eMLR - assumption testing

workflowr

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Last updated: 2021-08-09

Checks: 7 0

Knit directory: emlr_obs_v_XXX/

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

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File	Version	Author	Date	Message
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html	594ed9a	jens-daniel-mueller	2021-06-04	Build site.
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html	315710b	jens-daniel-mueller	2021-06-03	include anomalous changes
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Rmd	da3c481	jens-daniel-mueller	2021-03-24	included model data
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html	6c0bec6	jens-daniel-mueller	2021-03-05	Build site.
html	3c2ec33	jens-daniel-mueller	2021-03-05	Build site.
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html	33ba23c	jens-daniel-mueller	2021-01-07	Build site.
html	318609d	jens-daniel-mueller	2020-12-23	adapted more variable predictor selection
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Rmd	83203db	jens-daniel-mueller	2020-12-03	calculate cant with variable inventory depth
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html	196be51	jens-daniel-mueller	2020-11-30	Build site.
Rmd	7a4b015	jens-daniel-mueller	2020-11-30	first rebuild on ETH server
Rmd	bc61ce3	Jens Müller	2020-11-30	Initial commit
html	bc61ce3	Jens Müller	2020-11-30	Initial commit

---

1 Version ID

The results displayed on this site correspond to the Version_ID: v_XXX

2 Required data

Required are:

• cleaned and prepared GLODAPv2.2020 file

GLODAP <-
  read_csv(paste(path_version_data,
                 "GLODAPv2.2020_MLR_fitting_ready.csv",
                 sep = ""))

3 Predictor correlation

The correlation between:

• pairs of seven potential predictor variables and
• C* and seven potential predictor variables

were investigated based on:

• property-property plots and
• calculated correlation coeffcients.

3.1 Correlation plots

For an overview, a random subset of data from all eras was plotted separately for both basins, with color indicating neutral density slabs (high density = dark-purple color).

plot_fun <- function(df){
  ggpairs(
    data = df,
    columns = c(params_local$MLR_target,
                params_local$MLR_predictors),
    upper = "blank",
    ggplot2::aes(col = gamma_slab, fill = gamma_slab, alpha = 0.01)
  ) +
    scale_fill_viridis_d(direction = -1) +
    scale_color_viridis_d(direction = -1) +
    labs(title = paste(
      "Basin:",
      unique(df$basin),
      "| era: all | random subsample:",
      nrow(df),
      "| data source:",
      unique(df$data_source)
    ))
}

GLODAP %>%
  slice_sample(n = 5e3) %>% 
  group_split(basin, data_source) %>%
  # head(1) %>% 
  map(plot_fun)

[[1]]

Past versions of predictor_correlation_per_basin-1.png

Version	Author	Date
755c6b1	jens-daniel-mueller	2021-08-09
cd8e0d5	jens-daniel-mueller	2021-08-06
15773a0	jens-daniel-mueller	2021-08-06
da61d1a	jens-daniel-mueller	2021-08-06
340d731	jens-daniel-mueller	2021-08-06
71546e4	jens-daniel-mueller	2021-08-06
29444a1	jens-daniel-mueller	2021-08-05
42e80c0	jens-daniel-mueller	2021-08-04
48f6eed	jens-daniel-mueller	2021-08-04
81a46a4	jens-daniel-mueller	2021-08-03
b88c61b	jens-daniel-mueller	2021-08-03
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c18a8b1	jens-daniel-mueller	2021-07-09
910d64a	jens-daniel-mueller	2021-07-02
1cbf907	jens-daniel-mueller	2021-07-02
955f5dc	jens-daniel-mueller	2021-06-29
9480f94	jens-daniel-mueller	2021-06-29
d054c39	jens-daniel-mueller	2021-06-24
4fcd066	jens-daniel-mueller	2021-06-24
3b6387f	jens-daniel-mueller	2021-06-18
c6b3da6	jens-daniel-mueller	2021-06-14
439ee80	jens-daniel-mueller	2021-06-11
33ffcab	jens-daniel-mueller	2021-06-10
7e1f407	jens-daniel-mueller	2021-06-10
2cbe18c	jens-daniel-mueller	2021-06-10
69c79d0	jens-daniel-mueller	2021-06-08
1772903	jens-daniel-mueller	2021-06-07
594ed9a	jens-daniel-mueller	2021-06-04
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207339d	jens-daniel-mueller	2021-06-03
315710b	jens-daniel-mueller	2021-06-03
be90356	jens-daniel-mueller	2021-06-02
d37a85d	jens-daniel-mueller	2021-05-31
4b7a5ee	jens-daniel-mueller	2021-05-28
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62bd574	jens-daniel-mueller	2021-05-20
7c56c39	jens-daniel-mueller	2021-05-19
52e7583	jens-daniel-mueller	2021-05-12
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
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338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
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0bade3b	jens-daniel-mueller	2021-03-15
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5017709	jens-daniel-mueller	2021-03-11
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af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
9d0b2d0	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
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61b263c	jens-daniel-mueller	2020-12-15
4d612dd	jens-daniel-mueller	2020-12-15
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
5d96d3c	jens-daniel-mueller	2020-12-11

[[2]]

Past versions of predictor_correlation_per_basin-2.png

Version	Author	Date
755c6b1	jens-daniel-mueller	2021-08-09
cd8e0d5	jens-daniel-mueller	2021-08-06
15773a0	jens-daniel-mueller	2021-08-06
da61d1a	jens-daniel-mueller	2021-08-06
340d731	jens-daniel-mueller	2021-08-06
71546e4	jens-daniel-mueller	2021-08-06
29444a1	jens-daniel-mueller	2021-08-05
42e80c0	jens-daniel-mueller	2021-08-04
48f6eed	jens-daniel-mueller	2021-08-04
81a46a4	jens-daniel-mueller	2021-08-03
b88c61b	jens-daniel-mueller	2021-08-03
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c18a8b1	jens-daniel-mueller	2021-07-09
910d64a	jens-daniel-mueller	2021-07-02
1cbf907	jens-daniel-mueller	2021-07-02
955f5dc	jens-daniel-mueller	2021-06-29
9480f94	jens-daniel-mueller	2021-06-29
d054c39	jens-daniel-mueller	2021-06-24
4fcd066	jens-daniel-mueller	2021-06-24
3b6387f	jens-daniel-mueller	2021-06-18
c6b3da6	jens-daniel-mueller	2021-06-14
439ee80	jens-daniel-mueller	2021-06-11
33ffcab	jens-daniel-mueller	2021-06-10
7e1f407	jens-daniel-mueller	2021-06-10
2cbe18c	jens-daniel-mueller	2021-06-10
69c79d0	jens-daniel-mueller	2021-06-08
1772903	jens-daniel-mueller	2021-06-07
594ed9a	jens-daniel-mueller	2021-06-04
db7df0e	jens-daniel-mueller	2021-06-04
2edc791	jens-daniel-mueller	2021-06-04
207339d	jens-daniel-mueller	2021-06-03
315710b	jens-daniel-mueller	2021-06-03
d37a85d	jens-daniel-mueller	2021-05-31
4b7a5ee	jens-daniel-mueller	2021-05-28
25bd183	jens-daniel-mueller	2021-05-26
62bd574	jens-daniel-mueller	2021-05-20
7c56c39	jens-daniel-mueller	2021-05-19
52e7583	jens-daniel-mueller	2021-05-12
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
ea17c90	jens-daniel-mueller	2021-04-09
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
ab5c8cf	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
98d4a6d	jens-daniel-mueller	2021-03-23
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
585b07f	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
6c0bec6	jens-daniel-mueller	2021-03-05
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
9d0b2d0	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
4d612dd	jens-daniel-mueller	2020-12-15
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
5d96d3c	jens-daniel-mueller	2020-12-11

GLODAP <- GLODAP %>%
  filter(data_source == "obs") %>%
  select(-data_source)

Individual correlation plots for each basin, era and neutral density (gamma) slab are available at:

/nfs/kryo/work/jenmueller/emlr_cant/observations/v_XXX/figures/Observations_correlation/

if (params_local$plot_all_figures == "y") {

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]

    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)[5]

      GLODAP_highlight <- GLODAP_basin_era %>%
        mutate(gamma_highlight = if_else(gamma_slab == i_gamma_slab,
                                         "in", "out")) %>%
        arrange(desc(gamma_highlight))
      
      p <- GLODAP_highlight %>%
        ggpairs(
          columns = c(params_local$MLR_target,
                      params_local$MLR_predictors),
          ggplot2::aes(
            col = gamma_highlight,
            fill = gamma_highlight,
            alpha = 0.01
          )
        ) +
        scale_fill_manual(values = c("red", "grey")) +
        scale_color_manual(values = c("red", "grey")) +
        labs(
          title = paste(
            i_era,
            "|",
            i_basin,
            "| Gamma slab",
            i_gamma_slab,
            "| # obs total",
            nrow(GLODAP_basin_era),
            "| # obs slab",
            nrow(GLODAP_highlight %>%
                   filter(gamma_highlight == "in"))
          )
        )
      
      png(
        filename = paste(
          path_version_figures,
          "Observations_correlation/",
          paste(
            "Predictor_correlation",
            i_era,
            i_basin,
            i_gamma_slab,
            ".png",
            sep = "_"
          ),
          sep = ""),
        width = 12,
        height = 12,
        units = "in",
        res = 300
      )
      
      print(p)
      
      dev.off()
      
    }
  }
}

}

3.2 Correlation assesment

3.2.1 Calculation of correlation coeffcients

Correlation coefficients were calculated individually within each slabs, era and basin.

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]
    
    GLODAP_basin_era <- GLODAP %>%
      filter(basin == i_basin,
             era == i_era) %>%
      select(basin,
             era,
             gamma_slab,
             params_local$MLR_target,
             params_local$MLR_predictors)
    
    for (i_gamma_slab in unique(GLODAP_basin_era$gamma_slab)) {
      # i_gamma_slab <- unique(GLODAP_basin_era$gamma_slab)[5]
      print(i_gamma_slab)
      
      GLODAP_basin_era_slab <- GLODAP_basin_era %>%
        filter(gamma_slab == i_gamma_slab)
      
      # calculate correlation table
      cor_target_predictor_temp <- GLODAP_basin_era_slab %>%
        select(-c(basin, era, gamma_slab)) %>%
        correlate() %>%
        focus(params_local$MLR_target) %>%
        mutate(basin = i_basin,
               era = i_era,
               gamma_slab = i_gamma_slab)
      
      if (exists("cor_target_predictor")) {
        cor_target_predictor <-
          bind_rows(cor_target_predictor, cor_target_predictor_temp)
      }
      
      if (!exists("cor_target_predictor")) {
        cor_target_predictor <- cor_target_predictor_temp
      }
      
      
      cor_predictors_temp <- GLODAP_basin_era_slab %>%
        select(-c(basin, era, gamma_slab)) %>%
        correlate() %>%
        shave %>%
        stretch() %>%
        filter(!is.na(r),
               x != params_local$MLR_target,
               y != params_local$MLR_target) %>%
        mutate(pair = paste(x, y, sep = " + ")) %>%
        select(-c(x, y)) %>%
        mutate(basin = i_basin,
               era = i_era,
               gamma_slab = i_gamma_slab)
      
      if (exists("cor_predictors")) {
        cor_predictors <- bind_rows(cor_predictors, cor_predictors_temp)
      }
      
      if (!exists("cor_predictors")) {
        cor_predictors <- cor_predictors_temp
      }
      
      
      
    }
  }
}

rm(cor_predictors_temp, cor_target_predictor_temp,
   i_gamma_slab, i_era, i_basin,
   GLODAP_basin_era, GLODAP_basin_era_slab)

3.2.2 Predictor pairs

Below, the range of correlations coefficients for each predictor pair is plotted per basin (facet) and density slab (color). Note that the range indicates the min and max values of in total 3 calculated coefficients (one per era).

# calculate min, max, mean across all eras
cor_predictors_stats <- cor_predictors %>% 
  group_by(pair, basin, gamma_slab) %>% 
  summarise(mean_r = mean(r),
            min_r = min(r),
            max_r = max(r)) %>% 
  ungroup()

# plot figure
cor_predictors_stats %>% 
  mutate(pair = reorder(pair, mean_r)) %>%
  ggplot() +
  geom_vline(xintercept = c(-0.9, 0.9), col = "red") +
  geom_vline(xintercept = 0) +
  geom_linerange(
    aes(y = pair, xmin = min_r, xmax = max_r, col = gamma_slab),
    position = position_dodge(width = 0.6)) +
  facet_wrap(~basin) +
  scale_color_viridis_d(direction = -1) +
  labs(x = "correlation coefficient", y = "") +
  theme(legend.position = "top")

Past versions of correlation_predictor_pairs-1.png

Version	Author	Date
755c6b1	jens-daniel-mueller	2021-08-09
cd8e0d5	jens-daniel-mueller	2021-08-06
15773a0	jens-daniel-mueller	2021-08-06
da61d1a	jens-daniel-mueller	2021-08-06
340d731	jens-daniel-mueller	2021-08-06
71546e4	jens-daniel-mueller	2021-08-06
29444a1	jens-daniel-mueller	2021-08-05
42e80c0	jens-daniel-mueller	2021-08-04
48f6eed	jens-daniel-mueller	2021-08-04
81a46a4	jens-daniel-mueller	2021-08-03
b88c61b	jens-daniel-mueller	2021-08-03
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
910d64a	jens-daniel-mueller	2021-07-02
1cbf907	jens-daniel-mueller	2021-07-02
955f5dc	jens-daniel-mueller	2021-06-29
9480f94	jens-daniel-mueller	2021-06-29
d054c39	jens-daniel-mueller	2021-06-24
4fcd066	jens-daniel-mueller	2021-06-24
7e1f407	jens-daniel-mueller	2021-06-10
2cbe18c	jens-daniel-mueller	2021-06-10
594ed9a	jens-daniel-mueller	2021-06-04
db7df0e	jens-daniel-mueller	2021-06-04
207339d	jens-daniel-mueller	2021-06-03
315710b	jens-daniel-mueller	2021-06-03
d37a85d	jens-daniel-mueller	2021-05-31
25bd183	jens-daniel-mueller	2021-05-26
62bd574	jens-daniel-mueller	2021-05-20
7c56c39	jens-daniel-mueller	2021-05-19
52e7583	jens-daniel-mueller	2021-05-12
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
bf40480	jens-daniel-mueller	2021-04-13
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
19faa17	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
98d4a6d	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
585b07f	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
6c0bec6	jens-daniel-mueller	2021-03-05
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
9d0b2d0	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
5d96d3c	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
0ff728b	jens-daniel-mueller	2020-12-01
91435ae	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

# print table
kable(cor_predictors_stats) %>%
  add_header_above() %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "400px")

pair	basin	gamma_slab	mean_r	min_r	max_r
aou + nitrate	global	(-Inf,26.5]	0.9128222	0.9057623	0.9198821
aou + nitrate	global	(26.5,26.75]	0.9109317	0.9070124	0.9148509
aou + nitrate	global	(26.75,27]	0.9185233	0.9181666	0.9188801
aou + nitrate	global	(27,27.25]	0.9009799	0.8996482	0.9023115
aou + nitrate	global	(27.25,27.5]	0.8807464	0.8746190	0.8868738
aou + nitrate	global	(27.5,27.75]	0.9024298	0.8975158	0.9073439
aou + nitrate	global	(27.75,27.85]	0.9484441	0.9473659	0.9495223
aou + nitrate	global	(27.85,27.95]	0.9639842	0.9634724	0.9644960
aou + nitrate	global	(27.95,28.05]	0.9695305	0.9683359	0.9707250
aou + nitrate	global	(28.05,28.1]	0.9800558	0.9796288	0.9804828
aou + nitrate	global	(28.1,28.2]	0.9722962	0.9717879	0.9728046
aou + nitrate	global	(28.2,28.3]	0.9486408	0.9462877	0.9509938
aou + nitrate	global	(28.3, Inf]	0.4264546	0.3375055	0.5154037
aou + oxygen	global	(-Inf,26.5]	-0.9453619	-0.9537973	-0.9369265
aou + oxygen	global	(26.5,26.75]	-0.9672139	-0.9681704	-0.9662574
aou + oxygen	global	(26.75,27]	-0.9719712	-0.9735231	-0.9704192
aou + oxygen	global	(27,27.25]	-0.9816913	-0.9817160	-0.9816666
aou + oxygen	global	(27.25,27.5]	-0.9848010	-0.9853874	-0.9842146
aou + oxygen	global	(27.5,27.75]	-0.9861342	-0.9868689	-0.9853995
aou + oxygen	global	(27.75,27.85]	-0.9918218	-0.9918376	-0.9918060
aou + oxygen	global	(27.85,27.95]	-0.9940792	-0.9942183	-0.9939400
aou + oxygen	global	(27.95,28.05]	-0.9949974	-0.9950713	-0.9949234
aou + oxygen	global	(28.05,28.1]	-0.9974367	-0.9975945	-0.9972790
aou + oxygen	global	(28.1,28.2]	-0.9951229	-0.9952144	-0.9950314
aou + oxygen	global	(28.2,28.3]	-0.9885347	-0.9897590	-0.9873103
aou + oxygen	global	(28.3, Inf]	-0.9980254	-0.9987907	-0.9972601
aou + silicate	global	(-Inf,26.5]	0.7671489	0.7549804	0.7793173
aou + silicate	global	(26.5,26.75]	0.6779811	0.6699492	0.6860129
aou + silicate	global	(26.75,27]	0.7369849	0.7291637	0.7448061
aou + silicate	global	(27,27.25]	0.8416863	0.8241387	0.8592339
aou + silicate	global	(27.25,27.5]	0.8953833	0.8805883	0.9101783
aou + silicate	global	(27.5,27.75]	0.9358728	0.9266937	0.9450520
aou + silicate	global	(27.75,27.85]	0.9750089	0.9727760	0.9772418
aou + silicate	global	(27.85,27.95]	0.9786190	0.9782978	0.9789403
aou + silicate	global	(27.95,28.05]	0.9836583	0.9834341	0.9838825
aou + silicate	global	(28.05,28.1]	0.9919173	0.9912429	0.9925916
aou + silicate	global	(28.1,28.2]	0.9814483	0.9792190	0.9836776
aou + silicate	global	(28.2,28.3]	0.9430461	0.9344280	0.9516641
aou + silicate	global	(28.3, Inf]	0.6465239	0.5318196	0.7612282
nitrate + silicate	global	(-Inf,26.5]	0.8764582	0.8679920	0.8849244
nitrate + silicate	global	(26.5,26.75]	0.8320277	0.8264497	0.8376057
nitrate + silicate	global	(26.75,27]	0.8231288	0.8094251	0.8368324
nitrate + silicate	global	(27,27.25]	0.8300450	0.8140877	0.8460022
nitrate + silicate	global	(27.25,27.5]	0.8394908	0.8295280	0.8494535
nitrate + silicate	global	(27.5,27.75]	0.8896720	0.8845195	0.8948245
nitrate + silicate	global	(27.75,27.85]	0.9504065	0.9478729	0.9529401
nitrate + silicate	global	(27.85,27.95]	0.9686385	0.9666197	0.9706573
nitrate + silicate	global	(27.95,28.05]	0.9753283	0.9747550	0.9759016
nitrate + silicate	global	(28.05,28.1]	0.9789062	0.9779035	0.9799088
nitrate + silicate	global	(28.1,28.2]	0.9827081	0.9826272	0.9827889
nitrate + silicate	global	(28.2,28.3]	0.9658387	0.9629941	0.9686833
nitrate + silicate	global	(28.3, Inf]	0.7041301	0.6676680	0.7405923
oxygen + nitrate	global	(-Inf,26.5]	-0.7509805	-0.7781912	-0.7237698
oxygen + nitrate	global	(26.5,26.75]	-0.7884743	-0.7915485	-0.7854002
oxygen + nitrate	global	(26.75,27]	-0.8100840	-0.8140038	-0.8061642
oxygen + nitrate	global	(27,27.25]	-0.8146304	-0.8171400	-0.8121209
oxygen + nitrate	global	(27.25,27.5]	-0.7997995	-0.8104375	-0.7891615
oxygen + nitrate	global	(27.5,27.75]	-0.8317868	-0.8400905	-0.8234830
oxygen + nitrate	global	(27.75,27.85]	-0.9073404	-0.9091153	-0.9055654
oxygen + nitrate	global	(27.85,27.95]	-0.9340111	-0.9349430	-0.9330793
oxygen + nitrate	global	(27.95,28.05]	-0.9436707	-0.9454854	-0.9418560
oxygen + nitrate	global	(28.05,28.1]	-0.9657641	-0.9667363	-0.9647919
oxygen + nitrate	global	(28.1,28.2]	-0.9493270	-0.9515725	-0.9470816
oxygen + nitrate	global	(28.2,28.3]	-0.9096153	-0.9110131	-0.9082176
oxygen + nitrate	global	(28.3, Inf]	-0.3941997	-0.4695877	-0.3188118
oxygen + silicate	global	(-Inf,26.5]	-0.5863672	-0.6258766	-0.5468578
oxygen + silicate	global	(26.5,26.75]	-0.5044118	-0.5148081	-0.4940155
oxygen + silicate	global	(26.75,27]	-0.5912500	-0.5935396	-0.5889605
oxygen + silicate	global	(27,27.25]	-0.7559439	-0.7770412	-0.7348465
oxygen + silicate	global	(27.25,27.5]	-0.8331989	-0.8534184	-0.8129794
oxygen + silicate	global	(27.5,27.75]	-0.8863659	-0.9009332	-0.8717985
oxygen + silicate	global	(27.75,27.85]	-0.9494441	-0.9524014	-0.9464869
oxygen + silicate	global	(27.85,27.95]	-0.9572348	-0.9578708	-0.9565989
oxygen + silicate	global	(27.95,28.05]	-0.9657673	-0.9667403	-0.9647943
oxygen + silicate	global	(28.05,28.1]	-0.9842811	-0.9846132	-0.9839491
oxygen + silicate	global	(28.1,28.2]	-0.9631623	-0.9670159	-0.9593087
oxygen + silicate	global	(28.2,28.3]	-0.9090463	-0.9248800	-0.8932127
oxygen + silicate	global	(28.3, Inf]	-0.6172683	-0.7288579	-0.5056787
sal + aou	global	(-Inf,26.5]	-0.2902134	-0.3143376	-0.2660891
sal + aou	global	(26.5,26.75]	-0.3864899	-0.3869821	-0.3859978
sal + aou	global	(26.75,27]	-0.3254089	-0.3629905	-0.2878273
sal + aou	global	(27,27.25]	-0.2324432	-0.2574242	-0.2074622
sal + aou	global	(27.25,27.5]	-0.2086591	-0.2716275	-0.1456907
sal + aou	global	(27.5,27.75]	-0.4179475	-0.4714072	-0.3644877
sal + aou	global	(27.75,27.85]	-0.6004140	-0.6009167	-0.5999114
sal + aou	global	(27.85,27.95]	-0.7061477	-0.7178379	-0.6944575
sal + aou	global	(27.95,28.05]	-0.7899340	-0.8049115	-0.7749566
sal + aou	global	(28.05,28.1]	-0.8870981	-0.8949545	-0.8792417
sal + aou	global	(28.1,28.2]	-0.8798496	-0.8822870	-0.8774121
sal + aou	global	(28.2,28.3]	-0.7595754	-0.7871500	-0.7320008
sal + aou	global	(28.3, Inf]	0.6294774	0.3823790	0.8765759
sal + nitrate	global	(-Inf,26.5]	-0.5260329	-0.5588375	-0.4932283
sal + nitrate	global	(26.5,26.75]	-0.6754642	-0.6761041	-0.6748243
sal + nitrate	global	(26.75,27]	-0.6259674	-0.6580795	-0.5938553
sal + nitrate	global	(27,27.25]	-0.5727297	-0.5981795	-0.5472800
sal + nitrate	global	(27.25,27.5]	-0.5775228	-0.6165293	-0.5385163
sal + nitrate	global	(27.5,27.75]	-0.7081786	-0.7391268	-0.6772305
sal + nitrate	global	(27.75,27.85]	-0.7743207	-0.7817625	-0.7668790
sal + nitrate	global	(27.85,27.95]	-0.8390903	-0.8497911	-0.8283894
sal + nitrate	global	(27.95,28.05]	-0.8992160	-0.9095278	-0.8889042
sal + nitrate	global	(28.05,28.1]	-0.9488908	-0.9547671	-0.9430145
sal + nitrate	global	(28.1,28.2]	-0.9476542	-0.9526903	-0.9426181
sal + nitrate	global	(28.2,28.3]	-0.9110310	-0.9228049	-0.8992571
sal + nitrate	global	(28.3, Inf]	-0.1602095	-0.4789784	0.1585594
sal + oxygen	global	(-Inf,26.5]	0.0399620	0.0374693	0.0424548
sal + oxygen	global	(26.5,26.75]	0.1456106	0.1424784	0.1487429
sal + oxygen	global	(26.75,27]	0.1016158	0.0680689	0.1351627
sal + oxygen	global	(27,27.25]	0.0492533	0.0236990	0.0748076
sal + oxygen	global	(27.25,27.5]	0.0437663	-0.0238859	0.1114185
sal + oxygen	global	(27.5,27.75]	0.2721693	0.2121078	0.3322309
sal + oxygen	global	(27.75,27.85]	0.4976418	0.4969634	0.4983203
sal + oxygen	global	(27.85,27.95]	0.6303342	0.6167135	0.6439549
sal + oxygen	global	(27.95,28.05]	0.7290680	0.7118084	0.7463276
sal + oxygen	global	(28.05,28.1]	0.8535552	0.8435022	0.8636081
sal + oxygen	global	(28.1,28.2]	0.8326959	0.8306919	0.8346999
sal + oxygen	global	(28.2,28.3]	0.6891834	0.6661990	0.7121679
sal + oxygen	global	(28.3, Inf]	-0.6536818	-0.8769879	-0.4303756
sal + silicate	global	(-Inf,26.5]	-0.5895995	-0.6289727	-0.5502262
sal + silicate	global	(26.5,26.75]	-0.7626686	-0.7788656	-0.7464717
sal + silicate	global	(26.75,27]	-0.6916660	-0.7164747	-0.6668572
sal + silicate	global	(27,27.25]	-0.5155217	-0.5194794	-0.5115641
sal + silicate	global	(27.25,27.5]	-0.4193922	-0.4515995	-0.3871850
sal + silicate	global	(27.5,27.75]	-0.5366718	-0.5707830	-0.5025607
sal + silicate	global	(27.75,27.85]	-0.6772076	-0.6797284	-0.6746867
sal + silicate	global	(27.85,27.95]	-0.7753706	-0.7818438	-0.7688973
sal + silicate	global	(27.95,28.05]	-0.8408718	-0.8487085	-0.8330351
sal + silicate	global	(28.05,28.1]	-0.9050920	-0.9104509	-0.8997330
sal + silicate	global	(28.1,28.2]	-0.9217266	-0.9262739	-0.9171792
sal + silicate	global	(28.2,28.3]	-0.8396347	-0.8552108	-0.8240586
sal + silicate	global	(28.3, Inf]	0.1791531	0.0198920	0.3384143
sal + temp	global	(-Inf,26.5]	0.7369710	0.7280835	0.7458585
sal + temp	global	(26.5,26.75]	0.9870542	0.9870009	0.9871074
sal + temp	global	(26.75,27]	0.9808831	0.9807717	0.9809945
sal + temp	global	(27,27.25]	0.9745150	0.9741391	0.9748909
sal + temp	global	(27.25,27.5]	0.9605371	0.9602296	0.9608447
sal + temp	global	(27.5,27.75]	0.9452124	0.9398104	0.9506143
sal + temp	global	(27.75,27.85]	0.9791296	0.9765976	0.9816616
sal + temp	global	(27.85,27.95]	0.9549174	0.9521455	0.9576894
sal + temp	global	(27.95,28.05]	0.9309645	0.9272385	0.9346904
sal + temp	global	(28.05,28.1]	0.9399521	0.9387600	0.9411443
sal + temp	global	(28.1,28.2]	0.8119259	0.7778103	0.8460415
sal + temp	global	(28.2,28.3]	0.3508709	0.3284259	0.3733159
sal + temp	global	(28.3, Inf]	0.7227857	0.5768629	0.8687084
temp + aou	global	(-Inf,26.5]	-0.3795197	-0.3917275	-0.3673118
temp + aou	global	(26.5,26.75]	-0.3678541	-0.3719116	-0.3637966
temp + aou	global	(26.75,27]	-0.3274895	-0.3655477	-0.2894313
temp + aou	global	(27,27.25]	-0.2322399	-0.2513888	-0.2130910
temp + aou	global	(27.25,27.5]	-0.1934536	-0.2557429	-0.1311643
temp + aou	global	(27.5,27.75]	-0.3715627	-0.4338293	-0.3092961
temp + aou	global	(27.75,27.85]	-0.5641238	-0.5726906	-0.5555570
temp + aou	global	(27.85,27.95]	-0.5836647	-0.6054072	-0.5619221
temp + aou	global	(27.95,28.05]	-0.5907502	-0.6174718	-0.5640286
temp + aou	global	(28.05,28.1]	-0.7077337	-0.7245260	-0.6909413
temp + aou	global	(28.1,28.2]	-0.4979091	-0.5429385	-0.4528797
temp + aou	global	(28.2,28.3]	0.0306652	-0.0109045	0.0722349
temp + aou	global	(28.3, Inf]	0.9430672	0.9245377	0.9615967
temp + nitrate	global	(-Inf,26.5]	-0.6535030	-0.6718838	-0.6351223
temp + nitrate	global	(26.5,26.75]	-0.6726559	-0.6730033	-0.6723084
temp + nitrate	global	(26.75,27]	-0.6368428	-0.6689609	-0.6047247
temp + nitrate	global	(27,27.25]	-0.5728276	-0.5935984	-0.5520568
temp + nitrate	global	(27.25,27.5]	-0.5568581	-0.5924144	-0.5213018
temp + nitrate	global	(27.5,27.75]	-0.6591579	-0.7005979	-0.6177178
temp + nitrate	global	(27.75,27.85]	-0.7476854	-0.7493872	-0.7459835
temp + nitrate	global	(27.85,27.95]	-0.7443198	-0.7626764	-0.7259632
temp + nitrate	global	(27.95,28.05]	-0.7445580	-0.7646879	-0.7244281
temp + nitrate	global	(28.05,28.1]	-0.8147777	-0.8292160	-0.8003395
temp + nitrate	global	(28.1,28.2]	-0.6464389	-0.7021035	-0.5907742
temp + nitrate	global	(28.2,28.3]	-0.1296723	-0.1693126	-0.0900320
temp + nitrate	global	(28.3, Inf]	0.2846922	0.2611903	0.3081942
temp + oxygen	global	(-Inf,26.5]	0.0602198	0.0477677	0.0726719
temp + oxygen	global	(26.5,26.75]	0.1203856	0.1198056	0.1209656
temp + oxygen	global	(26.75,27]	0.0971510	0.0635709	0.1307312
temp + oxygen	global	(27,27.25]	0.0430573	0.0235113	0.0626034
temp + oxygen	global	(27.25,27.5]	0.0206739	-0.0461812	0.0875290
temp + oxygen	global	(27.5,27.75]	0.2129798	0.1431340	0.2828256
temp + oxygen	global	(27.75,27.85]	0.4543655	0.4449939	0.4637371
temp + oxygen	global	(27.85,27.95]	0.4922021	0.4677819	0.5166223
temp + oxygen	global	(27.95,28.05]	0.5075737	0.4783224	0.5368250
temp + oxygen	global	(28.05,28.1]	0.6565374	0.6366502	0.6764247
temp + oxygen	global	(28.1,28.2]	0.4133926	0.3647204	0.4620648
temp + oxygen	global	(28.2,28.3]	-0.1724439	-0.2069203	-0.1379675
temp + oxygen	global	(28.3, Inf]	-0.9523633	-0.9639860	-0.9407406
temp + silicate	global	(-Inf,26.5]	-0.6577659	-0.6916900	-0.6238419
temp + silicate	global	(26.5,26.75]	-0.7845810	-0.7996458	-0.7695162
temp + silicate	global	(26.75,27]	-0.7252033	-0.7523866	-0.6980200
temp + silicate	global	(27,27.25]	-0.5434022	-0.5444421	-0.5423624
temp + silicate	global	(27.25,27.5]	-0.4415919	-0.4733885	-0.4097953
temp + silicate	global	(27.5,27.75]	-0.5482186	-0.5832978	-0.5131394
temp + silicate	global	(27.75,27.85]	-0.6621421	-0.6660422	-0.6582420
temp + silicate	global	(27.85,27.95]	-0.6853362	-0.6989744	-0.6716980
temp + silicate	global	(27.95,28.05]	-0.6800687	-0.6976900	-0.6624475
temp + silicate	global	(28.05,28.1]	-0.7405270	-0.7524080	-0.7286460
temp + silicate	global	(28.1,28.2]	-0.5875340	-0.6405229	-0.5345452
temp + silicate	global	(28.2,28.3]	-0.0681683	-0.1335493	-0.0027873
temp + silicate	global	(28.3, Inf]	0.6090206	0.5381534	0.6798877

rm(cor_predictors, cor_predictors_stats)

3.2.3 C* vs individual predictors

Below, the range of correlations coefficients for C* with each predictor is plotted per basin (facet) and density slab (color). Note that the range indicates the min and max values of in total 3 calculated coefficients (one per era).

cor_target_predictor <- cor_target_predictor %>% 
  rename(predictor = term)

# calculate min, max, mean across all eras
cor_target_predictor_stats <- cor_target_predictor %>%
  select(-era) %>% 
  group_by(predictor, basin, gamma_slab) %>% 
  summarise_all(list(mean_r = mean, min_r = min, max_r = max)) %>% 
  ungroup()

# plot figure
cor_target_predictor_stats %>% 
  mutate(predictor = reorder(predictor, mean_r)) %>%
  ggplot() +
  geom_vline(xintercept = c(-0.9, 0.9), col = "red") +
  geom_vline(xintercept = 0) +
  geom_linerange(
    aes(y = predictor, xmin = min_r, xmax = max_r, col = gamma_slab),
    position = position_dodge(width = 0.6)) +
  facet_wrap(~basin) +
  scale_color_viridis_d(direction = -1) +
  labs(x = "correlation coefficient", y = "C* correlation with...") +
  theme(legend.position = "top")

Past versions of correlation_predictors-1.png

Version	Author	Date
755c6b1	jens-daniel-mueller	2021-08-09
cd8e0d5	jens-daniel-mueller	2021-08-06
15773a0	jens-daniel-mueller	2021-08-06
da61d1a	jens-daniel-mueller	2021-08-06
340d731	jens-daniel-mueller	2021-08-06
71546e4	jens-daniel-mueller	2021-08-06
29444a1	jens-daniel-mueller	2021-08-05
42e80c0	jens-daniel-mueller	2021-08-04
48f6eed	jens-daniel-mueller	2021-08-04
81a46a4	jens-daniel-mueller	2021-08-03
b88c61b	jens-daniel-mueller	2021-08-03
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c18a8b1	jens-daniel-mueller	2021-07-09
910d64a	jens-daniel-mueller	2021-07-02
1cbf907	jens-daniel-mueller	2021-07-02
955f5dc	jens-daniel-mueller	2021-06-29
9480f94	jens-daniel-mueller	2021-06-29
d054c39	jens-daniel-mueller	2021-06-24
4fcd066	jens-daniel-mueller	2021-06-24
3b6387f	jens-daniel-mueller	2021-06-18
c6b3da6	jens-daniel-mueller	2021-06-14
439ee80	jens-daniel-mueller	2021-06-11
33ffcab	jens-daniel-mueller	2021-06-10
7e1f407	jens-daniel-mueller	2021-06-10
2cbe18c	jens-daniel-mueller	2021-06-10
69c79d0	jens-daniel-mueller	2021-06-08
1772903	jens-daniel-mueller	2021-06-07
594ed9a	jens-daniel-mueller	2021-06-04
db7df0e	jens-daniel-mueller	2021-06-04
2edc791	jens-daniel-mueller	2021-06-04
207339d	jens-daniel-mueller	2021-06-03
315710b	jens-daniel-mueller	2021-06-03
d37a85d	jens-daniel-mueller	2021-05-31
4b7a5ee	jens-daniel-mueller	2021-05-28
25bd183	jens-daniel-mueller	2021-05-26
62bd574	jens-daniel-mueller	2021-05-20
7c56c39	jens-daniel-mueller	2021-05-19
52e7583	jens-daniel-mueller	2021-05-12
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
bf40480	jens-daniel-mueller	2021-04-13
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
19faa17	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
98d4a6d	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
585b07f	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
6c0bec6	jens-daniel-mueller	2021-03-05
3c2ec33	jens-daniel-mueller	2021-03-05
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
9d0b2d0	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
5d96d3c	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
090e4d5	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
91435ae	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

# print table
kable(cor_target_predictor_stats) %>%
  add_header_above() %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "400px")

predictor	basin	gamma_slab	mean_r	min_r	max_r
aou	global	(-Inf,26.5]	0.0345604	-0.0322569	0.1013778
aou	global	(26.5,26.75]	-0.1181408	-0.1662921	-0.0699895
aou	global	(26.75,27]	-0.0613538	-0.0857335	-0.0369741
aou	global	(27,27.25]	0.3338827	0.3114994	0.3562659
aou	global	(27.25,27.5]	0.4194730	0.3961203	0.4428257
aou	global	(27.5,27.75]	0.2802632	0.2231655	0.3373608
aou	global	(27.75,27.85]	0.0561629	0.0236469	0.0886790
aou	global	(27.85,27.95]	-0.0512106	-0.0694816	-0.0329397
aou	global	(27.95,28.05]	-0.1159693	-0.1414499	-0.0904888
aou	global	(28.05,28.1]	-0.3581491	-0.4174391	-0.2988592
aou	global	(28.1,28.2]	-0.6110217	-0.6634202	-0.5586233
aou	global	(28.2,28.3]	-0.8661177	-0.8784076	-0.8538279
aou	global	(28.3, Inf]	0.2440586	-0.0037229	0.4918402
nitrate	global	(-Inf,26.5]	-0.1486277	-0.2105986	-0.0866569
nitrate	global	(26.5,26.75]	-0.4175270	-0.4536697	-0.3813844
nitrate	global	(26.75,27]	-0.3381571	-0.3581213	-0.3181929
nitrate	global	(27,27.25]	-0.0171867	-0.0335537	-0.0008197
nitrate	global	(27.25,27.5]	0.0142256	-0.0092840	0.0377352
nitrate	global	(27.5,27.75]	-0.1042244	-0.1612424	-0.0472065
nitrate	global	(27.75,27.85]	-0.2204194	-0.2548690	-0.1859698
nitrate	global	(27.85,27.95]	-0.2614774	-0.2858578	-0.2370969
nitrate	global	(27.95,28.05]	-0.3000740	-0.3327170	-0.2674310
nitrate	global	(28.05,28.1]	-0.4940103	-0.5540929	-0.4339278
nitrate	global	(28.1,28.2]	-0.7374605	-0.7827996	-0.6921215
nitrate	global	(28.2,28.3]	-0.9533170	-0.9536898	-0.9529441
nitrate	global	(28.3, Inf]	-0.5638861	-0.6828505	-0.4449216
oxygen	global	(-Inf,26.5]	-0.0517135	-0.1064832	0.0030562
oxygen	global	(26.5,26.75]	0.0046655	-0.0374353	0.0467664
oxygen	global	(26.75,27]	-0.0311575	-0.0501567	-0.0121584
oxygen	global	(27,27.25]	-0.3966473	-0.4231101	-0.3701845
oxygen	global	(27.25,27.5]	-0.4795222	-0.5005748	-0.4584695
oxygen	global	(27.5,27.75]	-0.3536956	-0.4052487	-0.3021426
oxygen	global	(27.75,27.85]	-0.1218064	-0.1523690	-0.0912437
oxygen	global	(27.85,27.95]	0.0017065	-0.0149722	0.0183851
oxygen	global	(27.95,28.05]	0.0697988	0.0464327	0.0931649
oxygen	global	(28.05,28.1]	0.3293774	0.2712547	0.3875000
oxygen	global	(28.1,28.2]	0.5795273	0.5188887	0.6401659
oxygen	global	(28.2,28.3]	0.8285692	0.8161608	0.8409776
oxygen	global	(28.3, Inf]	-0.2746298	-0.5086221	-0.0406374
sal	global	(-Inf,26.5]	0.3878101	0.3306390	0.4449811
sal	global	(26.5,26.75]	0.4258123	0.3872991	0.4643255
sal	global	(26.75,27]	0.4170919	0.3986988	0.4354850
sal	global	(27,27.25]	0.3355441	0.3260460	0.3450421
sal	global	(27.25,27.5]	0.3605013	0.3598191	0.3611836
sal	global	(27.5,27.75]	0.3901185	0.3693701	0.4108669
sal	global	(27.75,27.85]	0.3940397	0.3768525	0.4112269
sal	global	(27.85,27.95]	0.3792050	0.3558016	0.4026085
sal	global	(27.95,28.05]	0.3940427	0.3660938	0.4219916
sal	global	(28.05,28.1]	0.5113174	0.4576047	0.5650301
sal	global	(28.1,28.2]	0.6871240	0.6587693	0.7154786
sal	global	(28.2,28.3]	0.8848243	0.8786282	0.8910204
sal	global	(28.3, Inf]	0.5187098	0.4709263	0.5664933
silicate	global	(-Inf,26.5]	-0.1972365	-0.2509320	-0.1435410
silicate	global	(26.5,26.75]	-0.3761127	-0.4001922	-0.3520332
silicate	global	(26.75,27]	-0.0813507	-0.1061762	-0.0565251
silicate	global	(27,27.25]	0.3999890	0.3965274	0.4034506
silicate	global	(27.25,27.5]	0.4800033	0.4517096	0.5082970
silicate	global	(27.5,27.75]	0.3066601	0.2463433	0.3669769
silicate	global	(27.75,27.85]	0.0671811	0.0239695	0.1103927
silicate	global	(27.85,27.95]	-0.0401014	-0.0728688	-0.0073340
silicate	global	(27.95,28.05]	-0.1170116	-0.1502375	-0.0837857
silicate	global	(28.05,28.1]	-0.3443060	-0.4100929	-0.2785191
silicate	global	(28.1,28.2]	-0.6474709	-0.6957471	-0.5991947
silicate	global	(28.2,28.3]	-0.9051055	-0.9064566	-0.9037543
silicate	global	(28.3, Inf]	-0.2429858	-0.3877838	-0.0981878
temp	global	(-Inf,26.5]	-0.0068689	-0.0593283	0.0455905
temp	global	(26.5,26.75]	0.4397241	0.4071289	0.4723193
temp	global	(26.75,27]	0.4106549	0.3982276	0.4230822
temp	global	(27,27.25]	0.3028902	0.2943133	0.3114672
temp	global	(27.25,27.5]	0.3133095	0.3037558	0.3228632
temp	global	(27.5,27.75]	0.3332935	0.3068205	0.3597665
temp	global	(27.75,27.85]	0.3863516	0.3639065	0.4087967
temp	global	(27.85,27.95]	0.4006311	0.3799171	0.4213450
temp	global	(27.95,28.05]	0.4543989	0.4266849	0.4821130
temp	global	(28.05,28.1]	0.5633718	0.5289154	0.5978283
temp	global	(28.1,28.2]	0.5710440	0.4955733	0.6465148
temp	global	(28.2,28.3]	0.1120634	0.1008138	0.1233129
temp	global	(28.3, Inf]	0.3440882	0.1444905	0.5436860

rm(cor_target_predictor, cor_target_predictor_stats)

Session information

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] corrr_0.4.3      broom_0.7.5      kableExtra_1.3.1 knitr_1.30      
 [5] olsrr_0.5.3      GGally_2.0.0     lubridate_1.7.9  ggforce_0.3.3   
 [9] metR_0.9.0       scico_1.2.0      patchwork_1.1.1  collapse_1.5.0  
[13] forcats_0.5.0    stringr_1.4.0    dplyr_1.0.5      purrr_0.3.4     
[17] readr_1.4.0      tidyr_1.1.2      tibble_3.0.4     ggplot2_3.3.3   
[21] tidyverse_1.3.0  workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] fs_1.5.0                 webshot_0.5.2            RColorBrewer_1.1-2      
 [4] httr_1.4.2               rprojroot_2.0.2          tools_4.0.3             
 [7] backports_1.1.10         R6_2.5.0                 nortest_1.0-4           
[10] DBI_1.1.0                colorspace_1.4-1         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_2.1.0               
[19] rvest_0.3.6              xml2_1.3.2               labeling_0.4.2          
[22] scales_1.1.1             checkmate_2.0.0          goftest_1.2-2           
[25] digest_0.6.27            foreign_0.8-80           rmarkdown_2.5           
[28] rio_0.5.16               pkgconfig_2.0.3          htmltools_0.5.0         
[31] highr_0.8                dbplyr_1.4.4             rlang_0.4.10            
[34] readxl_1.3.1             rstudioapi_0.11          farver_2.0.3            
[37] generics_0.0.2           jsonlite_1.7.1           zip_2.1.1               
[40] car_3.0-10               magrittr_1.5             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               carData_3.0-4           
[52] MASS_7.3-53              plyr_1.8.6               grid_4.0.3              
[55] blob_1.2.1               parallel_4.0.3           promises_1.1.1          
[58] crayon_1.3.4             lattice_0.20-41          haven_2.3.1             
[61] hms_0.5.3                pillar_1.4.7             reprex_0.3.0            
[64] glue_1.4.2               evaluate_0.14            RcppArmadillo_0.10.1.2.0
[67] data.table_1.13.2        modelr_0.1.8             vctrs_0.3.5             
[70] tweenr_1.0.2             httpuv_1.5.4             cellranger_1.1.0        
[73] gtable_0.3.0             polyclip_1.10-0          reshape_0.8.8           
[76] assertthat_0.2.1         openxlsx_4.2.3           xfun_0.18               
[79] RcppEigen_0.3.3.7.0      later_1.1.0.1            viridisLite_0.3.0       
[82] ellipsis_0.3.1           here_0.1