Last updated: 2021-08-06

Checks: 7 0

Knit directory: emlr_mod_v_XXX/

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Repository version: 90f70da

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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_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") &
           str_detect(lm_coeff_filter, "nitrate")) %>%
  select(-lm_coeff_filter)

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, "phosphate") &
           str_detect(lm_coeff_filter, "oxygen")) %>%
  select(-lm_coeff_filter)

lm_rm <- bind_rows(lm_rm_ph, lm_rm_si) %>%
  unique()

lm_all <- anti_join(lm_all, lm_rm)

# 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)

# 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
)

# lm_full <- lm(paste(
#   params_local$MLR_target,
#   paste(params_local$MLR_predictors, collapse = " + "),
#   sep = " ~ "
# ),
# data = GLODAP_basin_era_slab)
# 
# lm_full <-
#   loess(
#     cstar_tref ~ sal + temp + aou + phosphate,
#     span = 0.1,
#     degree = 1,
#     data = GLODAP_basin_era_slab
#   )

3 Apply predictor threshold

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

  • Minimum: 2
  • Maximum: 5
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:

  • 112

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)[100]
        
        # 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)
          
          # calculate max VIF
          if (is.na(max(i_lm_fit$coefficients))) {
            i_vif_max <- as.double("NA")
          } else{
            i_vif_max <- max(vif(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,
              vif_max = i_vif_max,
              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_vif_max,
  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

# lm_all_fitted <- lm_all_fitted_1

# lm_all_fitted <-
#   read_csv(paste(path_version_data,
#                   "lm_all_fitted.csv",
#                   sep = ""))

# select relevant columns
lm_all_fitted <- lm_all_fitted %>%
  select(
    basin,
    gamma_slab,
    era,
    model,
    nr_obs,
    n_predictors,
    term,
    estimate,
    rmse,
    aic,
    vif_max,
    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:

  • 10

The criterion used to select the best models was:

  • rmse

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)

5.3 VIF threshold

To avoid multicollinearity among predictors, models were excluded with a VIF above:

  • 1000

After removing models affected by multicollinearity, the targeted number of MLRs (10) was undercut in following fitting units:

# remove models with predictors fitted as NA
lm_all_fitted_wide <- lm_all_fitted_wide %>%
  filter(vif_max <= params_local$vif_max)

# lm_all_fitted_wide_check <- lm_all_fitted_wide %>%
#   group_by(era, basin, gamma_slab) %>% 
#   count() 
#   filter(n < params_local$MLR_number)
# calculate RMSE sum for adjacent eras
lm_all_fitted_wide_eras <- lm_all_fitted_wide %>%
  select(basin, gamma_slab, n_predictors, model, era, nr_obs, rmse, aic, vif_max, resid_max) %>%
  arrange(era) %>%
  group_by(basin, gamma_slab, model) %>%
  mutate(
    eras = paste(lag(era), era, sep = " --> "),
    rmse_sum = rmse + lag(rmse),
    rmse_lag = lag(rmse),
    aic_sum = aic + lag(aic),
    vif_max_sum = vif_max + lag(vif_max)
  ) %>%
  ungroup() %>%
  select(-c(era)) %>%
  drop_na() %>%
  filter(eras != "1982-1999 --> 2010-2019",
         eras != "1982-1999 --> 2008-2019")

# filter tripairs
lm_all_fitted_wide_eras <- lm_all_fitted_wide_eras %>%
  mutate(rmse = round(rmse, digits = 7), rmse_lag = round(rmse_lag, digits = 7)) %>%
  group_by(basin, gamma_slab, eras, rmse) %>%
  sample_n(1) %>%
  ungroup()

# calculate rank for each era
lm_all_fitted_wide_eras <- lm_all_fitted_wide_eras %>%
  group_by(eras, basin, gamma_slab) %>% 
  mutate(rank = rank(rmse, ties.method="first"),
         rank_sum = rank + rank(rmse_lag, ties.method="first")) %>%
  ungroup()

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

5.4 RMSE tables

5.4.1 per model

lm_best %>%
  kable() %>%
  add_header_above() %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "400px")
basin gamma_slab n_predictors model nr_obs rmse aic vif_max resid_max eras rmse_sum rmse_lag aic_sum vif_max_sum rank rank_sum
global (-Inf,26] 5 cstar_tref ~ sal + aou + nitrate + phosphate + phosphate_star 2590 5.5862374 16275.287 92.769898 23.712629 1982-1999 –> 2000-2009 10.796409 5.2101716 24755.109 199.10009 1 3
global (-Inf,26] 5 cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 2590 5.7673240 16440.540 92.144606 19.089016 1982-1999 –> 2000-2009 11.052842 5.2855180 24959.962 202.62262 2 6
global (-Inf,26] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 2590 6.0919418 16724.190 23.811276 23.237123 1982-1999 –> 2000-2009 11.234535 5.1425935 25168.006 54.21000 4 5
global (-Inf,26] 4 cstar_tref ~ sal + aou + phosphate + phosphate_star 2590 5.9054919 16561.174 91.201768 19.920787 1982-1999 –> 2000-2009 11.403350 5.4978580 25187.228 197.26461 3 10
global (-Inf,26] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 2590 6.3139584 16909.613 12.876238 26.818961 1982-1999 –> 2000-2009 11.576734 5.2627754 25417.142 25.25411 5 8
global (-Inf,26] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 2590 6.4145533 16991.491 15.625498 24.050568 1982-1999 –> 2000-2009 11.732347 5.3177943 25527.703 30.13552 6 11
global (-Inf,26] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 2590 6.4840545 17047.314 16.172196 18.961167 1982-1999 –> 2000-2009 11.852673 5.3686181 25609.760 41.86622 7 13
global (-Inf,26] 4 cstar_tref ~ sal + temp + aou + phosphate 2590 6.5370495 17087.479 15.619655 17.474055 1982-1999 –> 2000-2009 12.086844 5.5497947 25739.464 40.32529 8 17
global (-Inf,26] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 2590 6.8361119 17321.197 7.251537 20.235713 1982-1999 –> 2000-2009 12.371781 5.5356694 25968.153 13.21860 12 20
global (-Inf,26] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 2590 6.8301606 17316.685 10.135431 19.313201 1982-1999 –> 2000-2009 12.393534 5.5633737 25977.410 18.78986 11 21
global (-Inf,26] 5 cstar_tref ~ sal + aou + nitrate + phosphate + phosphate_star 3722 5.2653751 22942.197 148.310977 46.931268 2000-2009 –> 2010-2019 10.851612 5.5862374 39217.483 241.08087 1 2
global (-Inf,26] 5 cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3722 5.4407414 23186.084 157.152202 35.733806 2000-2009 –> 2010-2019 11.208065 5.7673240 39626.624 249.29681 2 4
global (-Inf,26] 4 cstar_tref ~ sal + aou + phosphate + phosphate_star 3722 5.5835627 23376.971 146.818837 29.890077 2000-2009 –> 2010-2019 11.489055 5.9054919 39938.145 238.02060 4 7
global (-Inf,26] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 3722 5.5216442 23295.960 30.750688 38.302760 2000-2009 –> 2010-2019 11.613586 6.0919418 40020.150 54.56196 3 7
global (-Inf,26] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 3722 5.6333808 23445.094 16.534015 42.135758 2000-2009 –> 2010-2019 11.947339 6.3139584 40354.707 29.41025 5 10
global (-Inf,26] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 3722 5.7277684 23568.785 19.932861 40.521932 2000-2009 –> 2010-2019 12.142322 6.4145533 40560.276 35.55836 6 12
global (-Inf,26] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 3722 5.9501116 23852.282 26.773235 21.522215 2000-2009 –> 2010-2019 12.434166 6.4840545 40899.596 42.94543 7 14
global (-Inf,26] 4 cstar_tref ~ sal + temp + aou + phosphate 3722 6.0345712 23955.204 25.775765 21.764364 2000-2009 –> 2010-2019 12.571621 6.5370495 41042.683 41.39542 8 16
global (-Inf,26] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 3722 6.2573828 24227.103 8.597696 22.106654 2000-2009 –> 2010-2019 12.920863 6.6634804 41415.810 16.68616 13 22
global (-Inf,26] 4 cstar_tref ~ sal + temp + nitrate + phosphate_star 3722 6.2634597 24232.329 8.558832 21.805942 2000-2009 –> 2010-2019 12.930688 6.6672283 41421.948 16.63463 15 25
global (26,26.5] 5 cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 2728 4.3157626 15733.896 553.899642 16.049558 1982-1999 –> 2000-2009 8.260599 3.9448361 29699.043 1433.52365 1 2
global (26,26.5] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 2728 4.3610247 15790.818 69.626622 20.069181 1982-1999 –> 2000-2009 8.530251 4.1692264 30032.470 133.11842 2 5
global (26,26.5] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 2728 4.4042959 15844.687 69.568012 15.767917 1982-1999 –> 2000-2009 8.555952 4.1516560 30065.231 135.10378 3 5
global (26,26.5] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 2728 4.5233175 15990.173 34.172473 16.534230 1982-1999 –> 2000-2009 8.792066 4.2687484 30349.729 62.00477 4 8
global (26,26.5] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 2728 4.5632070 16038.077 39.396121 16.085387 1982-1999 –> 2000-2009 8.861057 4.2978500 30431.590 70.04962 5 11
global (26,26.5] 5 cstar_tref ~ sal + aou + nitrate + phosphate + phosphate_star 2728 4.6242744 16110.608 526.591240 14.872500 1982-1999 –> 2000-2009 8.914044 4.2897694 30494.715 1445.28935 7 12
global (26,26.5] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 2728 4.6233617 16109.531 35.024174 20.528353 1982-1999 –> 2000-2009 8.971959 4.3485970 30561.712 60.38173 6 13
global (26,26.5] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 2728 4.6587637 16151.149 38.964104 20.564232 1982-1999 –> 2000-2009 9.033688 4.3749240 30633.498 65.64960 8 16
global (26,26.5] 5 cstar_tref ~ aou + nitrate + silicate + phosphate + phosphate_star 2728 4.8012583 16315.527 381.460007 13.855259 1982-1999 –> 2000-2009 9.311581 4.5103230 30950.213 899.81240 9 18
global (26,26.5] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 2728 4.8015821 16315.895 69.300862 17.947394 1982-1999 –> 2000-2009 9.427343 4.6257606 31076.891 137.51558 10 20
global (26,26.5] 5 cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3243 4.1632561 18468.201 643.423499 35.379501 2000-2009 –> 2010-2019 8.479019 4.3157626 34202.097 1197.32314 1 2
global (26,26.5] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 3243 4.3286022 18720.812 83.683271 14.554309 2000-2009 –> 2010-2019 8.689627 4.3610247 34511.631 153.30989 2 4
global (26,26.5] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 3243 4.4301956 18871.281 81.973726 19.220696 2000-2009 –> 2010-2019 8.834491 4.4042959 34715.969 151.54174 3 6
global (26,26.5] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 3243 4.5513510 19046.276 33.936748 18.619622 2000-2009 –> 2010-2019 9.074669 4.5233175 35036.449 68.10922 4 8
global (26,26.5] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 3243 4.5919749 19103.911 38.116045 18.401584 2000-2009 –> 2010-2019 9.155182 4.5632070 35141.988 77.51217 6 11
global (26,26.5] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 3243 4.5805619 19087.771 32.775572 14.792531 2000-2009 –> 2010-2019 9.203924 4.6233617 35197.302 67.79975 5 11
global (26,26.5] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 3243 4.6067941 19124.809 34.867003 14.917836 2000-2009 –> 2010-2019 9.265558 4.6587637 35275.958 73.83111 7 15
global (26,26.5] 5 cstar_tref ~ sal + aou + nitrate + phosphate + phosphate_star 3243 4.6944663 19247.085 647.312719 40.624491 2000-2009 –> 2010-2019 9.318741 4.6242744 35357.692 1173.90396 8 15
global (26,26.5] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 3243 4.8289469 19430.275 30.397215 16.392615 2000-2009 –> 2010-2019 9.748411 4.9194640 35878.501 60.80548 9 20
global (26,26.5] 4 cstar_tref ~ sal + temp + silicate + phosphate_star 3243 4.8324095 19432.924 30.039057 15.982789 2000-2009 –> 2010-2019 9.761481 4.9290712 35889.795 58.32100 10 22
global (26.5,26.75] 5 cstar_tref ~ aou + nitrate + silicate + phosphate + phosphate_star 2603 4.4661075 15191.863 302.300096 17.897775 1982-1999 –> 2000-2009 7.883819 3.4177118 27958.196 978.71451 2 3
global (26.5,26.75] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 2603 4.4398815 15161.202 48.478751 14.606190 1982-1999 –> 2000-2009 7.992719 3.5528380 28114.278 119.32549 1 4
global (26.5,26.75] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 2603 4.5704196 15312.058 49.994681 17.627757 1982-1999 –> 2000-2009 8.085261 3.5148417 28213.351 120.21374 5 7
global (26.5,26.75] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 2603 4.5187646 15252.884 47.779199 14.736929 1982-1999 –> 2000-2009 8.151237 3.6324722 28312.716 97.55370 3 10
global (26.5,26.75] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 2603 4.5247751 15259.804 50.342317 14.758065 1982-1999 –> 2000-2009 8.165372 3.6405973 28330.396 102.61606 4 12
global (26.5,26.75] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 2603 4.7001593 15457.781 13.008260 17.402877 1982-1999 –> 2000-2009 8.349547 3.6493874 28539.987 28.88978 9 18
global (26.5,26.75] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 2603 4.7505971 15513.349 51.689891 16.294671 1982-1999 –> 2000-2009 8.355947 3.6053501 28537.087 123.30343 12 16
global (26.5,26.75] 5 cstar_tref ~ temp + nitrate + silicate + phosphate + phosphate_star 2603 4.7168581 15476.244 19.310061 17.363239 1982-1999 –> 2000-2009 8.388826 3.6719675 28588.156 45.11947 10 20
global (26.5,26.75] 4 cstar_tref ~ aou + silicate + phosphate + phosphate_star 2603 4.6400479 15388.771 300.920171 16.771875 1982-1999 –> 2000-2009 8.395668 3.7556206 28607.168 911.72143 8 19
global (26.5,26.75] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 2603 4.7890696 15555.340 47.186301 16.417796 1982-1999 –> 2000-2009 8.405443 3.6163733 28593.780 97.26539 13 18
global (26.5,26.75] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 3101 4.2634618 17807.662 95.206369 15.516328 2000-2009 –> 2010-2019 8.703343 4.4398815 32968.864 143.68512 2 3
global (26.5,26.75] 5 cstar_tref ~ aou + nitrate + silicate + phosphate + phosphate_star 3101 4.2582252 17800.040 628.101553 17.043826 2000-2009 –> 2010-2019 8.724333 4.4661075 32991.902 930.40165 1 3
global (26.5,26.75] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 3101 4.4893629 18127.867 54.783307 15.738363 2000-2009 –> 2010-2019 9.008128 4.5187646 33380.751 102.56251 4 7
global (26.5,26.75] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 3101 4.5049730 18149.395 58.401292 15.750383 2000-2009 –> 2010-2019 9.029748 4.5247751 33409.199 108.74361 5 9
global (26.5,26.75] 4 cstar_tref ~ aou + silicate + phosphate + phosphate_star 3101 4.4249268 18036.204 607.815018 15.208494 2000-2009 –> 2010-2019 9.064975 4.6400479 33424.975 908.73519 3 11
global (26.5,26.75] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 3101 4.6151386 18299.235 90.257713 20.506245 2000-2009 –> 2010-2019 9.185558 4.5704196 33611.293 140.25239 6 11
global (26.5,26.75] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 3101 4.6352001 18326.136 50.456785 15.590673 2000-2009 –> 2010-2019 9.240842 4.6056420 33678.161 97.65835 7 13
global (26.5,26.75] 4 cstar_tref ~ sal + temp + silicate + phosphate_star 3101 4.6352433 18324.194 49.282218 15.583519 2000-2009 –> 2010-2019 9.260950 4.6257063 33696.849 96.39137 8 15
global (26.5,26.75] 4 cstar_tref ~ temp + aou + silicate + phosphate 3101 4.7965693 18536.378 89.963576 16.378399 2000-2009 –> 2010-2019 9.536123 4.7395534 34035.611 136.56146 9 20
global (26.5,26.75] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 3101 4.8144589 18561.467 95.085406 17.852008 2000-2009 –> 2010-2019 9.565056 4.7505971 34074.816 146.77530 10 22
global (26.75,27] 5 cstar_tref ~ aou + nitrate + silicate + phosphate + phosphate_star 3887 4.6542513 22999.538 303.870278 16.718326 1982-1999 –> 2000-2009 8.245458 3.5912072 42041.188 594.45535 1 2
global (26.75,27] 5 cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 3887 4.7725361 23194.641 748.696700 16.283493 1982-1999 –> 2000-2009 8.434044 3.6615082 42373.044 1637.65079 5 7
global (26.75,27] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 3887 4.7283231 23122.286 40.641008 16.479372 1982-1999 –> 2000-2009 8.482513 3.7541897 42477.021 73.47758 3 7
global (26.75,27] 4 cstar_tref ~ aou + silicate + phosphate + phosphate_star 3887 4.7989954 23235.621 301.417214 15.924584 1982-1999 –> 2000-2009 8.504945 3.7059497 42497.127 590.38387 6 9
global (26.75,27] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 3887 4.7563985 23168.309 40.419534 17.649305 1982-1999 –> 2000-2009 8.652687 3.8962888 42785.115 70.72231 4 9
global (26.75,27] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 3887 4.8222774 23275.245 41.218759 16.982279 1982-1999 –> 2000-2009 8.775908 3.9536311 42995.109 77.12232 9 16
global (26.75,27] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 3887 4.6769236 23037.316 38.709292 18.493197 1982-1999 –> 2000-2009 8.785662 4.1087380 43028.628 73.91599 2 13
global (26.75,27] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 3887 4.8187996 23269.636 44.240978 17.068502 1982-1999 –> 2000-2009 8.789079 3.9702792 43019.141 82.06016 8 16
global (26.75,27] 4 cstar_tref ~ temp + aou + silicate + phosphate 3887 4.8828042 23370.213 40.003437 16.872054 1982-1999 –> 2000-2009 8.824670 3.9418658 43067.054 69.46916 12 18
global (26.75,27] 5 cstar_tref ~ sal + aou + nitrate + phosphate + phosphate_star 3887 4.8547327 23327.391 755.478917 19.667586 1982-1999 –> 2000-2009 8.847716 3.9929829 43117.118 1730.56689 11 20
global (26.75,27] 5 cstar_tref ~ aou + nitrate + silicate + phosphate + phosphate_star 4159 4.2472849 23841.157 458.207655 17.659515 2000-2009 –> 2010-2019 8.901536 4.6542513 46840.695 762.07793 1 2
global (26.75,27] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 4159 4.4015324 24143.614 52.523304 17.568093 2000-2009 –> 2010-2019 9.129855 4.7283231 47265.900 93.16431 5 8
global (26.75,27] 5 cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 4159 4.3592774 24057.592 836.235259 16.644996 2000-2009 –> 2010-2019 9.131813 4.7725361 47252.233 1584.93196 2 7
global (26.75,27] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 4159 4.3888128 24119.541 50.070026 18.662292 2000-2009 –> 2010-2019 9.145211 4.7563985 47287.851 90.48956 4 8
global (26.75,27] 4 cstar_tref ~ aou + silicate + phosphate + phosphate_star 4159 4.3869222 24108.162 430.775031 17.003492 2000-2009 –> 2010-2019 9.185918 4.7989954 47343.784 732.19225 3 9
global (26.75,27] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 4159 4.5953532 24496.172 43.042859 20.398894 2000-2009 –> 2010-2019 9.272277 4.6769236 47533.488 81.75215 9 11
global (26.75,27] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 4159 4.5445740 24403.768 44.730936 18.137867 2000-2009 –> 2010-2019 9.366851 4.8222774 47679.013 85.94970 7 16
global (26.75,27] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 4159 4.5516985 24416.795 46.940543 18.244910 2000-2009 –> 2010-2019 9.370498 4.8187996 47686.431 91.18152 8 16
global (26.75,27] 4 cstar_tref ~ temp + aou + silicate + phosphate 4159 4.5161203 24355.391 49.482131 17.513086 2000-2009 –> 2010-2019 9.398924 4.8828042 47725.604 89.48557 6 18
global (26.75,27] 4 cstar_tref ~ sal + temp + silicate + phosphate_star 4159 4.6094670 24519.674 42.936093 19.635261 2000-2009 –> 2010-2019 9.447674 4.8382071 47818.557 81.57912 10 20
global (27,27.25] 5 cstar_tref ~ aou + nitrate + silicate + phosphate + phosphate_star 5192 4.2095123 29673.667 176.616729 20.358190 1982-1999 –> 2000-2009 7.576702 3.3671898 51568.038 402.05583 1 2
global (27,27.25] 4 cstar_tref ~ aou + nitrate + phosphate + phosphate_star 5192 4.2107695 29674.768 148.613154 20.125681 1982-1999 –> 2000-2009 7.756627 3.5458577 51996.779 324.74365 2 5
global (27,27.25] 4 cstar_tref ~ aou + silicate + phosphate + phosphate_star 5192 4.4306976 30203.434 167.714534 20.805916 1982-1999 –> 2000-2009 7.839605 3.4089072 52198.129 393.14987 6 8
global (27,27.25] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 5192 4.3607858 30040.279 28.165835 21.036659 1982-1999 –> 2000-2009 7.991144 3.6303585 52560.002 52.48762 4 9
global (27,27.25] 3 cstar_tref ~ aou + phosphate + phosphate_star 5192 4.4412930 30226.236 146.272863 21.558904 1982-1999 –> 2000-2009 8.039034 3.5977416 52666.961 322.22873 7 11
global (27,27.25] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 5192 4.3548832 30026.214 37.296366 20.679766 1982-1999 –> 2000-2009 8.125447 3.7705642 52860.830 77.58340 3 11
global (27,27.25] 4 cstar_tref ~ temp + aou + nitrate + phosphate 5192 4.3613906 30039.719 27.681159 20.876183 1982-1999 –> 2000-2009 8.153431 3.7920401 52919.532 50.36166 5 14
global (27,27.25] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 5192 4.5233833 30420.416 37.031684 21.181133 1982-1999 –> 2000-2009 8.178504 3.6551204 52996.628 76.05834 11 17
global (27,27.25] 4 cstar_tref ~ temp + aou + silicate + phosphate 5192 4.5402883 30457.151 22.915322 21.447192 1982-1999 –> 2000-2009 8.199438 3.6591497 53040.518 44.60948 16 23
global (27,27.25] 4 cstar_tref ~ sal + temp + aou + phosphate 5192 4.5310268 30435.948 36.494368 21.789313 1982-1999 –> 2000-2009 8.350425 3.8193981 53375.499 75.12085 15 25
global (27,27.25] 5 cstar_tref ~ aou + nitrate + silicate + phosphate + phosphate_star 5653 4.1796269 32226.609 158.205301 20.645896 2000-2009 –> 2010-2019 8.389139 4.2095123 61900.276 334.82203 1 2
global (27,27.25] 4 cstar_tref ~ aou + nitrate + phosphate + phosphate_star 5653 4.3349231 32637.073 136.179583 22.137950 2000-2009 –> 2010-2019 8.545693 4.2107695 62311.841 284.79274 3 5
global (27,27.25] 4 cstar_tref ~ aou + silicate + phosphate + phosphate_star 5653 4.2702225 32467.054 150.773934 21.452082 2000-2009 –> 2010-2019 8.700920 4.4306976 62670.488 318.48847 2 8
global (27,27.25] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 5653 4.3745102 32741.852 23.702655 21.411038 2000-2009 –> 2010-2019 8.735296 4.3607858 62782.131 51.86849 4 8
global (27,27.25] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 5653 4.5262431 33127.361 34.588440 23.025530 2000-2009 –> 2010-2019 8.881126 4.3548832 63153.575 71.88481 8 11
global (27,27.25] 4 cstar_tref ~ temp + aou + nitrate + phosphate 5653 4.5288476 33131.865 22.355932 22.857392 2000-2009 –> 2010-2019 8.890238 4.3613906 63171.584 50.03709 9 14
global (27,27.25] 3 cstar_tref ~ aou + phosphate + phosphate_star 5653 4.5128349 33089.819 134.080733 23.827979 2000-2009 –> 2010-2019 8.954128 4.4412930 63316.055 280.35360 7 14
global (27,27.25] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 5653 4.4623251 32966.563 34.049852 22.178338 2000-2009 –> 2010-2019 8.985708 4.5233833 63386.980 71.08154 5 16
global (27,27.25] 4 cstar_tref ~ temp + aou + silicate + phosphate 5653 4.4625294 32965.081 21.375310 22.233527 2000-2009 –> 2010-2019 9.002818 4.5402883 63422.233 44.29063 6 22
global (27,27.25] 5 cstar_tref ~ temp + nitrate + silicate + phosphate + phosphate_star 5653 4.5419183 33166.448 17.729496 21.978087 2000-2009 –> 2010-2019 9.060612 4.5186941 63576.094 43.94645 10 19
global (27.25,27.5] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 7224 3.7005773 39419.870 34.883546 15.224552 1982-1999 –> 2000-2009 6.425920 2.7253424 68434.145 60.22870 4 8
global (27.25,27.5] 5 cstar_tref ~ aou + nitrate + silicate + phosphate + phosphate_star 7224 3.7790162 39722.916 254.586599 15.984471 1982-1999 –> 2000-2009 6.467986 2.6889698 68576.282 498.12250 6 7
global (27.25,27.5] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 7224 3.6258350 39125.070 21.396768 14.520158 1982-1999 –> 2000-2009 6.479293 2.8534579 68689.492 41.35453 1 13
global (27.25,27.5] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 7224 3.6997308 39416.565 29.131325 16.147485 1982-1999 –> 2000-2009 6.511642 2.8119113 68805.334 43.49632 3 11
global (27.25,27.5] 5 cstar_tref ~ temp + nitrate + silicate + phosphate + phosphate_star 7224 3.6985412 39411.919 46.860952 16.107898 1982-1999 –> 2000-2009 6.517447 2.8189062 68830.442 73.27816 2 12
global (27.25,27.5] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 7224 3.9055310 40198.689 35.012997 14.473288 1982-1999 –> 2000-2009 6.616135 2.7106043 69148.024 60.35313 8 11
global (27.25,27.5] 4 cstar_tref ~ temp + nitrate + silicate + phosphate_star 7224 3.7451117 39590.706 18.900739 14.631149 1982-1999 –> 2000-2009 6.642967 2.8978550 69338.029 26.02085 5 20
global (27.25,27.5] 4 cstar_tref ~ temp + aou + nitrate + phosphate 7224 3.9769161 40458.384 33.698868 15.223976 1982-1999 –> 2000-2009 6.705278 2.7283623 69483.922 57.05885 15 20
global (27.25,27.5] 4 cstar_tref ~ aou + nitrate + phosphate + phosphate_star 7224 4.0208651 40617.174 200.840834 18.749079 1982-1999 –> 2000-2009 6.710582 2.6897169 69471.867 402.76234 18 20
global (27.25,27.5] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 7224 3.9314549 40294.274 24.872107 14.160506 1982-1999 –> 2000-2009 6.724069 2.7926146 69600.574 45.30826 10 16
global (27.25,27.5] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 7659 3.7275264 41903.880 20.520996 14.687788 2000-2009 –> 2010-2019 7.353361 3.6258350 81028.950 41.91776 1 2
global (27.25,27.5] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 7659 3.7782258 42110.821 27.639932 15.290426 2000-2009 –> 2010-2019 7.478803 3.7005773 81530.692 62.52348 7 11
global (27.25,27.5] 5 cstar_tref ~ temp + nitrate + silicate + phosphate + phosphate_star 7659 3.8244373 42297.039 31.034056 15.542884 2000-2009 –> 2010-2019 7.522978 3.6985412 81708.958 77.89501 10 12
global (27.25,27.5] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 7659 3.8256942 42302.073 18.346971 15.576000 2000-2009 –> 2010-2019 7.525425 3.6997308 81718.638 47.47830 11 14
global (27.25,27.5] 5 cstar_tref ~ aou + nitrate + silicate + phosphate + phosphate_star 7659 3.7657721 42060.247 204.377395 14.805870 2000-2009 –> 2010-2019 7.544788 3.7790162 81783.163 458.96399 3 9
global (27.25,27.5] 4 cstar_tref ~ temp + nitrate + silicate + phosphate_star 7659 3.8314468 42323.089 12.007560 15.835911 2000-2009 –> 2010-2019 7.576559 3.7451117 81913.795 30.90830 12 17
global (27.25,27.5] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 7659 3.7316515 41920.823 26.778324 14.906235 2000-2009 –> 2010-2019 7.637183 3.9055310 82119.512 61.79132 2 10
global (27.25,27.5] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 7659 3.7674465 42067.057 20.234038 15.125154 2000-2009 –> 2010-2019 7.698901 3.9314549 82361.331 45.10614 4 14
global (27.25,27.5] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 7659 3.7676589 42067.920 20.468377 15.115740 2000-2009 –> 2010-2019 7.700785 3.9331262 82368.335 48.84525 5 17
global (27.25,27.5] 4 cstar_tref ~ sal + temp + nitrate + phosphate_star 7659 3.7679796 42067.224 20.087302 15.079573 2000-2009 –> 2010-2019 7.723393 3.9554131 82447.277 39.63154 6 20
global (27.5,27.75] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 9841 3.2296064 51015.943 37.237859 14.666525 1982-1999 –> 2000-2009 5.728144 2.4985376 88846.509 50.33280 1 6
global (27.5,27.75] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 9841 3.5307433 52770.557 77.540526 15.562356 1982-1999 –> 2000-2009 5.881856 2.3511126 89616.010 140.56206 4 5
global (27.5,27.75] 5 cstar_tref ~ aou + nitrate + silicate + phosphate + phosphate_star 9841 3.5661831 52967.130 413.134909 13.683867 1982-1999 –> 2000-2009 5.979756 2.4135727 90237.285 723.42555 6 8
global (27.5,27.75] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 9841 3.6109477 53212.651 67.138200 17.244049 1982-1999 –> 2000-2009 6.033779 2.4228308 90544.821 119.40673 8 11
global (27.5,27.75] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 9841 3.5084865 52646.094 42.426069 12.940302 1982-1999 –> 2000-2009 6.042547 2.5340605 90705.333 57.16424 3 11
global (27.5,27.75] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 9841 3.6249440 53288.792 69.836138 17.557082 1982-1999 –> 2000-2009 6.066558 2.4416143 90746.056 123.69353 9 13
global (27.5,27.75] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 9841 3.6415892 53378.962 78.238680 14.488651 1982-1999 –> 2000-2009 6.151825 2.5102360 91285.191 148.76116 10 17
global (27.5,27.75] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 9841 3.5632703 52951.047 53.094880 13.715644 1982-1999 –> 2000-2009 6.162888 2.5996179 91424.007 72.25419 5 14
global (27.5,27.75] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 9841 3.5731476 53005.530 42.922594 14.040066 1982-1999 –> 2000-2009 6.173809 2.6006615 91484.991 57.71452 7 17
global (27.5,27.75] 5 cstar_tref ~ sal + aou + nitrate + silicate + phosphate 9841 3.4322163 52213.513 81.002795 13.206826 1982-1999 –> 2000-2009 6.283382 2.8511660 92182.578 153.61584 2 24
global (27.5,27.75] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 10161 3.4122379 53792.249 22.052941 13.501108 2000-2009 –> 2010-2019 6.641844 3.2296064 104808.192 59.29080 1 2
global (27.5,27.75] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 10161 3.4815915 54201.151 47.083861 16.939461 2000-2009 –> 2010-2019 7.012335 3.5307433 106971.708 124.62439 2 6
global (27.5,27.75] 5 cstar_tref ~ sal + aou + nitrate + silicate + phosphate 10161 3.6684790 55263.740 53.202098 15.722563 2000-2009 –> 2010-2019 7.100695 3.4322163 107477.252 134.20489 6 8
global (27.5,27.75] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 10161 3.5370573 54522.352 36.644938 15.168128 2000-2009 –> 2010-2019 7.148005 3.6109477 107735.003 103.78314 3 11
global (27.5,27.75] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 10161 3.5446600 54565.986 40.272954 15.140880 2000-2009 –> 2010-2019 7.169604 3.6249440 107854.779 110.10909 4 13
global (27.5,27.75] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 10161 3.6629734 55233.218 32.441026 14.860460 2000-2009 –> 2010-2019 7.171460 3.5084865 107879.312 74.86710 5 8
global (27.5,27.75] 5 cstar_tref ~ aou + nitrate + silicate + phosphate + phosphate_star 10161 3.6703860 55274.301 378.954620 19.093154 2000-2009 –> 2010-2019 7.236569 3.5661831 108241.431 792.08953 7 13
global (27.5,27.75] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 10161 3.7102225 55493.678 40.461704 14.615233 2000-2009 –> 2010-2019 7.273493 3.5632703 108444.725 93.55658 8 13
global (27.5,27.75] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 10161 3.7155286 55522.720 32.628245 14.642909 2000-2009 –> 2010-2019 7.288676 3.5731476 108528.249 75.55084 10 17
global (27.5,27.75] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 10161 3.7573746 55750.316 52.516943 17.065952 2000-2009 –> 2010-2019 7.398964 3.6415892 109129.278 130.75562 13 23
global (27.75,27.85] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 3767 2.6634626 18084.793 322.866957 13.392313 1982-1999 –> 2000-2009 4.682784 2.0193210 30875.669 552.31730 1 2
global (27.75,27.85] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 3767 2.7155580 18230.730 318.758084 13.388600 1982-1999 –> 2000-2009 4.785427 2.0698694 31170.495 562.53037 2 4
global (27.75,27.85] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 3767 2.7205001 18244.429 289.075008 13.400582 1982-1999 –> 2000-2009 4.799815 2.0793152 31211.613 496.99362 3 6
global (27.75,27.85] 4 cstar_tref ~ temp + aou + nitrate + phosphate 3767 2.7434900 18305.828 318.253289 14.002569 1982-1999 –> 2000-2009 4.847827 2.1043374 31343.048 544.61396 4 8
global (27.75,27.85] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 3767 2.7442305 18309.861 288.803085 13.457436 1982-1999 –> 2000-2009 4.851983 2.1077524 31358.846 498.40940 5 10
global (27.75,27.85] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 3767 2.7682639 18375.555 300.972435 13.376472 1982-1999 –> 2000-2009 4.894319 2.1260551 31476.606 509.83333 6 12
global (27.75,27.85] 5 cstar_tref ~ temp + nitrate + silicate + phosphate + phosphate_star 3767 2.7886217 18430.757 353.542492 13.392409 1982-1999 –> 2000-2009 4.938165 2.1495437 31597.974 657.01934 7 14
global (27.75,27.85] 4 cstar_tref ~ temp + aou + nitrate + phosphate_star 3767 2.7968996 18451.089 285.745281 14.002137 1982-1999 –> 2000-2009 4.955603 2.1587038 31641.913 491.20876 8 16
global (27.75,27.85] 4 cstar_tref ~ temp + nitrate + phosphate + phosphate_star 3767 2.8209916 18515.707 285.423792 14.064093 1982-1999 –> 2000-2009 5.007394 2.1864029 31783.311 490.99534 9 18
global (27.75,27.85] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 3767 2.8602894 18621.935 100.064514 14.187868 1982-1999 –> 2000-2009 5.144430 2.2841405 32154.894 141.41817 10 20
global (27.75,27.85] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 3897 2.7039575 18826.027 310.780216 13.902526 2000-2009 –> 2010-2019 5.367420 2.6634626 36910.820 633.64717 1 2
global (27.75,27.85] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 3897 2.7667926 19005.073 277.413916 13.872920 2000-2009 –> 2010-2019 5.487293 2.7205001 37249.502 566.48892 2 5
global (27.75,27.85] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 3897 2.7894914 19068.754 319.366756 15.004251 2000-2009 –> 2010-2019 5.505049 2.7155580 37299.484 638.12484 3 5
global (27.75,27.85] 4 cstar_tref ~ temp + aou + nitrate + phosphate 3897 2.7907492 19070.268 306.292560 14.916123 2000-2009 –> 2010-2019 5.534239 2.7434900 37376.096 624.54585 4 8
global (27.75,27.85] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 3897 2.7929742 19078.479 278.587749 13.908864 2000-2009 –> 2010-2019 5.537205 2.7442305 37388.340 567.39083 5 10
global (27.75,27.85] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 3897 2.8488571 19232.885 274.134185 14.974743 2000-2009 –> 2010-2019 5.617121 2.7682639 37608.440 575.10662 6 12
global (27.75,27.85] 4 cstar_tref ~ temp + aou + nitrate + phosphate_star 3897 2.8503694 19235.021 273.597642 14.877373 2000-2009 –> 2010-2019 5.647269 2.7968996 37686.110 559.34292 7 15
global (27.75,27.85] 5 cstar_tref ~ temp + nitrate + silicate + phosphate + phosphate_star 3897 2.8771255 19309.841 379.427059 14.987516 2000-2009 –> 2010-2019 5.665747 2.7886217 37740.599 732.96955 8 15
global (27.75,27.85] 4 cstar_tref ~ temp + nitrate + phosphate + phosphate_star 3897 2.8777129 19309.432 273.224136 14.926172 2000-2009 –> 2010-2019 5.698704 2.8209916 37825.140 558.64793 9 18
global (27.75,27.85] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 3897 2.9142466 19409.758 65.879248 14.860315 2000-2009 –> 2010-2019 5.774536 2.8602894 38031.693 165.94376 10 20
global (27.85,27.95] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 4266 2.6981044 20588.816 879.168561 25.260245 1982-1999 –> 2000-2009 4.544368 1.8462639 34307.316 1369.86429 1 2
global (27.85,27.95] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 4266 2.7188618 20654.204 886.852231 25.332787 1982-1999 –> 2000-2009 4.586824 1.8679622 34451.501 1376.54637 2 4
global (27.85,27.95] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 4266 2.7356154 20706.616 120.109071 24.933227 1982-1999 –> 2000-2009 4.612132 1.8765169 34534.729 182.36740 3 6
global (27.85,27.95] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 4266 2.7522947 20758.479 988.939207 26.659877 1982-1999 –> 2000-2009 4.657636 1.9053417 34689.397 1559.93985 4 9
global (27.85,27.95] 4 cstar_tref ~ sal + temp + aou + phosphate 4266 2.7609454 20783.254 33.877663 25.430991 1982-1999 –> 2000-2009 4.662064 1.9011182 34697.206 58.36487 5 9
global (27.85,27.95] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 4266 2.7772857 20835.601 914.077996 26.679956 1982-1999 –> 2000-2009 4.727431 1.9501449 34923.264 1417.94777 6 15
global (27.85,27.95] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 4266 2.7900926 20874.854 119.649620 25.153599 1982-1999 –> 2000-2009 4.727899 1.9378060 34919.712 181.83653 7 14
global (27.85,27.95] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 4266 2.7946056 20888.643 208.503720 25.136838 1982-1999 –> 2000-2009 4.739524 1.9449181 34958.208 332.24269 9 17
global (27.85,27.95] 5 cstar_tref ~ temp + nitrate + silicate + phosphate + phosphate_star 4266 2.7928981 20883.429 931.425879 26.705686 1982-1999 –> 2000-2009 4.760954 1.9680561 35032.750 1634.75892 8 20
global (27.85,27.95] 4 cstar_tref ~ temp + aou + nitrate + phosphate 4266 2.8282494 20988.745 984.519712 27.840456 1982-1999 –> 2000-2009 4.763284 1.9350346 35021.951 1522.95762 14 20
global (27.85,27.95] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 4441 2.4233924 20479.077 806.773749 13.729044 2000-2009 –> 2010-2019 5.121497 2.6981044 41067.893 1685.94231 1 2
global (27.85,27.95] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 4441 2.4479248 20568.539 810.076199 13.769268 2000-2009 –> 2010-2019 5.166786 2.7188618 41222.743 1696.92843 2 4
global (27.85,27.95] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 4441 2.4938845 20733.753 93.066713 14.812599 2000-2009 –> 2010-2019 5.229500 2.7356154 41440.369 213.17578 3 6
global (27.85,27.95] 4 cstar_tref ~ sal + temp + aou + phosphate 4441 2.4971448 20743.356 33.878843 14.621445 2000-2009 –> 2010-2019 5.258090 2.7609454 41526.610 67.75651 4 9
global (27.85,27.95] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 4441 2.5475069 20922.705 913.290566 15.370408 2000-2009 –> 2010-2019 5.299802 2.7522947 41681.184 1902.22977 5 9
global (27.85,27.95] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 4441 2.5476239 20923.113 92.741668 14.877899 2000-2009 –> 2010-2019 5.337716 2.7900926 41797.967 212.39129 6 13
global (27.85,27.95] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 4441 2.5553436 20949.986 159.417109 14.837729 2000-2009 –> 2010-2019 5.349949 2.7946056 41838.629 367.92083 9 18
global (27.85,27.95] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 4441 2.5751904 21018.704 806.334576 15.335925 2000-2009 –> 2010-2019 5.352476 2.7772857 41854.305 1720.41257 11 17
global (27.85,27.95] 4 cstar_tref ~ sal + temp + aou + phosphate_star 4441 2.5544189 20944.771 13.869573 14.598365 2000-2009 –> 2010-2019 5.362706 2.8082873 41873.083 28.04071 8 18
global (27.85,27.95] 4 cstar_tref ~ temp + aou + nitrate + phosphate 4441 2.5477720 20921.629 895.056865 15.309248 2000-2009 –> 2010-2019 5.376021 2.8282494 41910.374 1879.57658 7 21
global (27.95,28.05] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 4687 2.6238008 22357.515 136.430560 20.096299 1982-1999 –> 2000-2009 5.138327 2.5145266 41202.704 232.95525 1 2
global (27.95,28.05] 4 cstar_tref ~ sal + temp + aou + phosphate 4687 2.6315298 22383.088 50.960087 20.544428 1982-1999 –> 2000-2009 5.148544 2.5170142 41234.231 84.98456 2 4
global (27.95,28.05] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 4687 2.6433959 22427.262 135.897009 19.523056 1982-1999 –> 2000-2009 5.174421 2.5310251 41325.058 229.97590 4 7
global (27.95,28.05] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 4687 2.6472083 22440.771 275.462635 19.409764 1982-1999 –> 2000-2009 5.182766 2.5355573 41352.959 500.98717 7 11
global (27.95,28.05] 4 cstar_tref ~ sal + temp + aou + phosphate_star 4687 2.6470804 22438.318 13.773278 19.834167 1982-1999 –> 2000-2009 5.183714 2.5366336 41351.920 28.05890 6 11
global (27.95,28.05] 4 cstar_tref ~ sal + temp + phosphate + phosphate_star 4687 2.6509803 22452.119 22.880043 19.707965 1982-1999 –> 2000-2009 5.192197 2.5412169 41380.241 43.71650 8 14
global (27.95,28.05] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 4687 2.6398777 22414.777 248.349496 19.938411 1982-1999 –> 2000-2009 5.204168 2.5642906 41417.608 392.67251 3 10
global (27.95,28.05] 4 cstar_tref ~ sal + temp + nitrate + phosphate_star 4687 2.6462431 22435.353 23.327319 20.439521 1982-1999 –> 2000-2009 5.215681 2.5694378 41452.314 44.40520 5 13
global (27.95,28.05] 4 cstar_tref ~ temp + aou + silicate + phosphate 4687 2.7681287 22857.470 133.480322 21.279453 1982-1999 –> 2000-2009 5.376629 2.6085008 41995.802 229.27728 10 19
global (27.95,28.05] 3 cstar_tref ~ temp + aou + phosphate 4687 2.7690807 22858.693 38.919275 21.428191 1982-1999 –> 2000-2009 5.380059 2.6109778 42002.661 70.63676 11 21
global (27.95,28.05] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 6116 2.6624365 29348.540 141.847484 18.295755 2000-2009 –> 2010-2019 5.286237 2.6238008 51706.055 278.27804 1 2
global (27.95,28.05] 4 cstar_tref ~ sal + temp + aou + phosphate 6116 2.6742777 29400.822 68.675373 18.237005 2000-2009 –> 2010-2019 5.305808 2.6315298 51783.909 119.63546 2 4
global (27.95,28.05] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 6116 2.6915489 29481.565 112.990789 18.060804 2000-2009 –> 2010-2019 5.334945 2.6433959 51908.827 248.88780 3 7
global (27.95,28.05] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 6116 2.6953316 29498.744 213.894091 18.043570 2000-2009 –> 2010-2019 5.342540 2.6472083 51939.515 489.35673 4 11
global (27.95,28.05] 4 cstar_tref ~ sal + temp + aou + phosphate_star 6116 2.6977019 29507.496 20.003812 18.016076 2000-2009 –> 2010-2019 5.344782 2.6470804 51945.814 33.77709 5 11
global (27.95,28.05] 4 cstar_tref ~ sal + temp + phosphate + phosphate_star 6116 2.7018939 29526.489 28.362925 17.987331 2000-2009 –> 2010-2019 5.352874 2.6509803 51978.608 51.24297 6 14
global (27.95,28.05] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 6116 2.7153713 29589.352 179.861261 17.841686 2000-2009 –> 2010-2019 5.355249 2.6398777 52004.129 428.21076 7 10
global (27.95,28.05] 4 cstar_tref ~ sal + temp + nitrate + phosphate_star 6116 2.7395462 29695.771 28.679936 17.560133 2000-2009 –> 2010-2019 5.385789 2.6462431 52131.124 52.00726 8 13
global (27.95,28.05] 4 cstar_tref ~ sal + temp + silicate + phosphate_star 6116 2.7633645 29801.660 20.094397 18.519306 2000-2009 –> 2010-2019 5.525799 2.7624341 52639.825 33.98028 9 18
global (27.95,28.05] 4 cstar_tref ~ temp + aou + silicate + phosphate 6116 2.8490503 30175.185 134.690853 20.906685 2000-2009 –> 2010-2019 5.617179 2.7681287 53032.655 268.17117 10 20
global (28.05,28.1] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 2686 1.5651833 10043.209 332.466744 10.973365 1982-1999 –> 2000-2009 2.682494 1.1173103 17379.133 651.19000 1 2
global (28.05,28.1] 4 cstar_tref ~ temp + aou + silicate + phosphate 2686 1.5658928 10043.644 263.905323 11.409333 1982-1999 –> 2000-2009 2.683861 1.1179680 17380.384 516.31358 2 4
global (28.05,28.1] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 2686 1.6055476 10179.991 312.723727 10.754000 1982-1999 –> 2000-2009 2.733392 1.1278439 17560.824 550.83686 3 6
global (28.05,28.1] 4 cstar_tref ~ temp + nitrate + silicate + phosphate_star 2686 1.6078093 10185.553 312.560123 11.487770 1982-1999 –> 2000-2009 2.740827 1.1330175 17586.290 550.38232 4 8
global (28.05,28.1] 4 cstar_tref ~ sal + temp + aou + phosphate 2686 1.6266743 10248.218 195.855507 13.454784 1982-1999 –> 2000-2009 2.759880 1.1332054 17649.748 318.47334 9 14
global (28.05,28.1] 3 cstar_tref ~ temp + aou + phosphate 2686 1.6273857 10248.567 88.710185 13.900420 1982-1999 –> 2000-2009 2.763103 1.1357167 17658.692 160.65150 10 16
global (28.05,28.1] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 2686 1.6128638 10204.415 199.130126 11.336986 1982-1999 –> 2000-2009 2.765340 1.1524765 17688.651 382.82770 5 12
global (28.05,28.1] 4 cstar_tref ~ temp + aou + silicate + phosphate_star 2686 1.6137415 10205.337 199.129892 11.833130 1982-1999 –> 2000-2009 2.766620 1.1528783 17689.242 370.34960 6 14
global (28.05,28.1] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 2686 1.6174355 10219.620 469.270524 11.213485 1982-1999 –> 2000-2009 2.776478 1.1590430 17731.048 972.12832 7 16
global (28.05,28.1] 4 cstar_tref ~ temp + silicate + phosphate + phosphate_star 2686 1.6183836 10220.768 463.035920 11.731140 1982-1999 –> 2000-2009 2.777726 1.1593428 17731.434 932.04834 8 18
global (28.05,28.1] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 3487 1.8029519 14020.329 448.702643 11.335311 2000-2009 –> 2010-2019 3.368135 1.5651833 24063.538 781.16939 1 2
global (28.05,28.1] 4 cstar_tref ~ temp + aou + silicate + phosphate 3487 1.8164338 14070.285 283.395325 11.318378 2000-2009 –> 2010-2019 3.382327 1.5658928 24113.929 547.30065 2 4
global (28.05,28.1] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 3487 1.8407747 14165.118 301.839715 11.469221 2000-2009 –> 2010-2019 3.446322 1.6055476 24345.109 614.56344 4 7
global (28.05,28.1] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 3487 1.8409399 14165.744 234.352424 11.496868 2000-2009 –> 2010-2019 3.453804 1.6128638 24370.159 433.48255 5 10
global (28.05,28.1] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 3487 1.8404886 14164.034 489.285430 11.401620 2000-2009 –> 2010-2019 3.457924 1.6174355 24383.654 958.55595 3 10
global (28.05,28.1] 4 cstar_tref ~ temp + nitrate + silicate + phosphate_star 3487 1.8531450 14209.828 293.997436 11.589885 2000-2009 –> 2010-2019 3.460954 1.6078093 24395.381 606.55756 9 13
global (28.05,28.1] 4 cstar_tref ~ temp + aou + silicate + phosphate_star 3487 1.8522642 14206.512 213.607980 11.475366 2000-2009 –> 2010-2019 3.466006 1.6137415 24411.850 412.73787 8 14
global (28.05,28.1] 4 cstar_tref ~ temp + silicate + phosphate + phosphate_star 3487 1.8511246 14202.220 446.454960 11.379150 2000-2009 –> 2010-2019 3.469508 1.6183836 24422.988 909.49088 7 15
global (28.05,28.1] 4 cstar_tref ~ sal + temp + silicate + phosphate_star 3487 1.8411339 14164.479 44.762728 11.609322 2000-2009 –> 2010-2019 3.491882 1.6507481 24491.617 85.65952 6 17
global (28.05,28.1] 3 cstar_tref ~ temp + silicate + phosphate_star 3487 1.8545339 14213.053 18.605580 11.876617 2000-2009 –> 2010-2019 3.507959 1.6534251 24546.895 37.27006 10 22
global (28.1,28.15] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 1240 0.5941116 2241.661 89.924673 4.402543 1982-1999 –> 2000-2009 1.025771 0.4316591 3619.360 157.99188 1 2
global (28.1,28.15] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 1240 0.6191972 2344.226 238.044401 4.533885 1982-1999 –> 2000-2009 1.076060 0.4568630 3855.621 435.20674 3 6
global (28.1,28.15] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 1240 0.6197556 2346.461 202.779660 4.741639 1982-1999 –> 2000-2009 1.081765 0.4620091 3884.246 370.94755 4 9
global (28.1,28.15] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 1240 0.6224469 2357.207 53.051445 4.669364 1982-1999 –> 2000-2009 1.083042 0.4605950 3887.770 90.74982 5 9
global (28.1,28.15] 4 cstar_tref ~ temp + aou + silicate + phosphate 1240 0.6309085 2388.693 88.280028 4.344082 1982-1999 –> 2000-2009 1.086507 0.4555989 3891.561 156.29242 6 8
global (28.1,28.15] 4 cstar_tref ~ sal + temp + aou + phosphate 1240 0.6189065 2341.061 77.073960 5.233512 1982-1999 –> 2000-2009 1.117520 0.4986136 4056.484 137.44206 2 12
global (28.1,28.15] 4 cstar_tref ~ sal + temp + nitrate + phosphate_star 1240 0.6334027 2398.478 200.421466 5.047285 1982-1999 –> 2000-2009 1.129595 0.4961927 4102.434 368.37275 7 15
global (28.1,28.15] 3 cstar_tref ~ temp + aou + phosphate 1240 0.6392646 2419.324 77.073158 4.885442 1982-1999 –> 2000-2009 1.149686 0.5104212 4187.889 137.40557 8 19
global (28.1,28.15] 4 cstar_tref ~ sal + temp + phosphate + phosphate_star 1240 0.6408835 2427.597 209.091396 5.357120 1982-1999 –> 2000-2009 1.152820 0.5119362 4205.144 384.82869 9 21
global (28.1,28.15] 4 cstar_tref ~ temp + silicate + phosphate + phosphate_star 1240 0.6645827 2517.650 210.664047 4.427561 1982-1999 –> 2000-2009 1.153754 0.4891708 4188.026 381.28142 14 20
global (28.1,28.15] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 1472 0.6280792 2822.133 156.892872 2.891879 2000-2009 –> 2010-2019 1.222191 0.5941116 5063.794 246.81754 1 2
global (28.1,28.15] 4 cstar_tref ~ sal + temp + aou + phosphate 1472 0.6286543 2822.827 153.746865 2.905736 2000-2009 –> 2010-2019 1.247561 0.6189065 5163.889 230.82083 2 4
global (28.1,28.15] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 1472 0.6494040 2920.429 332.384384 2.967745 2000-2009 –> 2010-2019 1.269160 0.6197556 5266.890 535.16404 3 7
global (28.1,28.15] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 1472 0.6513683 2929.321 426.062952 2.988081 2000-2009 –> 2010-2019 1.270566 0.6191972 5273.546 664.10735 4 7
global (28.1,28.15] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 1472 0.6539021 2940.751 101.780855 3.008111 2000-2009 –> 2010-2019 1.276349 0.6224469 5297.958 154.83230 7 12
global (28.1,28.15] 4 cstar_tref ~ sal + temp + nitrate + phosphate_star 1472 0.6516809 2928.734 326.506503 2.946482 2000-2009 –> 2010-2019 1.285084 0.6334027 5327.212 526.92797 6 13
global (28.1,28.15] 4 cstar_tref ~ sal + temp + phosphate + phosphate_star 1472 0.6513742 2927.348 357.223004 2.989652 2000-2009 –> 2010-2019 1.292258 0.6408835 5354.944 566.31440 5 14
global (28.1,28.15] 4 cstar_tref ~ sal + temp + aou + phosphate_star 1472 0.6539365 2938.906 100.089610 3.004675 2000-2009 –> 2010-2019 1.296106 0.6421691 5371.472 145.71716 8 18
global (28.1,28.15] 5 cstar_tref ~ sal + temp + aou + nitrate + silicate 1472 0.6673759 3000.796 150.888828 3.166801 2000-2009 –> 2010-2019 1.322142 0.6547662 5483.540 232.26037 9 20
global (28.1,28.15] 4 cstar_tref ~ sal + temp + silicate + phosphate_star 1472 0.6772846 3042.185 75.557129 3.171250 2000-2009 –> 2010-2019 1.332131 0.6548460 5523.232 124.32362 10 22
global (28.1,28.2] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 4783 1.0473275 14029.914 39.791727 5.014590 1982-1999 –> 2000-2009 2.004210 0.9568824 23725.704 93.51537 1 2
global (28.1,28.2] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 4783 1.0569058 14117.002 367.905580 5.093581 1982-1999 –> 2000-2009 2.017614 0.9607085 23840.894 748.47449 2 4
global (28.1,28.2] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 4783 1.0617337 14160.599 49.953259 5.106680 1982-1999 –> 2000-2009 2.025120 0.9633862 23904.091 114.14408 3 6
global (28.1,28.2] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 4783 1.0622460 14165.214 57.333802 5.103558 1982-1999 –> 2000-2009 2.026016 0.9637699 23911.510 133.10807 4 8
global (28.1,28.2] 4 cstar_tref ~ sal + temp + silicate + phosphate_star 4783 1.1005762 14502.313 10.111094 5.578937 1982-1999 –> 2000-2009 2.111798 1.0112216 24585.060 22.49946 5 10
global (28.1,28.2] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 4783 1.3065925 16145.730 496.971068 6.737619 1982-1999 –> 2000-2009 2.402608 1.0960151 26797.513 992.30723 6 12
global (28.1,28.2] 5 cstar_tref ~ temp + nitrate + silicate + phosphate + phosphate_star 4783 1.3288338 16307.196 603.026611 6.999287 1982-1999 –> 2000-2009 2.441842 1.1130086 27067.325 962.30925 7 14
global (28.1,28.2] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 4783 1.3350188 16351.618 513.146289 7.083434 1982-1999 –> 2000-2009 2.451435 1.1164162 27133.274 813.99552 8 16
global (28.1,28.2] 4 cstar_tref ~ temp + nitrate + silicate + phosphate_star 4783 1.3504855 16459.806 34.756693 6.962919 1982-1999 –> 2000-2009 2.494441 1.1439555 27411.063 68.71816 12 21
global (28.1,28.2] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 4783 1.3488441 16450.173 438.964480 6.188395 1982-1999 –> 2000-2009 2.540588 1.1917437 27691.628 803.63290 10 21
global (28.1,28.2] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 5415 1.0374889 15779.684 72.565404 7.255217 2000-2009 –> 2010-2019 2.084816 1.0473275 29809.598 112.35713 4 5
global (28.1,28.2] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 5415 1.0314224 15716.171 637.889852 7.176264 2000-2009 –> 2010-2019 2.088328 1.0569058 29833.173 1005.79543 1 3
global (28.1,28.2] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 5415 1.0336275 15739.300 82.053487 7.305339 2000-2009 –> 2010-2019 2.095361 1.0617337 29899.900 132.00675 2 5
global (28.1,28.2] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 5415 1.0337976 15741.083 90.797199 7.310744 2000-2009 –> 2010-2019 2.096044 1.0622460 29906.297 148.13100 3 7
global (28.1,28.2] 4 cstar_tref ~ sal + temp + silicate + phosphate_star 5415 1.0439425 15844.842 18.330115 6.927384 2000-2009 –> 2010-2019 2.144519 1.1005762 30347.155 28.44121 5 10
global (28.1,28.2] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 5415 1.3079581 18288.604 764.860677 8.506076 2000-2009 –> 2010-2019 2.614550 1.3065925 34434.334 1261.83174 13 19
global (28.1,28.2] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 5415 1.2797118 18052.160 750.413600 8.383188 2000-2009 –> 2010-2019 2.627094 1.3473827 34491.962 1230.28987 8 17
global (28.1,28.2] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 5415 1.2787785 18044.259 599.772076 8.502431 2000-2009 –> 2010-2019 2.627623 1.3488441 34494.431 1038.73656 7 17
global (28.1,28.2] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 5415 1.2786370 18043.060 692.123125 8.527716 2000-2009 –> 2010-2019 2.627966 1.3493285 34496.668 1209.35735 6 17
global (28.1,28.2] 4 cstar_tref ~ sal + temp + phosphate + phosphate_star 5415 1.2828891 18077.016 31.971063 8.456855 2000-2009 –> 2010-2019 2.635137 1.3522481 34549.299 49.05283 9 24
global (28.15,28.2] 4 cstar_tref ~ aou + nitrate + phosphate + phosphate_star 2158 0.6535289 4300.248 920.594117 3.600495 1982-1999 –> 2000-2009 1.151033 0.4975040 7280.449 1760.93037 1 2
global (28.15,28.2] 5 cstar_tref ~ sal + temp + aou + nitrate + silicate 2158 0.6787825 4465.885 155.447821 3.888036 1982-1999 –> 2000-2009 1.180689 0.5019060 7484.362 278.74298 2 4
global (28.15,28.2] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 2158 0.6789892 4467.199 950.294894 3.829513 1982-1999 –> 2000-2009 1.186374 0.5073847 7530.383 1618.26037 3 6
global (28.15,28.2] 4 cstar_tref ~ temp + aou + nitrate + silicate 2158 0.6789927 4465.221 110.202784 3.832678 1982-1999 –> 2000-2009 1.196071 0.5170782 7604.337 187.13443 4 8
global (28.15,28.2] 4 cstar_tref ~ temp + aou + nitrate + phosphate 2158 0.6961616 4572.998 335.043792 3.876809 1982-1999 –> 2000-2009 1.218276 0.5221140 7752.025 548.39207 5 10
global (28.15,28.2] 4 cstar_tref ~ temp + aou + nitrate + phosphate_star 2158 0.7059201 4633.078 343.272815 3.897649 1982-1999 –> 2000-2009 1.237374 0.5314540 7885.120 563.81132 6 12
global (28.15,28.2] 4 cstar_tref ~ temp + nitrate + phosphate + phosphate_star 2158 0.7266071 4757.740 529.119540 4.019614 1982-1999 –> 2000-2009 1.273667 0.5470604 8128.968 872.20313 7 15
global (28.15,28.2] 4 cstar_tref ~ sal + temp + aou + nitrate 2158 0.7372036 4820.229 110.100791 4.134249 1982-1999 –> 2000-2009 1.296112 0.5589080 8279.687 202.49010 8 17
global (28.15,28.2] 5 cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 2158 0.7684372 5001.319 753.187263 4.684694 1982-1999 –> 2000-2009 1.310042 0.5416047 8333.273 1153.53185 9 16
global (28.15,28.2] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 2158 0.7812642 5072.769 161.244747 6.278895 1982-1999 –> 2000-2009 1.432602 0.6513375 9164.459 299.44539 10 22
global (28.15,28.2] 5 cstar_tref ~ sal + temp + aou + nitrate + silicate 2865 0.6527941 5700.710 244.661932 3.325047 2000-2009 –> 2010-2019 1.331577 0.6787825 10166.595 400.10975 1 2
global (28.15,28.2] 4 cstar_tref ~ temp + aou + nitrate + silicate 2865 0.6590479 5753.342 157.390313 2.965231 2000-2009 –> 2010-2019 1.338041 0.6789927 10218.564 267.59310 2 4
global (28.15,28.2] 4 cstar_tref ~ temp + aou + nitrate + phosphate 2865 0.6830023 5957.915 312.082038 3.180626 2000-2009 –> 2010-2019 1.379164 0.6961616 10530.913 647.12583 3 6
global (28.15,28.2] 4 cstar_tref ~ temp + aou + nitrate + phosphate_star 2865 0.6908765 6023.597 319.435060 3.206227 2000-2009 –> 2010-2019 1.396796 0.7059201 10656.674 662.70787 5 9
global (28.15,28.2] 4 cstar_tref ~ sal + temp + aou + nitrate 2865 0.6926713 6038.463 194.187971 3.696252 2000-2009 –> 2010-2019 1.429875 0.7372036 10858.692 304.28876 6 12
global (28.15,28.2] 4 cstar_tref ~ temp + nitrate + phosphate + phosphate_star 2865 0.7073940 6158.979 612.590586 3.303374 2000-2009 –> 2010-2019 1.434001 0.7266071 10916.719 1141.71013 7 12
global (28.15,28.2] 5 cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 2865 0.6843511 5971.219 868.687082 3.627171 2000-2009 –> 2010-2019 1.452788 0.7684372 10972.539 1621.87435 4 11
global (28.15,28.2] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 2865 0.7894598 6789.910 245.641457 3.048407 2000-2009 –> 2010-2019 1.574686 0.7852266 11884.513 392.78966 8 17
global (28.15,28.2] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 2865 0.8108210 6942.891 249.843716 3.277420 2000-2009 –> 2010-2019 1.592085 0.7812642 12015.661 411.08846 13 21
global (28.15,28.2] 3 cstar_tref ~ temp + aou + nitrate 2865 0.7944870 6822.282 157.306268 2.917600 2000-2009 –> 2010-2019 1.595605 0.8011179 11999.359 259.76119 9 20
global (28.2, Inf] 5 cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 2645 0.7730614 6158.556 182.210748 4.527328 1982-1999 –> 2000-2009 1.283910 0.5108485 11321.152 390.45331 1 2
global (28.2, Inf] 5 cstar_tref ~ sal + temp + aou + nitrate + silicate 2645 0.7833392 6228.422 50.958618 4.467527 1982-1999 –> 2000-2009 1.303639 0.5202995 11517.322 88.87097 2 4
global (28.2, Inf] 4 cstar_tref ~ sal + aou + nitrate + silicate 2645 0.7849151 6237.054 31.514534 4.436857 1982-1999 –> 2000-2009 1.305768 0.5208532 11531.283 62.56158 3 6
global (28.2, Inf] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 2645 0.8209142 6476.273 633.917968 4.349360 1982-1999 –> 2000-2009 1.349610 0.5286962 11875.478 999.26717 4 8
global (28.2, Inf] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 2645 0.8220026 6483.283 635.662996 4.351643 1982-1999 –> 2000-2009 1.350725 0.5287222 11882.826 993.53273 5 10
global (28.2, Inf] 4 cstar_tref ~ sal + aou + nitrate + phosphate_star 2645 0.8220656 6481.688 89.984366 4.355005 1982-1999 –> 2000-2009 1.351112 0.5290467 11883.459 184.09489 6 13
global (28.2, Inf] 4 cstar_tref ~ sal + temp + aou + nitrate 2645 0.8251271 6501.353 10.697342 4.335309 1982-1999 –> 2000-2009 1.353916 0.5287889 11899.765 30.52290 7 13
global (28.2, Inf] 4 cstar_tref ~ sal + aou + nitrate + phosphate 2645 0.8265027 6510.165 305.688815 4.383857 1982-1999 –> 2000-2009 1.356357 0.5298542 11922.444 512.12207 8 16
global (28.2, Inf] 5 cstar_tref ~ sal + temp + nitrate + phosphate + phosphate_star 2645 0.8271050 6516.018 651.700818 4.379764 1982-1999 –> 2000-2009 1.358265 0.5311605 11947.262 1016.42249 9 18
global (28.2, Inf] 3 cstar_tref ~ sal + aou + nitrate 2645 0.8586742 6710.170 10.085041 4.418741 1982-1999 –> 2000-2009 1.390687 0.5320128 12148.462 29.66382 10 20
global (28.2, Inf] 5 cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 4656 0.6559939 9301.181 713.806088 4.113312 2000-2009 –> 2010-2019 1.429055 0.7730614 15459.737 896.01684 1 2
global (28.2, Inf] 5 cstar_tref ~ sal + temp + aou + nitrate + silicate 4656 0.6991503 9894.488 308.625629 4.043197 2000-2009 –> 2010-2019 1.482489 0.7833392 16122.910 359.58425 3 5
global (28.2, Inf] 4 cstar_tref ~ sal + aou + nitrate + silicate 4656 0.7045759 9964.473 256.947569 3.907592 2000-2009 –> 2010-2019 1.489491 0.7849151 16201.527 288.46210 4 7
global (28.2, Inf] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 4656 0.7340897 10348.592 887.914143 4.227225 2000-2009 –> 2010-2019 1.555004 0.8209142 16824.866 1521.83211 7 11
global (28.2, Inf] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate_star 4656 0.7338307 10345.307 861.260606 4.231681 2000-2009 –> 2010-2019 1.555833 0.8220026 16828.589 1496.92360 6 11
global (28.2, Inf] 4 cstar_tref ~ sal + aou + nitrate + phosphate_star 4656 0.7349020 10356.891 713.369565 4.235827 2000-2009 –> 2010-2019 1.556968 0.8220656 16838.580 803.35393 9 15
global (28.2, Inf] 4 cstar_tref ~ sal + temp + aou + nitrate 4656 0.7349007 10356.874 71.045399 4.182416 2000-2009 –> 2010-2019 1.560028 0.8251271 16858.227 81.74274 8 15
global (28.2, Inf] 4 cstar_tref ~ sal + aou + nitrate + phosphate 4656 0.7386147 10403.816 857.419245 4.161764 2000-2009 –> 2010-2019 1.565117 0.8265027 16913.981 1163.10806 10 18
global (28.2, Inf] 3 cstar_tref ~ sal + aou + nitrate 4656 0.7396939 10415.412 16.169105 4.107700 2000-2009 –> 2010-2019 1.598368 0.8586742 17125.582 26.25415 11 20
global (28.2, Inf] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 4656 0.6957848 9849.555 740.898442 5.450796 2000-2009 –> 2010-2019 1.639206 0.9434209 17061.636 1607.15549 2 15
global (28.2,28.3] 5 cstar_tref ~ sal + aou + nitrate + silicate + phosphate 10984 0.6165560 20561.382 271.022220 2.833518 1982-1999 –> 2000-2009 1.450693 0.8341372 40500.438 386.53922 1 5
global (28.2,28.3] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 10984 0.7396725 24560.841 270.136172 8.856215 1982-1999 –> 2000-2009 1.529610 0.7899378 43623.354 449.27333 8 9
global (28.2,28.3] 5 cstar_tref ~ temp + nitrate + silicate + phosphate + phosphate_star 10984 0.7487731 24829.475 294.134716 8.983373 1982-1999 –> 2000-2009 1.549638 0.8008645 44113.163 423.75346 10 12
global (28.2,28.3] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 10984 0.7288284 24236.390 145.381302 3.940356 1982-1999 –> 2000-2009 1.569414 0.8405855 44299.428 271.85405 3 8
global (28.2,28.3] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate_star 10984 0.7604010 25168.002 244.204078 9.267448 1982-1999 –> 2000-2009 1.569561 0.8091596 44617.591 345.17033 12 15
global (28.2,28.3] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 10984 0.7299566 24270.369 15.030232 3.921903 1982-1999 –> 2000-2009 1.574543 0.8445868 44409.865 34.22176 5 11
global (28.2,28.3] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 10984 0.7299738 24270.887 15.017841 3.920866 1982-1999 –> 2000-2009 1.574692 0.8447179 44412.882 31.53911 6 13
global (28.2,28.3] 5 cstar_tref ~ sal + nitrate + silicate + phosphate + phosphate_star 10984 0.6632391 22164.749 207.613646 3.096512 1982-1999 –> 2000-2009 1.577650 0.9144112 43583.113 302.80835 2 12
global (28.2,28.3] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 10984 0.7294738 24255.836 14.309839 3.991831 1982-1999 –> 2000-2009 1.578427 0.8489529 44478.346 31.59109 4 12
global (28.2,28.3] 4 cstar_tref ~ sal + temp + silicate + phosphate_star 10984 0.7309250 24297.494 10.175565 3.934618 1982-1999 –> 2000-2009 1.581324 0.8503987 44545.399 21.30711 7 16
global (28.2,28.3] 5 cstar_tref ~ sal + aou + nitrate + silicate + phosphate 14697 0.6772848 30268.512 232.548994 5.235062 2000-2009 –> 2010-2019 1.293841 0.6165560 50829.895 503.57121 1 2
global (28.2,28.3] 5 cstar_tref ~ sal + nitrate + silicate + phosphate + phosphate_star 14697 0.6959523 31067.715 150.521775 4.744363 2000-2009 –> 2010-2019 1.359192 0.6632391 53232.464 358.13542 3 5
global (28.2,28.3] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate 14697 0.6872977 30699.891 157.754802 4.002963 2000-2009 –> 2010-2019 1.432547 0.7452492 55425.736 393.79310 2 11
global (28.2,28.3] 4 cstar_tref ~ sal + nitrate + silicate + phosphate 14697 0.7004532 31255.200 144.567449 4.362714 2000-2009 –> 2010-2019 1.451222 0.7507686 56141.143 352.10843 4 15
global (28.2,28.3] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 14697 0.7252212 32278.615 193.823489 5.143794 2000-2009 –> 2010-2019 1.454049 0.7288284 56515.005 339.20479 7 10
global (28.2,28.3] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 14697 0.7248752 32264.590 16.832453 5.158649 2000-2009 –> 2010-2019 1.454849 0.7299738 56535.477 31.85029 5 11
global (28.2,28.3] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 14697 0.7248955 32265.413 16.833639 5.157739 2000-2009 –> 2010-2019 1.454852 0.7299566 56535.782 31.86387 6 11
global (28.2,28.3] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 14697 0.7387663 32822.550 18.463981 5.129482 2000-2009 –> 2010-2019 1.468240 0.7294738 57078.386 32.77382 8 12
global (28.2,28.3] 4 cstar_tref ~ sal + temp + silicate + phosphate_star 14697 0.7445619 33050.246 12.353124 5.086479 2000-2009 –> 2010-2019 1.475487 0.7309250 57347.740 22.52869 9 16
global (28.2,28.3] 5 cstar_tref ~ temp + aou + nitrate + silicate + phosphate 14697 0.7898682 34788.551 232.607085 8.376274 2000-2009 –> 2010-2019 1.529541 0.7396725 59349.392 502.74326 10 18
global (28.3, Inf] 5 cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 5168 1.2818335 17246.489 320.114959 4.452828 1982-1999 –> 2000-2009 2.281566 0.9997322 31302.492 740.69734 8 9
global (28.3, Inf] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 5168 1.2714394 17162.336 72.809847 4.285113 1982-1999 –> 2000-2009 2.295036 1.0235967 31451.836 147.75691 2 4
global (28.3, Inf] 5 cstar_tref ~ sal + temp + aou + nitrate + silicate 5168 1.2788643 17222.520 79.788582 4.262597 1982-1999 –> 2000-2009 2.306117 1.0272523 31547.306 168.65737 5 8
global (28.3, Inf] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 5168 1.2783703 17218.526 48.671972 4.226537 1982-1999 –> 2000-2009 2.307360 1.0289900 31560.042 112.27649 4 8
global (28.3, Inf] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 5168 1.2812074 17241.440 51.257182 4.226922 1982-1999 –> 2000-2009 2.318383 1.0371752 31661.379 113.65522 6 11
global (28.3, Inf] 5 cstar_tref ~ sal + temp + silicate + phosphate + phosphate_star 5168 1.2834543 17259.550 49.118571 4.261183 1982-1999 –> 2000-2009 2.320970 1.0375156 31682.737 112.42828 13 19
global (28.3, Inf] 5 cstar_tref ~ sal + aou + nitrate + phosphate + phosphate_star 5168 1.2842757 17266.163 414.270744 4.458806 1982-1999 –> 2000-2009 2.324681 1.0404051 31716.878 991.01995 15 23
global (28.3, Inf] 4 cstar_tref ~ sal + temp + aou + silicate 5168 1.2904432 17313.681 23.904210 3.945948 1982-1999 –> 2000-2009 2.328711 1.0382678 31742.042 42.59849 26 33
global (28.3, Inf] 5 cstar_tref ~ sal + temp + aou + nitrate + phosphate 5168 1.2714314 17162.271 192.836449 4.270514 1982-1999 –> 2000-2009 2.337629 1.0661979 31855.376 337.11686 1 11
global (28.3, Inf] 5 cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 5168 1.2814235 17243.183 386.994209 4.403685 1982-1999 –> 2000-2009 2.341799 1.0603759 31882.091 919.13646 7 16
global (28.3, Inf] 5 cstar_tref ~ sal + nitrate + silicate + phosphate + phosphate_star 6286 1.4238108 22295.047 502.256445 7.304933 2000-2009 –> 2010-2019 2.705872 1.2820611 39543.371 855.34243 2 12
global (28.3, Inf] 5 cstar_tref ~ sal + temp + aou + nitrate + silicate 6286 1.4319674 22366.863 76.210457 7.146367 2000-2009 –> 2010-2019 2.710832 1.2788643 39589.383 155.99904 9 14
global (28.3, Inf] 5 cstar_tref ~ sal + temp + nitrate + silicate + phosphate_star 6286 1.4303417 22352.582 41.746465 7.168979 2000-2009 –> 2010-2019 2.711549 1.2812074 39594.022 93.00365 5 11
global (28.3, Inf] 5 cstar_tref ~ sal + aou + nitrate + silicate + phosphate_star 6286 1.4315306 22363.027 489.632759 7.159557 2000-2009 –> 2010-2019 2.712954 1.2814235 39606.210 876.62697 6 13
global (28.3, Inf] 4 cstar_tref ~ sal + nitrate + silicate + phosphate_star 6286 1.4315726 22361.396 27.719194 7.175290 2000-2009 –> 2010-2019 2.713641 1.2820685 39607.781 76.54572 7 18
global (28.3, Inf] 5 cstar_tref ~ sal + aou + nitrate + silicate + phosphate 6286 1.4227076 22285.302 479.526368 7.236019 2000-2009 –> 2010-2019 2.714747 1.2920390 39613.758 807.98112 1 28
global (28.3, Inf] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate 6286 1.4448609 22479.555 78.970132 6.819299 2000-2009 –> 2010-2019 2.716300 1.2714394 39641.891 151.77998 30 32
global (28.3, Inf] 5 cstar_tref ~ sal + temp + aou + silicate + phosphate_star 6286 1.4404055 22440.728 41.363299 6.852639 2000-2009 –> 2010-2019 2.718776 1.2783703 39659.255 90.03527 23 27
global (28.3, Inf] 5 cstar_tref ~ sal + aou + silicate + phosphate + phosphate_star 6286 1.4415785 22450.962 420.842127 6.816022 2000-2009 –> 2010-2019 2.723412 1.2818335 39697.452 740.95709 24 32
global (28.3, Inf] 4 cstar_tref ~ sal + aou + silicate + phosphate_star 6286 1.4416032 22449.177 30.906574 6.824247 2000-2009 –> 2010-2019 2.723587 1.2819834 39694.876 77.04012 25 34

5.4.2 per fitting unit

lm_best %>%
  select(-n_predictors) %>%
  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
global (-Inf,26] 1982-1999 –> 2000-2009 11.650105 25430.994
global (-Inf,26] 2000-2009 –> 2010-2019 12.010932 40449.742
global (26,26.5] 1982-1999 –> 2000-2009 8.865854 30429.509
global (26,26.5] 2000-2009 –> 2010-2019 9.153110 35120.738
global (26.5,26.75] 1982-1999 –> 2000-2009 8.217384 28379.511
global (26.5,26.75] 2000-2009 –> 2010-2019 9.131906 33527.242
global (26.75,27] 1982-1999 –> 2000-2009 8.634268 42740.054
global (26.75,27] 2000-2009 –> 2010-2019 9.235056 47443.355
global (27,27.25] 1982-1999 –> 2000-2009 8.021036 52618.292
global (27,27.25] 2000-2009 –> 2010-2019 8.814568 62969.126
global (27.25,27.5] 1982-1999 –> 2000-2009 6.580132 69037.811
global (27.25,27.5] 2000-2009 –> 2010-2019 7.576218 81898.065
global (27.5,27.75] 1982-1999 –> 2000-2009 6.050454 90707.278
global (27.5,27.75] 2000-2009 –> 2010-2019 7.144164 107706.993
global (27.75,27.85] 1982-1999 –> 2000-2009 4.890775 31461.437
global (27.75,27.85] 2000-2009 –> 2010-2019 5.583458 37511.622
global (27.85,27.95] 1982-1999 –> 2000-2009 4.678212 34753.603
global (27.85,27.95] 2000-2009 –> 2010-2019 5.285454 41621.316
global (27.95,28.05] 1982-1999 –> 2000-2009 5.219651 41471.550
global (27.95,28.05] 2000-2009 –> 2010-2019 5.385120 52107.046
global (28.05,28.1] 1982-1999 –> 2000-2009 2.744972 17605.545
global (28.05,28.1] 2000-2009 –> 2010-2019 3.450482 24354.512
global (28.1,28.15] 1982-1999 –> 2000-2009 1.105652 3987.853
global (28.1,28.15] 2000-2009 –> 2010-2019 1.281354 5312.648
global (28.1,28.2] 1982-1999 –> 2000-2009 2.251567 25606.806
global (28.1,28.2] 2000-2009 –> 2010-2019 2.364144 32226.282
global (28.15,28.2] 1982-1999 –> 2000-2009 1.248224 7944.306
global (28.15,28.2] 2000-2009 –> 2010-2019 1.452462 11022.023
global (28.2, Inf] 1982-1999 –> 2000-2009 1.340399 11792.945
global (28.2, Inf] 2000-2009 –> 2010-2019 1.543156 16623.563
global (28.2,28.3] 1982-1999 –> 2000-2009 1.555555 43858.358
global (28.2,28.3] 2000-2009 –> 2010-2019 1.437382 55899.102
global (28.3, Inf] 1982-1999 –> 2000-2009 2.316225 31640.218
global (28.3, Inf] 2000-2009 –> 2010-2019 2.715167 39624.800

5.5 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)

5.6 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/

5.7 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)

5.8 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 = ""))

lm_best %>%
  write_csv(paste(path_version_data,
                  "lm_best.csv",
                  sep = ""))

6 Model diagnotics

6.1 Selection criterion vs predictors

The selection criterion (rmse) was plotted against the number of predictors (limited to 2 - 5).

6.1.1 All models

if (params_local$MLR_criterion != "rank") {
lm_all_fitted_wide %>%
  ggplot(aes(as.factor(n_predictors),
             !!sym(params_local$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")
}

6.1.2 Best models

if (params_local$MLR_criterion != "rank") {
lm_best_target %>%
  ggplot(aes("",
             !!sym(params_local$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")
}

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)

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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
90f70da Donghe-Zhu 2021-08-06
496fa02 Donghe-Zhu 2021-08-05
19ecfe2 Donghe-Zhu 2021-08-05
3253031 Donghe-Zhu 2021-08-05
6c70952 Donghe-Zhu 2021-08-01
2666bd7 Donghe-Zhu 2021-08-01
e51cfa2 Donghe-Zhu 2021-08-01
78cbc18 Donghe-Zhu 2021-08-01
c55d4d7 Donghe-Zhu 2021-08-01
26741db Donghe-Zhu 2021-08-01
ca539b5 Donghe-Zhu 2021-08-01
1e98138 Donghe-Zhu 2021-08-01
777b9e9 Donghe-Zhu 2021-08-01
b110c47 Donghe-Zhu 2021-08-01
3611a48 Donghe-Zhu 2021-08-01
af832ef Donghe-Zhu 2021-07-31
016f9ed Donghe-Zhu 2021-07-31
2080ac5 Donghe-Zhu 2021-07-31
50f0a16 Donghe-Zhu 2021-07-31
1682579 Donghe-Zhu 2021-07-31
9fa8832 Donghe-Zhu 2021-07-31
4abf542 Donghe-Zhu 2021-07-31
a2483f0 Donghe-Zhu 2021-07-31
3300527 Donghe-Zhu 2021-07-31
afeb6fd Donghe-Zhu 2021-07-31
cb3ef32 Donghe-Zhu 2021-07-31
6528618 Donghe-Zhu 2021-07-31
f50925e Donghe-Zhu 2021-07-31
f4de2f3 Donghe-Zhu 2021-07-30
43bc7b4 Donghe-Zhu 2021-07-30
f8c34a8 Donghe-Zhu 2021-07-30
4f897c4 Donghe-Zhu 2021-07-30
0f6e960 Donghe-Zhu 2021-07-30
fe067ea Donghe-Zhu 2021-07-30
fbca39a Donghe-Zhu 2021-07-30
f945f95 Donghe-Zhu 2021-07-30
a3006cd Donghe-Zhu 2021-07-30
a55ffdc Donghe-Zhu 2021-07-30
d5e4458 Donghe-Zhu 2021-07-30
10a97ef Donghe-Zhu 2021-07-30
1c501fd Donghe-Zhu 2021-07-30
68bce3f Donghe-Zhu 2021-07-30
400c457 Donghe-Zhu 2021-07-29
3ba7852 Donghe-Zhu 2021-07-29
ec68363 Donghe-Zhu 2021-07-28
d3018f6 Donghe-Zhu 2021-07-28
b8ba2c4 Donghe-Zhu 2021-07-28
a4ddd84 Donghe-Zhu 2021-07-27
7ef49ab Donghe-Zhu 2021-07-27
da818c0 Donghe-Zhu 2021-07-27
8623898 Donghe-Zhu 2021-07-27
f84b77c Donghe-Zhu 2021-07-27
70ad9a5 Donghe-Zhu 2021-07-27
0d0b48d Donghe-Zhu 2021-07-27
d0d97c6 Donghe-Zhu 2021-07-27
829510d Donghe-Zhu 2021-07-27
780627d Donghe-Zhu 2021-07-27
e9838c9 Donghe-Zhu 2021-07-27
2777f33 Donghe-Zhu 2021-07-27
d19760f Donghe-Zhu 2021-07-27
cac5680 Donghe-Zhu 2021-07-23
7bb279a Donghe-Zhu 2021-07-23
d52eac4 Donghe-Zhu 2021-07-23
dcf34b1 Donghe-Zhu 2021-07-23
419cb72 Donghe-Zhu 2021-07-22
9447d10 Donghe-Zhu 2021-07-22
639c9a0 Donghe-Zhu 2021-07-22
25bb96f Donghe-Zhu 2021-07-22
cdc98a3 Donghe-Zhu 2021-07-22
e4805a1 Donghe-Zhu 2021-07-22
7957fd1 Donghe-Zhu 2021-07-21
b94be4e Donghe-Zhu 2021-07-21
9950e70 Donghe-Zhu 2021-07-21
698b6f0 Donghe-Zhu 2021-07-20
d84a1cb Donghe-Zhu 2021-07-20
4dce223 Donghe-Zhu 2021-07-20
6f28c0c Donghe-Zhu 2021-07-20
db1e7b7 Donghe-Zhu 2021-07-19
ff0575d Donghe-Zhu 2021-07-19
5d60eac Donghe-Zhu 2021-07-19
57d0420 Donghe-Zhu 2021-07-16
c1be8f6 Donghe-Zhu 2021-07-16
26a7c15 Donghe-Zhu 2021-07-16
046902b Donghe-Zhu 2021-07-16
3d4ac14 Donghe-Zhu 2021-07-15
be6fff9 Donghe-Zhu 2021-07-15
b473472 Donghe-Zhu 2021-07-15
363270a Donghe-Zhu 2021-07-15
8b75a03 Donghe-Zhu 2021-07-15
a01e135 Donghe-Zhu 2021-07-07
bc83ef2 Donghe-Zhu 2021-07-07
75e7b07 Donghe-Zhu 2021-07-06
697aa6d Donghe-Zhu 2021-07-06
d52e2de Donghe-Zhu 2021-07-06
f2bde6b Donghe-Zhu 2021-07-06
1133f28 Donghe-Zhu 2021-07-06
fe3a1cd Donghe-Zhu 2021-07-06
d9cdf15 Donghe-Zhu 2021-07-06
ee4cd2d Donghe-Zhu 2021-07-06
93821c1 Donghe-Zhu 2021-07-05
d44db8e Donghe-Zhu 2021-07-05
20b3436 Donghe-Zhu 2021-07-05
d982ea5 Donghe-Zhu 2021-07-05
7397e53 Donghe-Zhu 2021-07-05
f2e4805 Donghe-Zhu 2021-07-05
e2d011a Donghe-Zhu 2021-07-05
85e0349 Donghe-Zhu 2021-07-05
6ee0146 Donghe-Zhu 2021-07-05
a482a6b Donghe-Zhu 2021-07-05
f59f1b3 Donghe-Zhu 2021-07-05
116bcab Donghe-Zhu 2021-07-04
cb1c8dc Donghe-Zhu 2021-07-04
c9bdd83 Donghe-Zhu 2021-07-04
1920ad9 Donghe-Zhu 2021-07-04
0b02791 Donghe-Zhu 2021-06-25
caf62d8 Donghe-Zhu 2021-06-25
90a9da5 Donghe-Zhu 2021-06-25
bb413c8 Donghe-Zhu 2021-06-25
e7a5266 Donghe-Zhu 2021-06-24
7c57720 Donghe-Zhu 2021-06-24
b5b95a9 Donghe-Zhu 2021-06-24
472aad4 Donghe-Zhu 2021-06-24
d517836 Donghe-Zhu 2021-06-24
9221bfe Donghe-Zhu 2021-06-24
bff5fbf Donghe-Zhu 2021-06-23
256a616 Donghe-Zhu 2021-06-23
00072e2 Donghe-Zhu 2021-06-23
7431530 Donghe-Zhu 2021-06-20
d20c383 Donghe-Zhu 2021-06-18
b0874a2 Donghe-Zhu 2021-06-17
72c2f2e Donghe-Zhu 2021-06-15
d97dcb6 Donghe-Zhu 2021-06-08
25dda1d Donghe-Zhu 2021-06-08
bd2bcc3 Donghe-Zhu 2021-06-08
e9b27d7 Donghe-Zhu 2021-06-07
7ae0d4b Donghe-Zhu 2021-06-07
325391f Donghe-Zhu 2021-06-07
ed89970 Donghe-Zhu 2021-06-07
df37958 Donghe-Zhu 2021-06-07
812bf63 Donghe-Zhu 2021-06-06
65c89c8 Donghe-Zhu 2021-06-06
9baa3b2 Donghe-Zhu 2021-06-06
b7d99cf Donghe-Zhu 2021-06-06
5d8fa90 Donghe-Zhu 2021-06-06
dbe2a95 Donghe-Zhu 2021-05-28
d50b039 Donghe-Zhu 2021-05-28
441ebe5 Donghe-Zhu 2021-05-28
6262ad3 Donghe-Zhu 2021-05-28
a61aa77 Donghe-Zhu 2021-05-28
b8dbce4 Donghe-Zhu 2021-05-28
94a833e Donghe-Zhu 2021-05-27
5e0bd64 Donghe-Zhu 2021-05-27
af1eadd Donghe-Zhu 2021-05-27
9179bfe Donghe-Zhu 2021-05-27
28cbe5e Donghe-Zhu 2021-05-27
a618f02 Donghe-Zhu 2021-05-27
4623e38 Donghe-Zhu 2021-05-27
b81bb49 Donghe-Zhu 2021-05-27
951e188 Donghe-Zhu 2021-05-27
e377833 Donghe-Zhu 2021-05-26
2705a04 Donghe-Zhu 2021-03-28
cd5f759 Donghe-Zhu 2021-03-28
75dda4d Donghe-Zhu 2021-03-27
0b19f6e Donghe-Zhu 2021-03-27
e2704ca Donghe-Zhu 2021-03-27
3061a0b Donghe-Zhu 2021-03-27
b883157 Donghe-Zhu 2021-03-27
d19654d Donghe-Zhu 2021-03-26
6c53dbf Donghe-Zhu 2021-03-25
1914a11 Donghe-Zhu 2021-03-24
8be810e Donghe-Zhu 2021-03-23
bf19764 Donghe-Zhu 2021-03-22
3ec9d3d Donghe-Zhu 2021-03-22
134ace1 Donghe-Zhu 2021-03-22
f6d70a4 Donghe-Zhu 2021-03-22
183443b Donghe-Zhu 2021-03-21
2e6976b Donghe-Zhu 2021-03-21
51a42bd Donghe-Zhu 2021-03-16
f745381 Donghe-Zhu 2021-03-16
aecbf75 Donghe-Zhu 2021-03-14
1b2a0c2 Donghe-Zhu 2021-03-14
6733e48 Donghe-Zhu 2021-03-12
ba71e6a Donghe-Zhu 2021-03-12
9dbf5bd Donghe-Zhu 2021-03-11
a49df30 Donghe-Zhu 2021-03-11
b3348a5 Donghe-Zhu 2021-03-11
1c24ff7 Donghe-Zhu 2021-03-10
94ce5a8 Donghe-Zhu 2021-03-10
db33928 Donghe-Zhu 2021-03-10
3d1f470 Donghe-Zhu 2021-03-10
9b7bc66 Donghe-Zhu 2021-03-10
4168b43 Donghe-Zhu 2021-03-10
5365f80 Donghe-Zhu 2021-03-10
2b6c392 Donghe-Zhu 2021-03-10
9f58753 Donghe-Zhu 2021-03-10
fd528ed Donghe-Zhu 2021-03-10
9d5a62c Donghe-Zhu 2021-03-10
5d1e70b Donghe-Zhu 2021-03-10
2093979 Donghe-Zhu 2021-03-10
b865899 Donghe-Zhu 2021-03-10
cc2a956 Donghe-Zhu 2021-03-10
60689fb Donghe-Zhu 2021-03-10
dba33c8 Donghe-Zhu 2021-03-09
17f1c4a Donghe-Zhu 2021-03-09
c024d1a Donghe-Zhu 2021-03-09
02f7242 Donghe-Zhu 2021-03-09
6f50bc6 Donghe-Zhu 2021-03-09
1691156 Donghe-Zhu 2021-03-08
c0ceaf8 Donghe-Zhu 2021-03-08
058e0a1 Donghe-Zhu 2021-03-08
112dea0 Donghe-Zhu 2021-03-08
1843412 Donghe-Zhu 2021-03-08
3fbbfa4 Donghe-Zhu 2021-03-07
627c8fb Donghe-Zhu 2021-03-07
9ef3222 Donghe-Zhu 2021-03-05
8c1e978 Donghe-Zhu 2021-03-05
865f68c Donghe-Zhu 2021-03-05
ee69bc1 Donghe-Zhu 2021-03-05
a79291f Donghe-Zhu 2021-03-05
e8c6f30 Donghe-Zhu 2021-03-04
59288fe Donghe-Zhu 2021-03-04
731abc8 Donghe-Zhu 2021-03-04
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
global - 1982-1999 --> 2000-2009
(-Inf,26] 8 4 8 7 10 4 7
(26,26.5] 8 6 8 7 8 6 7
(26.5,26.75] 8 6 7 7 5 8 8
(26.75,27] 8 4 8 7 6 9 6
(27,27.25] 10 5 10 4 3 5 6
(27.25,27.5] 7 10 6 7 3 6 8
(27.5,27.75] 7 7 7 6 8 7 8
(27.75,27.85] 7 9 7 6 4 4 10
(27.85,27.95] 7 6 7 6 6 6 10
(27.95,28.05] 6 2 6 6 8 5 10
(28.05,28.1] 6 2 6 6 5 8 10
(28.1,28.15] 5 2 7 6 7 6 10
(28.1,28.2] 5 6 4 8 6 9 10
(28.15,28.2] 9 9 4 5 4 5 8
(28.2, Inf] 9 10 3 4 10 3 5
(28.2,28.3] 5 6 6 7 7 10 8
(28.3, Inf] 8 5 5 6 10 8 7
total 123.00 99.00 109.00 105.00 110.00 109.00 138.00
global - 2000-2009 --> 2010-2019
(-Inf,26] 7 6 7 7 10 3 7
(26,26.5] 6 5 6 8 10 6 8
(26.5,26.75] 7 4 7 6 6 9 8
(26.75,27] 7 3 7 7 6 10 7
(27,27.25] 9 6 10 5 2 6 6
(27.25,27.5] 5 10 5 8 5 6 9
(27.5,27.75] 7 7 7 6 8 7 8
(27.75,27.85] 7 9 7 6 4 4 10
(27.85,27.95] 8 5 6 6 7 5 10
(27.95,28.05] 5 2 5 7 9 6 10
(28.05,28.1] 4 2 4 8 5 10 10
(28.1,28.15] 5 3 4 7 10 6 10
(28.1,28.2] 5 5 6 7 9 6 10
(28.15,28.2] 8 9 3 4 5 5 9
(28.2, Inf] 10 10 2 4 9 4 5
(28.2,28.3] 4 6 7 5 9 10 7
(28.3, Inf] 7 6 4 7 10 10 4
total 111.00 98.00 97.00 108.00 124.00 113.00 138.00

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
90f70da Donghe-Zhu 2021-08-06
496fa02 Donghe-Zhu 2021-08-05
19ecfe2 Donghe-Zhu 2021-08-05
3253031 Donghe-Zhu 2021-08-05
6c70952 Donghe-Zhu 2021-08-01
2666bd7 Donghe-Zhu 2021-08-01
e51cfa2 Donghe-Zhu 2021-08-01
78cbc18 Donghe-Zhu 2021-08-01
c55d4d7 Donghe-Zhu 2021-08-01
26741db Donghe-Zhu 2021-08-01
ca539b5 Donghe-Zhu 2021-08-01
1e98138 Donghe-Zhu 2021-08-01
777b9e9 Donghe-Zhu 2021-08-01
b110c47 Donghe-Zhu 2021-08-01
3611a48 Donghe-Zhu 2021-08-01
af832ef Donghe-Zhu 2021-07-31
016f9ed Donghe-Zhu 2021-07-31
2080ac5 Donghe-Zhu 2021-07-31
50f0a16 Donghe-Zhu 2021-07-31
1682579 Donghe-Zhu 2021-07-31
9fa8832 Donghe-Zhu 2021-07-31
4abf542 Donghe-Zhu 2021-07-31
a2483f0 Donghe-Zhu 2021-07-31
3300527 Donghe-Zhu 2021-07-31
afeb6fd Donghe-Zhu 2021-07-31
cb3ef32 Donghe-Zhu 2021-07-31
6528618 Donghe-Zhu 2021-07-31
f50925e Donghe-Zhu 2021-07-31
f4de2f3 Donghe-Zhu 2021-07-30
43bc7b4 Donghe-Zhu 2021-07-30
f8c34a8 Donghe-Zhu 2021-07-30
4f897c4 Donghe-Zhu 2021-07-30
0f6e960 Donghe-Zhu 2021-07-30
fe067ea Donghe-Zhu 2021-07-30
fbca39a Donghe-Zhu 2021-07-30
f945f95 Donghe-Zhu 2021-07-30
a3006cd Donghe-Zhu 2021-07-30
a55ffdc Donghe-Zhu 2021-07-30
d5e4458 Donghe-Zhu 2021-07-30
10a97ef Donghe-Zhu 2021-07-30
1c501fd Donghe-Zhu 2021-07-30
68bce3f Donghe-Zhu 2021-07-30
400c457 Donghe-Zhu 2021-07-29
3ba7852 Donghe-Zhu 2021-07-29
ec68363 Donghe-Zhu 2021-07-28
d3018f6 Donghe-Zhu 2021-07-28
b8ba2c4 Donghe-Zhu 2021-07-28
a4ddd84 Donghe-Zhu 2021-07-27
7ef49ab Donghe-Zhu 2021-07-27
da818c0 Donghe-Zhu 2021-07-27
8623898 Donghe-Zhu 2021-07-27
f84b77c Donghe-Zhu 2021-07-27
70ad9a5 Donghe-Zhu 2021-07-27
0d0b48d Donghe-Zhu 2021-07-27
d0d97c6 Donghe-Zhu 2021-07-27
829510d Donghe-Zhu 2021-07-27
780627d Donghe-Zhu 2021-07-27
e9838c9 Donghe-Zhu 2021-07-27
2777f33 Donghe-Zhu 2021-07-27
d19760f Donghe-Zhu 2021-07-27
cac5680 Donghe-Zhu 2021-07-23
7bb279a Donghe-Zhu 2021-07-23
d52eac4 Donghe-Zhu 2021-07-23
dcf34b1 Donghe-Zhu 2021-07-23
419cb72 Donghe-Zhu 2021-07-22
9447d10 Donghe-Zhu 2021-07-22
639c9a0 Donghe-Zhu 2021-07-22
25bb96f Donghe-Zhu 2021-07-22
cdc98a3 Donghe-Zhu 2021-07-22
e4805a1 Donghe-Zhu 2021-07-22
7957fd1 Donghe-Zhu 2021-07-21
b94be4e Donghe-Zhu 2021-07-21
9950e70 Donghe-Zhu 2021-07-21
698b6f0 Donghe-Zhu 2021-07-20
d84a1cb Donghe-Zhu 2021-07-20
4dce223 Donghe-Zhu 2021-07-20
6f28c0c Donghe-Zhu 2021-07-20
db1e7b7 Donghe-Zhu 2021-07-19
ff0575d Donghe-Zhu 2021-07-19
5d60eac Donghe-Zhu 2021-07-19
57d0420 Donghe-Zhu 2021-07-16
c1be8f6 Donghe-Zhu 2021-07-16
26a7c15 Donghe-Zhu 2021-07-16
046902b Donghe-Zhu 2021-07-16
3d4ac14 Donghe-Zhu 2021-07-15
be6fff9 Donghe-Zhu 2021-07-15
b473472 Donghe-Zhu 2021-07-15
363270a Donghe-Zhu 2021-07-15
8b75a03 Donghe-Zhu 2021-07-15
a01e135 Donghe-Zhu 2021-07-07
bc83ef2 Donghe-Zhu 2021-07-07
75e7b07 Donghe-Zhu 2021-07-06
697aa6d Donghe-Zhu 2021-07-06
d52e2de Donghe-Zhu 2021-07-06
f2bde6b Donghe-Zhu 2021-07-06
1133f28 Donghe-Zhu 2021-07-06
fe3a1cd Donghe-Zhu 2021-07-06
d9cdf15 Donghe-Zhu 2021-07-06
ee4cd2d Donghe-Zhu 2021-07-06
93821c1 Donghe-Zhu 2021-07-05
d44db8e Donghe-Zhu 2021-07-05
20b3436 Donghe-Zhu 2021-07-05
d982ea5 Donghe-Zhu 2021-07-05
7397e53 Donghe-Zhu 2021-07-05
f2e4805 Donghe-Zhu 2021-07-05
e2d011a Donghe-Zhu 2021-07-05
85e0349 Donghe-Zhu 2021-07-05
6ee0146 Donghe-Zhu 2021-07-05
a482a6b Donghe-Zhu 2021-07-05
f59f1b3 Donghe-Zhu 2021-07-05
116bcab Donghe-Zhu 2021-07-04
cb1c8dc Donghe-Zhu 2021-07-04
c9bdd83 Donghe-Zhu 2021-07-04
1920ad9 Donghe-Zhu 2021-07-04
0b02791 Donghe-Zhu 2021-06-25
caf62d8 Donghe-Zhu 2021-06-25
90a9da5 Donghe-Zhu 2021-06-25
bb413c8 Donghe-Zhu 2021-06-25
e7a5266 Donghe-Zhu 2021-06-24
7c57720 Donghe-Zhu 2021-06-24
b5b95a9 Donghe-Zhu 2021-06-24
472aad4 Donghe-Zhu 2021-06-24
d517836 Donghe-Zhu 2021-06-24
9221bfe Donghe-Zhu 2021-06-24
bff5fbf Donghe-Zhu 2021-06-23
256a616 Donghe-Zhu 2021-06-23
00072e2 Donghe-Zhu 2021-06-23
7431530 Donghe-Zhu 2021-06-20
d20c383 Donghe-Zhu 2021-06-18
b0874a2 Donghe-Zhu 2021-06-17
72c2f2e Donghe-Zhu 2021-06-15
d97dcb6 Donghe-Zhu 2021-06-08
25dda1d Donghe-Zhu 2021-06-08
bd2bcc3 Donghe-Zhu 2021-06-08
e9b27d7 Donghe-Zhu 2021-06-07
7ae0d4b Donghe-Zhu 2021-06-07
325391f Donghe-Zhu 2021-06-07
ed89970 Donghe-Zhu 2021-06-07
df37958 Donghe-Zhu 2021-06-07
812bf63 Donghe-Zhu 2021-06-06
65c89c8 Donghe-Zhu 2021-06-06
9baa3b2 Donghe-Zhu 2021-06-06
b7d99cf Donghe-Zhu 2021-06-06
5d8fa90 Donghe-Zhu 2021-06-06
dbe2a95 Donghe-Zhu 2021-05-28
d50b039 Donghe-Zhu 2021-05-28
441ebe5 Donghe-Zhu 2021-05-28
6262ad3 Donghe-Zhu 2021-05-28
a61aa77 Donghe-Zhu 2021-05-28
b8dbce4 Donghe-Zhu 2021-05-28
94a833e Donghe-Zhu 2021-05-27
5e0bd64 Donghe-Zhu 2021-05-27
af1eadd Donghe-Zhu 2021-05-27
9179bfe Donghe-Zhu 2021-05-27
28cbe5e Donghe-Zhu 2021-05-27
a618f02 Donghe-Zhu 2021-05-27
4623e38 Donghe-Zhu 2021-05-27
b81bb49 Donghe-Zhu 2021-05-27
951e188 Donghe-Zhu 2021-05-27
e377833 Donghe-Zhu 2021-05-26
2705a04 Donghe-Zhu 2021-03-28
cd5f759 Donghe-Zhu 2021-03-28
75dda4d Donghe-Zhu 2021-03-27
0b19f6e Donghe-Zhu 2021-03-27
e2704ca Donghe-Zhu 2021-03-27
3061a0b Donghe-Zhu 2021-03-27
b883157 Donghe-Zhu 2021-03-27
d19654d Donghe-Zhu 2021-03-26
6c53dbf Donghe-Zhu 2021-03-25
1914a11 Donghe-Zhu 2021-03-24
8be810e Donghe-Zhu 2021-03-23
bf19764 Donghe-Zhu 2021-03-22
3ec9d3d Donghe-Zhu 2021-03-22
134ace1 Donghe-Zhu 2021-03-22
f6d70a4 Donghe-Zhu 2021-03-22
183443b Donghe-Zhu 2021-03-21
2e6976b Donghe-Zhu 2021-03-21
51a42bd Donghe-Zhu 2021-03-16
f745381 Donghe-Zhu 2021-03-16
aecbf75 Donghe-Zhu 2021-03-14
1b2a0c2 Donghe-Zhu 2021-03-14
6733e48 Donghe-Zhu 2021-03-12
ba71e6a Donghe-Zhu 2021-03-12
9dbf5bd Donghe-Zhu 2021-03-11
a49df30 Donghe-Zhu 2021-03-11
b3348a5 Donghe-Zhu 2021-03-11
1c24ff7 Donghe-Zhu 2021-03-10
94ce5a8 Donghe-Zhu 2021-03-10
db33928 Donghe-Zhu 2021-03-10
3d1f470 Donghe-Zhu 2021-03-10
9b7bc66 Donghe-Zhu 2021-03-10
5365f80 Donghe-Zhu 2021-03-10
2b6c392 Donghe-Zhu 2021-03-10
9f58753 Donghe-Zhu 2021-03-10
9d5a62c Donghe-Zhu 2021-03-10
5d1e70b Donghe-Zhu 2021-03-10
2093979 Donghe-Zhu 2021-03-10
b865899 Donghe-Zhu 2021-03-10
cc2a956 Donghe-Zhu 2021-03-10
dba33c8 Donghe-Zhu 2021-03-09
17f1c4a Donghe-Zhu 2021-03-09
c024d1a Donghe-Zhu 2021-03-09
02f7242 Donghe-Zhu 2021-03-09
1691156 Donghe-Zhu 2021-03-08
c0ceaf8 Donghe-Zhu 2021-03-08
058e0a1 Donghe-Zhu 2021-03-08
112dea0 Donghe-Zhu 2021-03-08
1843412 Donghe-Zhu 2021-03-08
3fbbfa4 Donghe-Zhu 2021-03-07
627c8fb Donghe-Zhu 2021-03-07
9ef3222 Donghe-Zhu 2021-03-05
8c1e978 Donghe-Zhu 2021-03-05
865f68c Donghe-Zhu 2021-03-05
ee69bc1 Donghe-Zhu 2021-03-05
a79291f Donghe-Zhu 2021-03-05
e8c6f30 Donghe-Zhu 2021-03-04
59288fe Donghe-Zhu 2021-03-04
731abc8 Donghe-Zhu 2021-03-04
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
90f70da Donghe-Zhu 2021-08-06
496fa02 Donghe-Zhu 2021-08-05
19ecfe2 Donghe-Zhu 2021-08-05
3253031 Donghe-Zhu 2021-08-05
6c70952 Donghe-Zhu 2021-08-01
2666bd7 Donghe-Zhu 2021-08-01
e51cfa2 Donghe-Zhu 2021-08-01
78cbc18 Donghe-Zhu 2021-08-01
c55d4d7 Donghe-Zhu 2021-08-01
26741db Donghe-Zhu 2021-08-01
ca539b5 Donghe-Zhu 2021-08-01
1e98138 Donghe-Zhu 2021-08-01
777b9e9 Donghe-Zhu 2021-08-01
b110c47 Donghe-Zhu 2021-08-01
3611a48 Donghe-Zhu 2021-08-01
af832ef Donghe-Zhu 2021-07-31
016f9ed Donghe-Zhu 2021-07-31
2080ac5 Donghe-Zhu 2021-07-31
50f0a16 Donghe-Zhu 2021-07-31
1682579 Donghe-Zhu 2021-07-31
9fa8832 Donghe-Zhu 2021-07-31
4abf542 Donghe-Zhu 2021-07-31
a2483f0 Donghe-Zhu 2021-07-31
3300527 Donghe-Zhu 2021-07-31
afeb6fd Donghe-Zhu 2021-07-31
cb3ef32 Donghe-Zhu 2021-07-31
6528618 Donghe-Zhu 2021-07-31
f50925e Donghe-Zhu 2021-07-31
f4de2f3 Donghe-Zhu 2021-07-30
43bc7b4 Donghe-Zhu 2021-07-30
f8c34a8 Donghe-Zhu 2021-07-30
4f897c4 Donghe-Zhu 2021-07-30
0f6e960 Donghe-Zhu 2021-07-30
fe067ea Donghe-Zhu 2021-07-30
fbca39a Donghe-Zhu 2021-07-30
f945f95 Donghe-Zhu 2021-07-30
a3006cd Donghe-Zhu 2021-07-30
a55ffdc Donghe-Zhu 2021-07-30
d5e4458 Donghe-Zhu 2021-07-30
10a97ef Donghe-Zhu 2021-07-30
1c501fd Donghe-Zhu 2021-07-30
68bce3f Donghe-Zhu 2021-07-30
400c457 Donghe-Zhu 2021-07-29
3ba7852 Donghe-Zhu 2021-07-29
ec68363 Donghe-Zhu 2021-07-28
d3018f6 Donghe-Zhu 2021-07-28
b8ba2c4 Donghe-Zhu 2021-07-28
a4ddd84 Donghe-Zhu 2021-07-27
7ef49ab Donghe-Zhu 2021-07-27
da818c0 Donghe-Zhu 2021-07-27
8623898 Donghe-Zhu 2021-07-27
f84b77c Donghe-Zhu 2021-07-27
70ad9a5 Donghe-Zhu 2021-07-27
0d0b48d Donghe-Zhu 2021-07-27
d0d97c6 Donghe-Zhu 2021-07-27
829510d Donghe-Zhu 2021-07-27
780627d Donghe-Zhu 2021-07-27
e9838c9 Donghe-Zhu 2021-07-27
2777f33 Donghe-Zhu 2021-07-27
d19760f Donghe-Zhu 2021-07-27
cac5680 Donghe-Zhu 2021-07-23
7bb279a Donghe-Zhu 2021-07-23
d52eac4 Donghe-Zhu 2021-07-23
dcf34b1 Donghe-Zhu 2021-07-23
419cb72 Donghe-Zhu 2021-07-22
9447d10 Donghe-Zhu 2021-07-22
639c9a0 Donghe-Zhu 2021-07-22
25bb96f Donghe-Zhu 2021-07-22
cdc98a3 Donghe-Zhu 2021-07-22
e4805a1 Donghe-Zhu 2021-07-22
7957fd1 Donghe-Zhu 2021-07-21
b94be4e Donghe-Zhu 2021-07-21
9950e70 Donghe-Zhu 2021-07-21
698b6f0 Donghe-Zhu 2021-07-20
d84a1cb Donghe-Zhu 2021-07-20
4dce223 Donghe-Zhu 2021-07-20
6f28c0c Donghe-Zhu 2021-07-20
db1e7b7 Donghe-Zhu 2021-07-19
ff0575d Donghe-Zhu 2021-07-19
5d60eac Donghe-Zhu 2021-07-19
57d0420 Donghe-Zhu 2021-07-16
c1be8f6 Donghe-Zhu 2021-07-16
26a7c15 Donghe-Zhu 2021-07-16
046902b Donghe-Zhu 2021-07-16
3d4ac14 Donghe-Zhu 2021-07-15
be6fff9 Donghe-Zhu 2021-07-15
b473472 Donghe-Zhu 2021-07-15
363270a Donghe-Zhu 2021-07-15
8b75a03 Donghe-Zhu 2021-07-15
a01e135 Donghe-Zhu 2021-07-07
bc83ef2 Donghe-Zhu 2021-07-07
75e7b07 Donghe-Zhu 2021-07-06
697aa6d Donghe-Zhu 2021-07-06
d52e2de Donghe-Zhu 2021-07-06
f2bde6b Donghe-Zhu 2021-07-06
1133f28 Donghe-Zhu 2021-07-06
fe3a1cd Donghe-Zhu 2021-07-06
d9cdf15 Donghe-Zhu 2021-07-06
ee4cd2d Donghe-Zhu 2021-07-06
93821c1 Donghe-Zhu 2021-07-05
d44db8e Donghe-Zhu 2021-07-05
20b3436 Donghe-Zhu 2021-07-05
d982ea5 Donghe-Zhu 2021-07-05
7397e53 Donghe-Zhu 2021-07-05
f2e4805 Donghe-Zhu 2021-07-05
e2d011a Donghe-Zhu 2021-07-05
85e0349 Donghe-Zhu 2021-07-05
6ee0146 Donghe-Zhu 2021-07-05
a482a6b Donghe-Zhu 2021-07-05
f59f1b3 Donghe-Zhu 2021-07-05
116bcab Donghe-Zhu 2021-07-04
cb1c8dc Donghe-Zhu 2021-07-04
c9bdd83 Donghe-Zhu 2021-07-04
1920ad9 Donghe-Zhu 2021-07-04
0b02791 Donghe-Zhu 2021-06-25
caf62d8 Donghe-Zhu 2021-06-25
90a9da5 Donghe-Zhu 2021-06-25
bb413c8 Donghe-Zhu 2021-06-25
e7a5266 Donghe-Zhu 2021-06-24
7c57720 Donghe-Zhu 2021-06-24
b5b95a9 Donghe-Zhu 2021-06-24
472aad4 Donghe-Zhu 2021-06-24
d517836 Donghe-Zhu 2021-06-24
9221bfe Donghe-Zhu 2021-06-24
bff5fbf Donghe-Zhu 2021-06-23
256a616 Donghe-Zhu 2021-06-23
00072e2 Donghe-Zhu 2021-06-23
7431530 Donghe-Zhu 2021-06-20
d20c383 Donghe-Zhu 2021-06-18
b0874a2 Donghe-Zhu 2021-06-17
72c2f2e Donghe-Zhu 2021-06-15
d97dcb6 Donghe-Zhu 2021-06-08
25dda1d Donghe-Zhu 2021-06-08
bd2bcc3 Donghe-Zhu 2021-06-08
e9b27d7 Donghe-Zhu 2021-06-07
7ae0d4b Donghe-Zhu 2021-06-07
325391f Donghe-Zhu 2021-06-07
ed89970 Donghe-Zhu 2021-06-07
df37958 Donghe-Zhu 2021-06-07
812bf63 Donghe-Zhu 2021-06-06
65c89c8 Donghe-Zhu 2021-06-06
9baa3b2 Donghe-Zhu 2021-06-06
b7d99cf Donghe-Zhu 2021-06-06
5d8fa90 Donghe-Zhu 2021-06-06
dbe2a95 Donghe-Zhu 2021-05-28
d50b039 Donghe-Zhu 2021-05-28
441ebe5 Donghe-Zhu 2021-05-28
6262ad3 Donghe-Zhu 2021-05-28
a61aa77 Donghe-Zhu 2021-05-28
b8dbce4 Donghe-Zhu 2021-05-28
94a833e Donghe-Zhu 2021-05-27
5e0bd64 Donghe-Zhu 2021-05-27
af1eadd Donghe-Zhu 2021-05-27
9179bfe Donghe-Zhu 2021-05-27
28cbe5e Donghe-Zhu 2021-05-27
a618f02 Donghe-Zhu 2021-05-27
4623e38 Donghe-Zhu 2021-05-27
b81bb49 Donghe-Zhu 2021-05-27
951e188 Donghe-Zhu 2021-05-27
e377833 Donghe-Zhu 2021-05-26
2705a04 Donghe-Zhu 2021-03-28
cd5f759 Donghe-Zhu 2021-03-28
75dda4d Donghe-Zhu 2021-03-27
0b19f6e Donghe-Zhu 2021-03-27
e2704ca Donghe-Zhu 2021-03-27
3061a0b Donghe-Zhu 2021-03-27
b883157 Donghe-Zhu 2021-03-27
d19654d Donghe-Zhu 2021-03-26
6c53dbf Donghe-Zhu 2021-03-25
1914a11 Donghe-Zhu 2021-03-24
8be810e Donghe-Zhu 2021-03-23
bf19764 Donghe-Zhu 2021-03-22
3ec9d3d Donghe-Zhu 2021-03-22
134ace1 Donghe-Zhu 2021-03-22
f6d70a4 Donghe-Zhu 2021-03-22
183443b Donghe-Zhu 2021-03-21
2e6976b Donghe-Zhu 2021-03-21
51a42bd Donghe-Zhu 2021-03-16
f745381 Donghe-Zhu 2021-03-16
aecbf75 Donghe-Zhu 2021-03-14
1b2a0c2 Donghe-Zhu 2021-03-14
6733e48 Donghe-Zhu 2021-03-12
ba71e6a Donghe-Zhu 2021-03-12
9dbf5bd Donghe-Zhu 2021-03-11
a49df30 Donghe-Zhu 2021-03-11
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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] car_3.0-10       carData_3.0-4    gt_0.2.2         corrr_0.4.3     
 [5] broom_0.7.5      kableExtra_1.3.1 knitr_1.30       olsrr_0.5.3     
 [9] GGally_2.0.0     lubridate_1.7.9  ggforce_0.3.3    metR_0.9.0      
[13] scico_1.2.0      patchwork_1.1.1  collapse_1.5.0   forcats_0.5.0   
[17] stringr_1.4.0    dplyr_1.0.5      purrr_0.3.4      readr_1.4.0     
[21] tidyr_1.1.2      tibble_3.0.4     ggplot2_3.3.3    tidyverse_1.3.0 
[25] 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                magrittr_2.0.1           Matrix_1.2-18           
[43] Rcpp_1.0.5               munsell_0.5.0            fansi_0.4.1             
[46] abind_1.4-5              lifecycle_1.0.0          stringi_1.5.3           
[49] whisker_0.4              yaml_2.2.1               MASS_7.3-53             
[52] plyr_1.8.6               grid_4.0.3               blob_1.2.1              
[55] parallel_4.0.3           promises_1.1.1           crayon_1.3.4            
[58] lattice_0.20-41          haven_2.3.1              hms_0.5.3               
[61] pillar_1.4.7             reprex_0.3.0             glue_1.4.2              
[64] evaluate_0.14            RcppArmadillo_0.10.1.2.2 data.table_1.13.6       
[67] modelr_0.1.8             vctrs_0.3.6              tweenr_1.0.2            
[70] httpuv_1.5.4             cellranger_1.1.0         gtable_0.3.0            
[73] polyclip_1.10-0          reshape_0.8.8            assertthat_0.2.1        
[76] openxlsx_4.2.3           xfun_0.20                RcppEigen_0.3.3.9.1     
[79] later_1.1.0.1            viridisLite_0.3.0        ellipsis_0.3.1          
[82] here_1.0.1