-Zhu 2021-03-01
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
e9a7418 Donghe-Zhu 2021-02-28
287123c Donghe-Zhu 2021-02-27
54d5b5b Donghe-Zhu 2021-02-27
330f064 Donghe-Zhu 2021-02-27
adbc9bc Donghe-Zhu 2021-02-27
5937141 Donghe-Zhu 2021-02-27
4414bbf Donghe-Zhu 2021-02-27
a265efb Donghe-Zhu 2021-02-27
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
02b976d Donghe-Zhu 2021-02-24
354c224 Donghe-Zhu 2021-02-24
1a0a88a Donghe-Zhu 2021-02-24
57f701e Donghe-Zhu 2021-02-24
06f3149 Donghe-Zhu 2021-02-16
e3bba84 Donghe-Zhu 2021-02-15
5dce4b1 Donghe-Zhu 2021-02-15
4469a0c Donghe-Zhu 2021-02-13
5ae6a69 Donghe-Zhu 2021-02-10
05385dc Donghe-Zhu 2021-02-10
f791ae4 Donghe-Zhu 2021-02-09
f71ae34 Donghe-Zhu 2021-02-09
c011832 Donghe-Zhu 2021-02-09
a145fa7 Donghe-Zhu 2021-02-09
c344e42 Donghe-Zhu 2021-02-08
2f095d7 Donghe-Zhu 2021-02-07
1fad5f1 Donghe-Zhu 2021-02-07
ca03c39 Donghe-Zhu 2021-02-07
cd7c52c Donghe-Zhu 2021-02-04
bcf84f4 Donghe-Zhu 2021-02-02
61666de Donghe-Zhu 2021-01-31
865b582 Donghe-Zhu 2021-01-31
3e68089 Donghe-Zhu 2021-01-31
ecf335c Donghe-Zhu 2021-01-31
a618965 Donghe-Zhu 2021-01-31
59e006e Donghe-Zhu 2021-01-31
a1c8f87 Donghe-Zhu 2021-01-31
ae5c18f Donghe-Zhu 2021-01-31
b50fe52 Donghe-Zhu 2021-01-31
ac99ae5 jens-daniel-mueller 2021-01-29
b5bdcaf Donghe-Zhu 2021-01-29
442010d Donghe-Zhu 2021-01-29
372adf5 Donghe-Zhu 2021-01-29
af8788e Donghe-Zhu 2021-01-29
21c91c9 Donghe-Zhu 2021-01-29
eded038 Donghe-Zhu 2021-01-29
541d4dd Donghe-Zhu 2021-01-29
6a75576 Donghe-Zhu 2021-01-28
16fba40 Donghe-Zhu 2021-01-28
12bc567 Donghe-Zhu 2021-01-27
ceed31b Donghe-Zhu 2021-01-27
342402d Donghe-Zhu 2021-01-27
5bad5c2 Donghe-Zhu 2021-01-27
61efb56 Donghe-Zhu 2021-01-25
48f638e Donghe-Zhu 2021-01-25
c1cec47 Donghe-Zhu 2021-01-25
05ffb0c Donghe-Zhu 2021-01-25
8b97165 Donghe-Zhu 2021-01-25
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
17dee1d jens-daniel-mueller 2021-01-13
7cdea0c jens-daniel-mueller 2021-01-06
fa85b93 jens-daniel-mueller 2021-01-06
e5cb81a Donghe-Zhu 2021-01-05
a499f10 Donghe-Zhu 2021-01-05
fb8a752 Donghe-Zhu 2020-12-23
8fae0b2 Donghe-Zhu 2020-12-21
c8b76b3 jens-daniel-mueller 2020-12-19 
lm_best %>% 
  ggplot(aes(rmse, aic, col = gamma_slab)) +
  geom_point() +
  scale_color_viridis_d() +
  facet_grid(eras~basin)

Version Author Date
e2a5a33 Donghe-Zhu 2021-03-04
c7892c1 Donghe-Zhu 2021-03-04
924430b Donghe-Zhu 2021-03-03
0d0bca1 Donghe-Zhu 2021-03-03
cb63c16 Donghe-Zhu 2021-03-03
ffda45a Donghe-Zhu 2021-03-03
691ba81 Donghe-Zhu 2021-03-03
c5e45a2 Donghe-Zhu 2021-03-03
89c3e58 Donghe-Zhu 2021-03-03
c407a50 Donghe-Zhu 2021-03-03
c911669 Donghe-Zhu 2021-03-03
b71c719 Donghe-Zhu 2021-03-01
13666ca Donghe-Zhu 2021-03-01
c6e60fe Donghe-Zhu 2021-03-01
7a388f7 Donghe-Zhu 2021-03-01
799e913 Donghe-Zhu 2021-03-01
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
e9a7418 Donghe-Zhu 2021-02-28
e152917 Donghe-Zhu 2021-02-28
287123c Donghe-Zhu 2021-02-27
54d5b5b Donghe-Zhu 2021-02-27
330f064 Donghe-Zhu 2021-02-27
adbc9bc Donghe-Zhu 2021-02-27
5937141 Donghe-Zhu 2021-02-27
4414bbf Donghe-Zhu 2021-02-27
a265efb Donghe-Zhu 2021-02-27
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
354c224 Donghe-Zhu 2021-02-24
1a0a88a Donghe-Zhu 2021-02-24
57f701e Donghe-Zhu 2021-02-24
06f3149 Donghe-Zhu 2021-02-16
401eab3 Donghe-Zhu 2021-02-15
e3bba84 Donghe-Zhu 2021-02-15
5dce4b1 Donghe-Zhu 2021-02-15
4469a0c Donghe-Zhu 2021-02-13
5ae6a69 Donghe-Zhu 2021-02-10
05385dc Donghe-Zhu 2021-02-10
f791ae4 Donghe-Zhu 2021-02-09
f71ae34 Donghe-Zhu 2021-02-09
c011832 Donghe-Zhu 2021-02-09
a145fa7 Donghe-Zhu 2021-02-09
c344e42 Donghe-Zhu 2021-02-08
2f095d7 Donghe-Zhu 2021-02-07
1fad5f1 Donghe-Zhu 2021-02-07
ca03c39 Donghe-Zhu 2021-02-07
e2ffc14 Donghe-Zhu 2021-02-05
cd7c52c Donghe-Zhu 2021-02-04
bcf84f4 Donghe-Zhu 2021-02-02
a518739 Donghe-Zhu 2021-02-01
61666de Donghe-Zhu 2021-01-31
865b582 Donghe-Zhu 2021-01-31
3e68089 Donghe-Zhu 2021-01-31
ecf335c Donghe-Zhu 2021-01-31
a618965 Donghe-Zhu 2021-01-31
59e006e Donghe-Zhu 2021-01-31
a1c8f87 Donghe-Zhu 2021-01-31
ae5c18f Donghe-Zhu 2021-01-31
b50fe52 Donghe-Zhu 2021-01-31
ac99ae5 jens-daniel-mueller 2021-01-29
b5bdcaf Donghe-Zhu 2021-01-29
442010d Donghe-Zhu 2021-01-29
372adf5 Donghe-Zhu 2021-01-29
af8788e Donghe-Zhu 2021-01-29
21c91c9 Donghe-Zhu 2021-01-29
eded038 Donghe-Zhu 2021-01-29
541d4dd Donghe-Zhu 2021-01-29
6a75576 Donghe-Zhu 2021-01-28
16fba40 Donghe-Zhu 2021-01-28
12bc567 Donghe-Zhu 2021-01-27
ceed31b Donghe-Zhu 2021-01-27
342402d Donghe-Zhu 2021-01-27
5bad5c2 Donghe-Zhu 2021-01-27
61efb56 Donghe-Zhu 2021-01-25
48f638e Donghe-Zhu 2021-01-25
c1cec47 Donghe-Zhu 2021-01-25
05ffb0c Donghe-Zhu 2021-01-25
8b97165 Donghe-Zhu 2021-01-25
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 Donghe-Zhu 2021-01-21
d4cf1cb Donghe-Zhu 2021-01-21
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
b3564aa jens-daniel-mueller 2021-01-14
8d032c3 jens-daniel-mueller 2021-01-14
17dee1d jens-daniel-mueller 2021-01-13
7cdea0c jens-daniel-mueller 2021-01-06
fa85b93 jens-daniel-mueller 2021-01-06
e5cb81a Donghe-Zhu 2021-01-05
a499f10 Donghe-Zhu 2021-01-05
fb8a752 Donghe-Zhu 2020-12-23
8fae0b2 Donghe-Zhu 2020-12-21
c8b76b3 jens-daniel-mueller 2020-12-19 

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] gt_0.2.2         corrr_0.4.3      broom_0.7.5      kableExtra_1.3.1
 [5] knitr_1.30       olsrr_0.5.3      GGally_2.0.0     lubridate_1.7.9 
 [9] metR_0.9.0       scico_1.2.0      patchwork_1.1.1  collapse_1.5.0  
[13] forcats_0.5.0    stringr_1.4.0    dplyr_1.0.2      purrr_0.3.4     
[17] readr_1.4.0      tidyr_1.1.2      tibble_3.0.4     ggplot2_3.3.3   
[21] tidyverse_1.3.0  workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] fs_1.5.0                 webshot_0.5.2            RColorBrewer_1.1-2      
 [4] httr_1.4.2               rprojroot_2.0.2          tools_4.0.3             
 [7] backports_1.1.10         R6_2.5.0                 nortest_1.0-4           
[10] DBI_1.1.0                colorspace_2.0-0         withr_2.3.0             
[13] gridExtra_2.3            tidyselect_1.1.0         curl_4.3                
[16] compiler_4.0.3           git2r_0.27.1             cli_2.2.0               
[19] rvest_0.3.6              xml2_1.3.2               sass_0.2.0              
[22] labeling_0.4.2           scales_1.1.1             checkmate_2.0.0         
[25] goftest_1.2-2            digest_0.6.27            foreign_0.8-80          
[28] rmarkdown_2.5            rio_0.5.16               pkgconfig_2.0.3         
[31] htmltools_0.5.0          highr_0.8                dbplyr_1.4.4            
[34] rlang_0.4.10             readxl_1.3.1             rstudioapi_0.13         
[37] farver_2.0.3             generics_0.1.0           jsonlite_1.7.2          
[40] zip_2.1.1                car_3.0-10               magrittr_2.0.1          
[43] Matrix_1.2-18            Rcpp_1.0.5               munsell_0.5.0           
[46] fansi_0.4.1              abind_1.4-5              lifecycle_0.2.0         
[49] stringi_1.5.3            whisker_0.4              yaml_2.2.1              
[52] carData_3.0-4            plyr_1.8.6               grid_4.0.3              
[55] blob_1.2.1               parallel_4.0.3           promises_1.1.1          
[58] crayon_1.3.4             lattice_0.20-41          haven_2.3.1             
[61] hms_0.5.3                pillar_1.4.7             reprex_0.3.0            
[64] glue_1.4.2               evaluate_0.14            RcppArmadillo_0.10.1.2.2
[67] data.table_1.13.6        modelr_0.1.8             vctrs_0.3.6             
[70] httpuv_1.5.4             cellranger_1.1.0         gtable_0.3.0            
[73] reshape_0.8.8            assertthat_0.2.1         xfun_0.20               
[76] openxlsx_4.2.3           RcppEigen_0.3.3.9.1      later_1.1.0.1           
[79] viridisLite_0.3.0        ellipsis_0.3.1           here_1.0.1