Last updated: 2022-11-06
Checks: 7 0
Knit directory:
emlr_obs_analysis/analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20210412)
was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 820fff8. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish
or
wflow_git_commit
). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: data/
Ignored: output/other/
Ignored: output/presentation/
Ignored: output/publication/
Untracked files:
Untracked: code/visualization_equal_earth.R
Untracked: code/visualization_equal_earth_cleaned.R
Untracked: code/visualization_equal_earth_cleaned_jdm.R
Unstaged changes:
Modified: analysis/_site.yml
Modified: code/Workflowr_project_managment.R
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown
(analysis/data_adjustment_none_budgets.Rmd
) and HTML
(docs/data_adjustment_none_budgets.html
) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote
),
click on the hyperlinks in the table below to view the files as they
were in that past version.
File | Version | Author | Date | Message |
---|---|---|---|---|
html | e99640e | jens-daniel-mueller | 2022-07-29 | Build site. |
html | 08c00b4 | jens-daniel-mueller | 2022-07-16 | Build site. |
html | 692c937 | jens-daniel-mueller | 2022-07-16 | Build site. |
html | 1aabfea | jens-daniel-mueller | 2022-07-12 | Build site. |
Rmd | 567c3ed | jens-daniel-mueller | 2022-07-12 | revised bias decomposition |
html | b44c72a | jens-daniel-mueller | 2022-07-03 | Build site. |
html | 6e173bf | jens-daniel-mueller | 2022-06-30 | updated regional budget plots |
html | a13a7cf | jens-daniel-mueller | 2022-06-28 | Build site. |
html | b52b159 | jens-daniel-mueller | 2022-06-27 | Build site. |
html | 7600951 | jens-daniel-mueller | 2022-06-26 | Build site. |
html | 09b0780 | jens-daniel-mueller | 2022-05-24 | Build site. |
html | 25da2fb | jens-daniel-mueller | 2022-05-24 | Build site. |
html | e09320d | jens-daniel-mueller | 2022-04-12 | Build site. |
html | 8dca96a | jens-daniel-mueller | 2022-04-12 | Build site. |
Rmd | e5e9288 | jens-daniel-mueller | 2022-04-12 | 3 data adjustment procedures implemented |
version_id_pattern <- "d"
config <- "MLR_basins"
print(version_id_pattern)
[1] "d"
# identify required version IDs
Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_1", "d"))
Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_2", "d"))
Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_3", "d"))
Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
# print(Version_IDs)
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
# print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_budget_global <-
read_csv(paste(path_version_data,
"dcant_budget_global.csv",
sep = ""))
dcant_budget_global_mod_truth <-
read_csv(paste(
path_version_data,
"dcant_budget_global_mod_truth.csv",
sep = ""
))
dcant_budget_global_bias <-
read_csv(paste(path_version_data,
"dcant_budget_global_bias.csv",
sep = ""))
lm_best_predictor_counts <-
read_csv(paste(path_version_data,
"lm_best_predictor_counts.csv",
sep = ""))
lm_best_dcant <-
read_csv(paste(path_version_data,
"lm_best_dcant.csv",
sep = ""))
dcant_budget_global <- bind_rows(dcant_budget_global,
dcant_budget_global_mod_truth)
dcant_budget_global <- dcant_budget_global %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_global_bias <- dcant_budget_global_bias %>%
mutate(Version_ID = i_Version_IDs)
lm_best_predictor_counts <- lm_best_predictor_counts %>%
mutate(Version_ID = i_Version_IDs)
lm_best_dcant <- lm_best_dcant %>%
mutate(Version_ID = i_Version_IDs)
params_local <-
read_rds(paste(path_version_data,
"params_local.rds",
sep = ""))
params_local <- bind_cols(
Version_ID = i_Version_IDs,
MLR_basins := str_c(params_local$MLR_basins, collapse = "|"),
tref1 = params_local$tref1,
tref2 = params_local$tref2)
tref <- read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
params_local <- params_local %>%
mutate(
median_year_1 = sort(tref$median_year)[1],
median_year_2 = sort(tref$median_year)[2],
duration = median_year_2 - median_year_1,
period = paste(median_year_1, "-", median_year_2)
)
if (exists("dcant_budget_global_all")) {
dcant_budget_global_all <-
bind_rows(dcant_budget_global_all, dcant_budget_global)
}
if (!exists("dcant_budget_global_all")) {
dcant_budget_global_all <- dcant_budget_global
}
if (exists("dcant_budget_global_bias_all")) {
dcant_budget_global_bias_all <-
bind_rows(dcant_budget_global_bias_all,
dcant_budget_global_bias)
}
if (!exists("dcant_budget_global_bias_all")) {
dcant_budget_global_bias_all <- dcant_budget_global_bias
}
if (exists("lm_best_predictor_counts_all")) {
lm_best_predictor_counts_all <-
bind_rows(lm_best_predictor_counts_all, lm_best_predictor_counts)
}
if (!exists("lm_best_predictor_counts_all")) {
lm_best_predictor_counts_all <- lm_best_predictor_counts
}
if (exists("lm_best_dcant_all")) {
lm_best_dcant_all <-
bind_rows(lm_best_dcant_all, lm_best_dcant)
}
if (!exists("lm_best_dcant_all")) {
lm_best_dcant_all <- lm_best_dcant
}
if (exists("params_local_all")) {
params_local_all <- bind_rows(params_local_all, params_local)
}
if (!exists("params_local_all")) {
params_local_all <- params_local
}
}
rm(
dcant_budget_global,
dcant_budget_global_bias,
dcant_budget_global_mod_truth,
lm_best_predictor_counts,
lm_best_dcant,
params_local,
tref
)
# Version_IDs <- Version_IDs[1:length(Version_IDs)-1]
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
# print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_budget_basin_AIP <-
read_csv(paste(path_version_data,
"dcant_budget_basin_AIP.csv",
sep = ""))
dcant_budget_basin_AIP_mod_truth <-
read_csv(paste(
path_version_data,
"dcant_budget_basin_AIP_mod_truth.csv",
sep = ""
))
dcant_budget_basin_AIP <- bind_rows(dcant_budget_basin_AIP,
dcant_budget_basin_AIP_mod_truth)
dcant_budget_basin_AIP_bias <-
read_csv(paste(path_version_data,
"dcant_budget_basin_AIP_bias.csv",
sep = ""))
dcant_slab_budget_bias <-
read_csv(paste0(path_version_data,
"dcant_slab_budget_bias.csv"))
dcant_slab_budget <-
read_csv(paste0(path_version_data,
"dcant_slab_budget.csv"))
dcant_budget_basin_AIP <- dcant_budget_basin_AIP %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_basin_AIP_bias <- dcant_budget_basin_AIP_bias %>%
mutate(Version_ID = i_Version_IDs)
dcant_slab_budget <- dcant_slab_budget %>%
mutate(Version_ID = i_Version_IDs)
dcant_slab_budget_bias <- dcant_slab_budget_bias %>%
mutate(Version_ID = i_Version_IDs)
if (exists("dcant_budget_basin_AIP_all")) {
dcant_budget_basin_AIP_all <-
bind_rows(dcant_budget_basin_AIP_all, dcant_budget_basin_AIP)
}
if (!exists("dcant_budget_basin_AIP_all")) {
dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP
}
if (exists("dcant_budget_basin_AIP_bias_all")) {
dcant_budget_basin_AIP_bias_all <-
bind_rows(dcant_budget_basin_AIP_bias_all,
dcant_budget_basin_AIP_bias)
}
if (!exists("dcant_budget_basin_AIP_bias_all")) {
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias
}
if (exists("dcant_slab_budget_all")) {
dcant_slab_budget_all <-
bind_rows(dcant_slab_budget_all, dcant_slab_budget)
}
if (!exists("dcant_slab_budget_all")) {
dcant_slab_budget_all <- dcant_slab_budget
}
if (exists("dcant_slab_budget_bias_all")) {
dcant_slab_budget_bias_all <-
bind_rows(dcant_slab_budget_bias_all,
dcant_slab_budget_bias)
}
if (!exists("dcant_slab_budget_bias_all")) {
dcant_slab_budget_bias_all <- dcant_slab_budget_bias
}
}
rm(
dcant_budget_basin_AIP,
dcant_budget_basin_AIP_bias,
dcant_budget_basin_AIP_mod_truth,
dcant_slab_budget,
dcant_slab_budget_bias
)
# Version_IDs <- Version_IDs[1:length(Version_IDs)-1]
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
# print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_budget_basin_MLR <-
read_csv(paste(path_version_data,
"dcant_budget_basin_MLR.csv",
sep = ""))
dcant_budget_basin_MLR_mod_truth <-
read_csv(paste(
path_version_data,
"dcant_budget_basin_MLR_mod_truth.csv",
sep = ""
))
dcant_budget_basin_MLR <- bind_rows(dcant_budget_basin_MLR,
dcant_budget_basin_MLR_mod_truth)
dcant_budget_basin_MLR <- dcant_budget_basin_MLR %>%
mutate(Version_ID = i_Version_IDs)
if (exists("dcant_budget_basin_MLR_all")) {
dcant_budget_basin_MLR_all <-
bind_rows(dcant_budget_basin_MLR_all, dcant_budget_basin_MLR)
}
if (!exists("dcant_budget_basin_MLR_all")) {
dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR
}
}
rm(
dcant_budget_basin_MLR,
dcant_budget_basin_MLR_mod_truth
)
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
# print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_obs_budget <-
read_csv(paste0(path_version_data,
"anom_dcant_obs_budget.csv"))
dcant_obs_budget <- dcant_obs_budget %>%
mutate(Version_ID = i_Version_IDs)
if (exists("dcant_obs_budget_all")) {
dcant_obs_budget_all <-
bind_rows(dcant_obs_budget_all, dcant_obs_budget)
}
if (!exists("dcant_obs_budget_all")) {
dcant_obs_budget_all <- dcant_obs_budget
}
}
rm(dcant_obs_budget)
co2_atm <-
read_csv(paste(path_preprocessing,
"co2_atm.csv",
sep = ""))
all_predictors <- c("saltempaouoxygenphosphatenitratesilicate")
params_local_all <- params_local_all %>%
mutate(MLR_predictors = str_remove_all(all_predictors,
MLR_predictors))
dcant_budget_global_all <- dcant_budget_global_all %>%
filter(estimate == "dcant",
method == "total") %>%
select(-c(estimate, method)) %>%
rename(dcant = value)
dcant_budget_global_all_depth <- dcant_budget_global_all
dcant_budget_global_all <- dcant_budget_global_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
filter(estimate == "dcant") %>%
select(-c(estimate))
dcant_budget_global_bias_all_depth <- dcant_budget_global_bias_all
dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP_all %>%
filter(estimate == "dcant",
method == "total") %>%
select(-c(estimate, method)) %>%
rename(dcant = value)
dcant_budget_basin_AIP_all_depth <- dcant_budget_basin_AIP_all
dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias_all %>%
filter(estimate == "dcant") %>%
select(-c(estimate))
dcant_budget_basin_AIP_bias_all_depth <- dcant_budget_basin_AIP_bias_all
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR_all %>%
filter(estimate == "dcant",
method == "total") %>%
select(-c(estimate, method)) %>%
rename(dcant = value)
# dcant_budget_basin_MLR_all_depth <- dcant_budget_basin_MLR_all
dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
# dcant_budget_basin_MLR_bias_all <- dcant_budget_basin_MLR_bias_all %>%
# filter(estimate == "dcant") %>%
# select(-c(estimate))
#
# dcant_budget_basin_MLR_bias_all_depth <- dcant_budget_basin_MLR_bias_all
#
# dcant_budget_basin_MLR_bias_all <- dcant_budget_basin_MLR_bias_all %>%
# filter(inv_depth == params_global$inventory_depth_standard)
global_bias_rel_max <- 10
global_bias_rel_max
[1] 10
regional_bias_rel_max <- 20
regional_bias_rel_max
[1] 20
legend_title = expression(atop(Delta * C[ant],
(mu * mol ~ kg ^ {
-1
})))
dcant_budget_global_all %>%
ggplot(aes(period, dcant, col = MLR_basins)) +
geom_jitter(width = 0.05, height = 0) +
scale_color_brewer(palette = "Dark2") +
facet_grid(. ~ data_source) +
ylim(0,NA) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
dcant_budget_global_bias_all %>%
ggplot(aes(period, dcant_bias, col=MLR_basins)) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(atop(Delta * C[ant] ~ bias,
(PgC)))) +
geom_point() +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank())
p_global_bias <-
dcant_budget_global_bias_all %>%
ggplot() +
geom_hline(yintercept = global_bias_rel_max * c(-1, 1),
linetype = 2) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(Delta * C[ant] ~ bias ~ ("%")),
title = "Model-based assesment") +
theme(axis.title.x = element_blank()) +
geom_point(aes(period, dcant_bias_rel, col = MLR_basins),
alpha = 0.7) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_blank())
p_global_bias
dcant_budget_global_bias_all %>%
group_by(period) %>%
summarise(
dcant_bias_sd = sd(dcant_bias),
dcant_bias = mean(dcant_bias),
dcant_bias_rel_sd = sd(dcant_bias_rel),
dcant_bias_rel = mean(dcant_bias_rel)
) %>%
ungroup() %>%
kable() %>%
kable_styling() %>%
scroll_box(height = "300px")
period | dcant_bias_sd | dcant_bias | dcant_bias_rel_sd | dcant_bias_rel |
---|---|---|---|---|
1994 - 2004 | 1.4828720 | 1.392833 | 8.406304 | 7.895881 |
1994 - 2014 | 1.7122542 | 2.776167 | 4.451113 | 7.216821 |
2004 - 2014 | 0.8719045 | 1.506333 | 4.186213 | 7.232252 |
dcant_budget_basin_AIP_all %>%
ggplot(aes(period, dcant, col = MLR_basins)) +
geom_jitter(width = 0.05, height = 0) +
scale_color_brewer(palette = "Dark2") +
facet_grid(basin_AIP ~ data_source) +
ylim(0,NA) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
dcant_budget_basin_AIP_bias_all %>%
ggplot(aes(period, dcant_bias, col=MLR_basins)) +
geom_hline(yintercept = 0) +
geom_point() +
facet_grid(basin_AIP ~ .)
dcant_budget_basin_AIP_bias_all %>%
ggplot() +
geom_tile(aes(y = 0, height = regional_bias_rel_max * 2,
x = "2004 - 2014", width = Inf,
fill = "bias\nthreshold"), alpha = 0.5) +
geom_hline(yintercept = 0) +
scale_fill_manual(values = "grey70", name = "") +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(Delta ~ C[ant] ~ bias)) +
theme(axis.title.x = element_blank()) +
geom_jitter(aes(period, dcant_bias_rel, col = MLR_basins),
width = 0.05, height = 0) +
facet_grid(. ~ basin_AIP)
p_regional_bias <-
dcant_budget_basin_AIP_bias_all %>%
ggplot() +
geom_hline(yintercept = regional_bias_rel_max * c(-1,1),
linetype = 2) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(Delta * C[ant] ~ bias ~ ("%")),
title = "Model-based assesment") +
theme(axis.title.x = element_blank()) +
geom_point(aes(period, dcant_bias_rel, col = MLR_basins),
alpha = 0.7) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank()) +
facet_grid(. ~ basin_AIP) +
theme(
strip.background = element_blank(),
strip.text.x = element_blank()
)
p_regional_bias
dcant_slab_budget_all %>%
filter(data_source == "obs",
period != "1994 - 2014") %>%
ggplot(aes(MLR_basins, dcant, fill = gamma_slab)) +
geom_hline(yintercept = 0, col = "red") +
geom_col() +
scale_fill_scico_d(direction = -1) +
facet_grid(basin_AIP ~ period)
dcant_slab_budget_all %>%
filter(data_source == "obs",
period != "1994 - 2014") %>%
group_by(basin_AIP) %>%
group_split() %>%
map(
~ ggplot(data = .x,
aes(MLR_basins, dcant, fill = gamma_slab)) +
geom_hline(yintercept = 0) +
geom_col() +
scale_fill_scico_d(direction = -1) +
labs(title = paste("data_source:", unique(.x$basin_AIP))) +
facet_grid(gamma_slab ~ period)
)
[[1]]
[[2]]
[[3]]
dcant_slab_budget_bias_all %>%
filter(period != "1994 - 2014") %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(gamma_slab, dcant_bias, fill = gamma_slab)) +
geom_col() +
coord_flip() +
scale_x_discrete(limits = rev) +
scale_fill_scico_d(direction = -1) +
facet_grid(period ~ MLR_basins) +
labs(title = paste("data_source:", unique(.x$basin_AIP)))
)
[[1]]
Warning: Removed 48 rows containing missing values (position_stack).
[[2]]
Warning: Removed 24 rows containing missing values (position_stack).
[[3]]
Warning: Removed 108 rows containing missing values (position_stack).
dcant_slab_budget_all %>%
filter(period != "1994 - 2014",
data_source != "mod_truth") %>%
group_by(data_source, basin_AIP, gamma_slab, period) %>%
summarise(dcant_range = max(dcant) - min(dcant)) %>%
ungroup() %>%
group_split(basin_AIP) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(gamma_slab, dcant_range, fill = gamma_slab)) +
geom_col() +
coord_flip() +
scale_x_discrete(limits = rev) +
scale_fill_scico_d(direction = -1) +
facet_grid(period ~ data_source) +
labs(title = paste("data_source:", unique(.x$basin_AIP)))
)
`summarise()` has grouped output by 'data_source', 'basin_AIP', 'gamma_slab'.
You can override using the `.groups` argument.
[[1]]
[[2]]
[[3]]
dcant_budget_basin_MLR_all %>%
ggplot(aes(period, dcant, col = MLR_basins)) +
geom_jitter(width = 0.05, height = 0) +
scale_color_brewer(palette = "Dark2") +
facet_grid(basin ~ data_source) +
ylim(0,NA) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
dcant_budget_basin_MLR_bias_all <-
dcant_budget_basin_MLR_all %>%
filter(data_source %in% c("mod", "mod_truth")) %>%
pivot_wider(names_from = data_source,
values_from = dcant) %>%
mutate(dcant_bias = mod - mod_truth,
dcant_bias_rel = 100*(mod - mod_truth)/mod_truth)
dcant_budget_basin_MLR_bias_all %>%
ggplot(aes(period, dcant_bias, col=MLR_basins)) +
geom_hline(yintercept = 0) +
geom_point() +
facet_grid(basin ~ .)
dcant_budget_basin_MLR_bias_all %>%
ggplot() +
geom_tile(aes(y = 0, height = regional_bias_rel_max * 2,
x = "2004 - 2014", width = Inf,
fill = "bias\nthreshold"), alpha = 0.5) +
geom_hline(yintercept = 0) +
scale_fill_manual(values = "grey70", name = "") +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(Delta ~ C[ant] ~ bias)) +
theme(axis.title.x = element_blank()) +
geom_jitter(aes(period, dcant_bias_rel, col = MLR_basins),
width = 0.05, height = 0) +
facet_grid(. ~ basin)
p_regional_bias <-
dcant_budget_basin_MLR_bias_all %>%
ggplot() +
geom_hline(yintercept = regional_bias_rel_max * c(-1,1),
linetype = 2) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(Delta * C[ant] ~ bias ~ ("%")),
title = "Model-based assesment") +
theme(axis.title.x = element_blank()) +
geom_point(aes(period, dcant_bias_rel, col = MLR_basins),
alpha = 0.7) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank()) +
facet_grid(. ~ basin) +
theme(
strip.background = element_blank(),
strip.text.x = element_blank()
)
p_regional_bias
dcant_budget_basin_MLR_bias_all %>%
group_by(period, basin) %>%
summarise(
dcant_bias_sd = sd(dcant_bias),
dcant_bias = mean(dcant_bias),
dcant_bias_rel_sd = sd(dcant_bias_rel),
dcant_bias_rel = mean(dcant_bias_rel)
) %>%
ungroup() %>%
kable() %>%
kable_styling() %>%
scroll_box(height = "300px")
`summarise()` has grouped output by 'period'. You can override using the
`.groups` argument.
period | basin | dcant_bias_sd | dcant_bias | dcant_bias_rel_sd | dcant_bias_rel |
---|---|---|---|---|---|
1994 - 2004 | Indian | 1.0132346 | 0.2183333 | 21.017103 | 4.528798 |
1994 - 2004 | N_Atlantic | 0.2330789 | 0.1928333 | 11.946637 | 9.883820 |
1994 - 2004 | N_Pacific | 0.1759814 | 0.4383333 | 6.479434 | 16.138930 |
1994 - 2004 | S_Atlantic | 0.2899674 | 0.1415000 | 11.598697 | 5.660000 |
1994 - 2004 | S_Pacific | 0.8252037 | 0.4020000 | 14.600208 | 7.112527 |
1994 - 2014 | Indian | 1.3095493 | 0.7731667 | 12.533971 | 7.400140 |
1994 - 2014 | N_Atlantic | 0.3460339 | -0.0213333 | 8.054793 | -0.496586 |
1994 - 2014 | N_Pacific | 0.2774804 | 0.6051667 | 4.753004 | 10.365993 |
1994 - 2014 | S_Atlantic | 0.3555788 | 0.5125000 | 6.722988 | 9.689922 |
1994 - 2014 | S_Pacific | 1.2985550 | 0.9071667 | 10.308446 | 7.201450 |
2004 - 2014 | Indian | 0.5355758 | 0.5163333 | 9.519656 | 9.177628 |
2004 - 2014 | N_Atlantic | 0.1771892 | -0.1030000 | 7.556041 | -4.392324 |
2004 - 2014 | N_Pacific | 0.2145324 | 0.1978333 | 6.871635 | 6.336750 |
2004 - 2014 | S_Atlantic | 0.2090327 | 0.4416667 | 7.494898 | 15.836023 |
2004 - 2014 | S_Pacific | 0.5017992 | 0.4550000 | 7.225330 | 6.551476 |
dcant_budget_global_all_in <- dcant_budget_global_all %>%
filter(data_source %in% c("mod", "obs"))
dcant_budget_global_ensemble <- dcant_budget_global_all_in %>%
group_by(data_source, period, tref2) %>%
summarise(dcant_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_range = max(dcant)- min(dcant)) %>%
ungroup()
`summarise()` has grouped output by 'data_source', 'period'. You can override
using the `.groups` argument.
legend_title = expression(Delta * C[ant]~(PgC))
ggplot() +
geom_col(data = dcant_budget_global_ensemble,
aes(x = period,
y = dcant_mean),
fill = "darkgrey") +
geom_errorbar(
data = dcant_budget_global_ensemble,
aes(
x = period,
y = dcant_mean,
ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd
),
width = 0.1
) +
geom_point(
data = dcant_budget_global_all,
aes(period, dcant, col = MLR_basins),
alpha = 0.7,
position = position_jitter(width = 0.2, height = 0)
) +
scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
scale_color_brewer(palette = "Dark2") +
facet_grid(. ~ data_source) +
labs(y = legend_title) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
p_global_dcant <- ggplot() +
geom_col(data = dcant_budget_global_ensemble %>%
filter(data_source == "obs"),
aes(x = period,
y = dcant_mean),
fill = "darkgrey") +
geom_point(
data = dcant_budget_global_all %>%
filter(data_source == "obs"),
aes(period, dcant, col = MLR_basins),
alpha = 0.7,
position = position_jitter(width = 0.1, height = 0)
) +
geom_errorbar(
data = dcant_budget_global_ensemble %>%
filter(data_source == "obs"),
aes(
x = period,
y = dcant_mean,
ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd
),
width = 0.1
) +
scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
scale_color_brewer(palette = "Dark2") +
labs(y = legend_title,
title = "Observation-based results") +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank())
p_global_dcant_bias <-
p_global_dcant / p_global_bias +
plot_layout(guides = 'collect',
heights = c(2,1))
p_global_dcant_bias
# ggsave(plot = p_global_dcant_bias,
# path = here::here("output/publication"),
# filename = "Fig_global_dcant_budget.png",
# height = 5,
# width = 5)
rm(p_global_bias, p_global_dcant, p_global_dcant_bias)
dcant_ensemble <- dcant_budget_global_ensemble %>%
filter(data_source == "obs",
period != "1994 - 2014") %>%
select(year = tref2, dcant_mean, dcant_sd)
tcant_S04 <- bind_cols(year = 1994, dcant_mean = 118, dcant_sd = 19)
tcant_ensemble <- full_join(dcant_ensemble, tcant_S04)
Joining, by = c("year", "dcant_mean", "dcant_sd")
tcant_ensemble <- left_join(tcant_ensemble, co2_atm)
Joining, by = "year"
co2_atm_pi <- bind_cols(pCO2 = 280, dcant_mean = 0, year = 1750, dcant_sd = 0)
tcant_ensemble <- full_join(tcant_ensemble, co2_atm_pi)
Joining, by = c("year", "dcant_mean", "dcant_sd", "pCO2")
tcant_ensemble <- tcant_ensemble %>%
arrange(year) %>%
mutate(tcant = cumsum(dcant_mean),
tcant_sd = cumsum(dcant_sd))
tcant_ensemble %>%
ggplot(aes(pCO2, tcant, ymin = tcant - tcant_sd, ymax = tcant + tcant_sd)) +
geom_ribbon(fill = "grey80") +
geom_point() +
geom_line() +
scale_x_continuous(breaks = seq(280, 400, 30),
sec.axis = dup_axis(labels = c(1750, 1940, 1980, 2000, 2015),
name = "Year")) +
geom_text(aes(label = year), nudge_x = -5, nudge_y = 5) +
labs(x = expression(Atmospheric~pCO[2]~(µatm)),
y = expression(Total~oceanic~C[ant]~(PgC)))
# ggsave(path = "output/publication",
# filename = "Fig_global_dcant_budget_vs_atm_pCO2.png",
# height = 4,
# width = 7)
dcant_budget_global_all_in_sum <-
dcant_budget_global_all_in %>%
filter(period != "1994 - 2014") %>%
arrange(tref1) %>%
group_by(data_source, MLR_basins) %>%
mutate(dcant = dcant + lag(dcant)) %>%
ungroup() %>%
drop_na() %>%
mutate(estimate = "sum")
dcant_budget_global_all_in_sum <-
bind_rows(
dcant_budget_global_all_in_sum,
dcant_budget_global_all_in %>%
filter(period == "1994 - 2014") %>%
mutate(estimate = "direct")
)
ggplot() +
geom_point(
data = dcant_budget_global_all_in_sum,
aes(estimate, dcant, col = MLR_basins),
alpha = 0.7,
position = position_jitter(width = 0, height = 0)
) +
scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
scale_color_brewer(palette = "Dark2") +
facet_grid(. ~ data_source) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
dcant_budget_global_ensemble_bias <- full_join(
dcant_budget_global_ensemble %>%
filter(data_source == "mod") %>%
select(period, dcant_mean, dcant_sd),
dcant_budget_global_all %>%
filter(data_source == "mod_truth",
MLR_basins == unique(dcant_budget_global_all$MLR_basins)[1]) %>%
select(period, dcant)
)
Joining, by = "period"
dcant_budget_global_ensemble_bias <- dcant_budget_global_ensemble_bias %>%
mutate(dcant_mean_bias = dcant_mean - dcant,
dcant_mean_bias_rel = 100 * dcant_mean_bias / dcant)
dcant_budget_global_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias)) +
geom_hline(yintercept = 0) +
geom_point()
dcant_budget_global_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias_rel)) +
geom_hline(yintercept = 0) +
geom_point()
dcant_budget_global_all_depth %>%
filter(data_source != "mod_truth") %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant, MLR_basins, fill=period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .) +
labs(title = paste("data_source:", unique(.x$data_source)))
)
[[1]]
[[2]]
dcant_budget_global_bias_all_depth %>%
ggplot(aes(dcant_bias, MLR_basins, fill = period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .)
dcant_budget_global_bias_all_depth %>%
ggplot(aes(dcant_bias_rel, MLR_basins, fill = period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .)
rm(dcant_budget_global_all,
dcant_budget_global_all_depth,
dcant_budget_global_bias_all,
dcant_budget_global_bias_all_depth,
dcant_budget_global_ensemble,
dcant_budget_global_ensemble_bias)
dcant_budget_basin_AIP_ensemble <- dcant_budget_basin_AIP_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(basin_AIP, data_source, period) %>%
summarise(dcant_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_range = max(dcant)- min(dcant)) %>%
ungroup()
`summarise()` has grouped output by 'basin_AIP', 'data_source'. You can override
using the `.groups` argument.
dcant_budget_basin_AIP_ensemble %>%
ggplot(aes(period, dcant_mean, col=basin_AIP)) +
geom_pointrange(aes(ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd),
shape = 21) +
facet_grid(. ~ data_source)
p_regional_dcant <- ggplot() +
geom_col(
data = dcant_budget_basin_AIP_ensemble %>%
filter(data_source == "obs"),
aes(x = period,
y = dcant_mean),
fill = "darkgrey"
) +
geom_point(
data = dcant_budget_basin_AIP_all %>%
filter(data_source == "obs"),
aes(period, dcant, col = MLR_basins),
position = position_jitter(width = 0.1, height = 0),
alpha = 0.7
) +
geom_errorbar(
data = dcant_budget_basin_AIP_ensemble %>%
filter(data_source == "obs"),
aes(
x = period,
y = dcant_mean,
ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd
),
width = 0.1
) +
scale_y_continuous(limits = c(0, 35), expand = c(0, 0)) +
scale_color_brewer(palette = "Dark2") +
labs(y = legend_title,
title = "Observation-based results") +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank()) +
facet_grid(. ~ basin_AIP)
p_regional_dcant_bias <-
p_regional_dcant / p_regional_bias +
plot_layout(guides = 'collect',
heights = c(2,1))
p_regional_dcant_bias
# ggsave(plot = p_regional_dcant_bias,
# path = "output/publication",
# filename = "Fig_regional_dcant_budget.png",
# height = 5,
# width = 10)
rm(p_regional_bias, p_regional_dcant, p_regional_dcant_bias)
dcant_budget_basin_AIP_ensemble_bias <- full_join(
dcant_budget_basin_AIP_ensemble %>%
filter(data_source == "mod") %>%
select(basin_AIP, period, dcant_mean, dcant_sd),
dcant_budget_basin_AIP_all %>%
filter(data_source == "mod_truth",
MLR_basins == unique(dcant_budget_basin_AIP_all$MLR_basins)[1]) %>%
select(basin_AIP, period, dcant)
)
Joining, by = c("basin_AIP", "period")
dcant_budget_basin_AIP_ensemble_bias <- dcant_budget_basin_AIP_ensemble_bias %>%
mutate(dcant_mean_bias = dcant_mean - dcant,
dcant_mean_bias_rel = 100 * dcant_mean_bias / dcant)
dcant_budget_basin_AIP_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias, col = basin_AIP)) +
geom_hline(yintercept = 0) +
geom_point()
dcant_budget_basin_AIP_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias_rel, col = basin_AIP)) +
geom_hline(yintercept = 0) +
geom_point()
dcant_budget_basin_AIP_all_depth %>%
filter(data_source != "mod_truth") %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period) +
labs(title = paste("data_source:", unique(.x$data_source)))
)
[[1]]
[[2]]
dcant_budget_basin_AIP_bias_all_depth %>%
ggplot(aes(dcant_bias, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
dcant_budget_basin_AIP_bias_all_depth %>%
ggplot(aes(dcant_bias_rel, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
dcant_obs_budget_all %>%
group_by(inv_depth) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(estimate, dcant_pos, fill = basin_AIP)) +
scale_fill_brewer(palette = "Dark2") +
geom_col() +
facet_grid(MLR_basins ~ period) +
labs(title = paste("inventory depth:",unique(.x$inv_depth)))
)
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
lm_best_predictor_counts_all <-
full_join(lm_best_predictor_counts_all,
params_local_all)
Joining, by = "Version_ID"
lm_best_predictor_counts_all <- lm_best_predictor_counts_all %>%
mutate(n_predictors_total = rowSums(across(aou:temp), na.rm = TRUE)/10)
lm_best_predictor_counts_all %>%
ggplot(aes(x = MLR_basins, y = n_predictors_total)) +
# ggdist::stat_halfeye(
# adjust = .5,
# width = .6,
# .width = 0,
# justification = -.2,
# point_colour = NA
# ) +
geom_boxplot(width = 0.5,
outlier.shape = NA) +
gghalves::geom_half_point(
side = "l",
range_scale = .4,
alpha = .5,
aes(col = gamma_slab)
) +
scale_color_viridis_d() +
facet_grid(basin ~ data_source)
lm_best_predictor_counts_all %>%
pivot_longer(aou:temp,
names_to = "predictor",
values_to = "count") %>%
group_split(predictor) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(MLR_basins, count, color = gamma_slab)) +
geom_jitter(alpha = 0.5) +
scale_color_viridis_d() +
labs(title = paste0("predictor:", unique(.x$predictor))) +
coord_cartesian(ylim = c(0, 10)) +
facet_grid(basin ~ data_source)
)
[[1]]
Warning: Removed 1 rows containing missing values (geom_point).
[[2]]
Warning: Removed 40 rows containing missing values (geom_point).
[[3]]
Warning: Removed 1 rows containing missing values (geom_point).
[[4]]
Warning: Removed 6 rows containing missing values (geom_point).
[[5]]
[[6]]
Warning: Removed 3 rows containing missing values (geom_point).
[[7]]
Warning: Removed 3 rows containing missing values (geom_point).
lm_best_dcant_all <-
full_join(lm_best_dcant_all,
params_local_all)
Joining, by = "Version_ID"
lm_best_dcant_all %>%
count(basin, data_source, gamma_slab, MLR_basins, period) %>%
ggplot(aes(MLR_basins, n)) +
geom_jitter(height = 0, alpha = 0.3) +
facet_grid(basin ~ data_source)
dcant_budget_global_all_dissic %>%
filter(estimate == "dcant") %>%
ggplot(aes(inv_depth, value, col = !!sym(config))) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2") +
geom_point() +
geom_path() +
labs(y = "DIC change (PgC)") +
facet_grid(data_source ~ period, scales = "free_y")
Version | Author | Date |
---|---|---|
1aabfea | jens-daniel-mueller | 2022-07-12 |
dcant_budget_global_bias_all_decomposition <-
dcant_budget_global_bias_all_decomposition %>%
filter(estimate == "dcant") %>%
select(inv_depth, dcant_bias, contribution, !!sym(config), period) %>%
pivot_wider(names_from = contribution,
values_from = dcant_bias)
dcant_budget_global_bias_all_decomposition <-
full_join(
dcant_budget_global_bias_all_decomposition,
dcant_budget_global_bias_all_depth %>%
select(inv_depth, !!sym(config), period, mod_truth)
)
Joining, by = c("inv_depth", "MLR_basins", "period")
dcant_budget_global_bias_all_decomposition %>%
ggplot(aes(`dcant offset`, `delta C* - mod_truth`, col = !!sym(config))) +
geom_vline(xintercept = 0, col = "grey50") +
geom_hline(yintercept = 0, col = "grey50") +
geom_abline(intercept = 0, slope = 1) +
geom_point() +
coord_fixed() +
scale_color_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
Version | Author | Date |
---|---|---|
1aabfea | jens-daniel-mueller | 2022-07-12 |
dcant_budget_global_bias_all_decomposition %>%
ggplot(aes(`dcant offset`, `C* prediction error`, col = !!sym(config))) +
geom_vline(xintercept = 0, col = "grey50") +
geom_hline(yintercept = 0, col = "grey50") +
geom_abline(intercept = 0, slope = 1) +
geom_point() +
coord_fixed() +
scale_color_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
Version | Author | Date |
---|---|---|
1aabfea | jens-daniel-mueller | 2022-07-12 |
dcant_budget_global_bias_all_decomposition %>%
ggplot(aes(
`dcant offset`,
`C* prediction error` + `delta C* - mod_truth`,
col = !!sym(config)
)) +
geom_vline(xintercept = 0, col = "grey50") +
geom_hline(yintercept = 0, col = "grey50") +
geom_abline(intercept = 0, slope = 1) +
geom_point() +
coord_fixed() +
scale_color_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
Version | Author | Date |
---|---|---|
1aabfea | jens-daniel-mueller | 2022-07-12 |
dcant_budget_global_bias_all_decomposition %>%
ggplot(aes(`dcant offset`, `C* drift`, col = !!sym(config))) +
geom_vline(xintercept = 0, col = "grey50") +
geom_hline(yintercept = 0, col = "grey50") +
geom_abline(intercept = 0, slope = 1) +
geom_point() +
coord_fixed() +
scale_color_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
Version | Author | Date |
---|---|---|
1aabfea | jens-daniel-mueller | 2022-07-12 |
dcant_budget_global_bias_all_decomposition %>%
ggplot(aes(
`dcant offset` - `C* drift`,
`C* prediction error`,
col = !!sym(config)
)) +
geom_vline(xintercept = 0, col = "grey50") +
geom_hline(yintercept = 0, col = "grey50") +
geom_abline(intercept = 0, slope = 1) +
geom_point() +
coord_fixed() +
scale_color_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
Version | Author | Date |
---|---|---|
1aabfea | jens-daniel-mueller | 2022-07-12 |
dcant_budget_global_bias_all_decomposition %>%
ggplot(aes(
x = period,
fill = !!sym(config),
col = !!sym(config)
)) +
geom_hline(yintercept = 0) +
geom_point(
aes(y = `dcant offset`, shape = "dcant offset"),
position = position_nudge(x = -0.05),
alpha = 0.5
) +
geom_point(
aes(y = `dcant offset` - `C* drift`, shape = "dcant offset - C* drift"),
position = position_nudge(x = 0.05),
alpha = 0.5
) +
scale_color_brewer(palette = "Dark2") +
scale_fill_brewer(palette = "Dark2") +
scale_shape_manual(values = c(21,23)) +
facet_grid(inv_depth ~ .)
Version | Author | Date |
---|---|---|
1aabfea | jens-daniel-mueller | 2022-07-12 |
dcant_budget_global_bias_all_decomposition <-
dcant_budget_global_bias_all_decomposition %>%
mutate(
`dcant offset rel` = 100 * `dcant offset` / mod_truth,
`dcant offset rel corr` = 100 * (`dcant offset` - `C* drift`) / mod_truth,
`C* prediction error rel` = 100 * (`C* prediction error`) / mod_truth
)
dcant_budget_global_bias_all_decomposition %>%
ggplot(aes(
x = period,
fill = !!sym(config),
col = !!sym(config)
)) +
geom_hline(yintercept = 0) +
geom_point(
aes(y = `dcant offset rel`, shape = "dcant offset"),
position = position_nudge(x = -0.05),
alpha = 0.5
) +
geom_point(
aes(y = `dcant offset rel corr`, shape = "dcant offset - C* drift"),
position = position_nudge(x = 0.05),
alpha = 0.5
) +
scale_color_brewer(palette = "Dark2") +
scale_fill_brewer(palette = "Dark2") +
scale_shape_manual(values = c(21,23)) +
facet_grid(inv_depth ~ .)
Version | Author | Date |
---|---|---|
1aabfea | jens-daniel-mueller | 2022-07-12 |
dcant_budget_global_bias_all_decomposition <-
dcant_budget_global_bias_all_decomposition %>%
pivot_longer(-c(inv_depth:period),
names_to = "estimate",
values_to = "value")
dcant_budget_global_bias_all_decomposition %>%
group_by(inv_depth, estimate) %>%
summarise(mean = mean(value),
sd = sd(value)) %>%
ungroup() %>%
kable() %>%
kable_styling() %>%
scroll_box(height = "300px")
`summarise()` has grouped output by 'inv_depth'. You can override using the
`.groups` argument.
inv_depth | estimate | mean | sd |
---|---|---|---|
100 | C* drift | 0.2033333 | 0.0740032 |
100 | C* prediction error | -0.2260000 | 0.3340370 |
100 | C* prediction error rel | -4.2210478 | 7.9304934 |
100 | dcant offset | 0.3218333 | 0.1261312 |
100 | dcant offset rel | 6.9511182 | 2.1892369 |
100 | dcant offset rel corr | 2.6590514 | 1.9862102 |
100 | delta C* - mod_truth | -0.3946667 | 0.2164184 |
100 | mod_truth | 4.7563333 | 1.7425592 |
500 | C* drift | 1.3446667 | 0.4893907 |
500 | C* prediction error | -1.2675000 | 0.9507535 |
500 | C* prediction error rel | -7.2467954 | 4.9133843 |
500 | dcant offset | 0.5022778 | 0.6749676 |
500 | dcant offset rel | 3.1884095 | 4.4341542 |
500 | dcant offset rel corr | -4.7779560 | 4.0508120 |
500 | delta C* - mod_truth | 0.3773333 | 0.1374833 |
500 | mod_truth | 16.9600000 | 6.2242462 |
1000 | C* drift | 3.1486667 | 1.1458415 |
1000 | C* prediction error | -2.9200000 | 1.5964190 |
1000 | C* prediction error rel | -12.7348372 | 5.1795271 |
1000 | dcant offset | 0.5593333 | 1.0538447 |
1000 | dcant offset rel | 2.5870639 | 4.9555512 |
1000 | dcant offset rel corr | -11.3220057 | 4.6872027 |
1000 | delta C* - mod_truth | 2.0866667 | 0.7598902 |
1000 | mod_truth | 22.7516667 | 8.3557301 |
3000 | C* drift | 3.9900000 | 1.4521475 |
3000 | C* prediction error | -2.1191667 | 1.4816209 |
3000 | C* prediction error rel | -8.2558943 | 5.2558311 |
3000 | dcant offset | 1.8917778 | 1.4659397 |
3000 | dcant offset rel | 7.4483176 | 5.6454938 |
3000 | dcant offset rel corr | -8.1958269 | 5.7295779 |
3000 | delta C* - mod_truth | 2.6026667 | 0.9469936 |
3000 | mod_truth | 25.6453333 | 9.4254881 |
10000 | C* drift | 3.3760000 | 1.2289476 |
10000 | C* prediction error | -0.9883889 | 1.8316069 |
10000 | C* prediction error rel | -3.7428197 | 7.3892362 |
10000 | dcant offset | 2.1743333 | 2.0352708 |
10000 | dcant offset rel | 8.4523184 | 7.9561364 |
10000 | dcant offset rel corr | -4.5865577 | 7.9889550 |
10000 | delta C* - mod_truth | 1.7546667 | 0.6384472 |
10000 | mod_truth | 26.0420000 | 9.5725748 |
dcant_budget_global_bias_all_decomposition %>%
group_by(inv_depth, estimate, period) %>%
summarise(mean = mean(value),
sd = sd(value)) %>%
ungroup() %>%
kable() %>%
kable_styling() %>%
scroll_box(height = "300px")
`summarise()` has grouped output by 'inv_depth', 'estimate'. You can override
using the `.groups` argument.
inv_depth | estimate | period | mean | sd |
---|---|---|---|---|
100 | C* drift | 1994 - 2004 | 0.1550000 | 0.0000000 |
100 | C* drift | 1994 - 2014 | 0.3050000 | 0.0000000 |
100 | C* drift | 2004 - 2014 | 0.1500000 | 0.0000000 |
100 | C* prediction error | 1994 - 2004 | 0.1168333 | 0.1882832 |
100 | C* prediction error | 1994 - 2014 | -0.3811667 | 0.2664563 |
100 | C* prediction error | 2004 - 2014 | -0.4136667 | 0.2464319 |
100 | C* prediction error rel | 1994 - 2004 | 3.5201366 | 5.6728897 |
100 | C* prediction error rel | 1994 - 2014 | -5.3429586 | 3.7350198 |
100 | C* prediction error rel | 2004 - 2014 | -10.8403215 | 6.4578582 |
100 | dcant offset | 1994 - 2004 | 0.2983333 | 0.0571478 |
100 | dcant offset | 1994 - 2014 | 0.4610000 | 0.0681674 |
100 | dcant offset | 2004 - 2014 | 0.2061667 | 0.0788680 |
100 | dcant offset rel | 1994 - 2004 | 8.9886512 | 1.7218367 |
100 | dcant offset rel | 1994 - 2014 | 6.4620129 | 0.9555290 |
100 | dcant offset rel | 2004 - 2014 | 5.4026904 | 2.0667724 |
100 | dcant offset rel corr | 1994 - 2004 | 4.3185699 | 1.7218367 |
100 | dcant offset rel corr | 1994 - 2014 | 2.1867115 | 0.9555290 |
100 | dcant offset rel corr | 2004 - 2014 | 1.4718728 | 2.0667724 |
100 | delta C* - mod_truth | 1994 - 2004 | -0.4900000 | 0.0000000 |
100 | delta C* - mod_truth | 1994 - 2014 | -0.5910000 | 0.0000000 |
100 | delta C* - mod_truth | 2004 - 2014 | -0.1030000 | 0.0000000 |
100 | mod_truth | 1994 - 2004 | 3.3190000 | 0.0000000 |
100 | mod_truth | 1994 - 2014 | 7.1340000 | 0.0000000 |
100 | mod_truth | 2004 - 2014 | 3.8160000 | 0.0000000 |
500 | C* drift | 1994 - 2004 | 1.0250000 | 0.0000000 |
500 | C* drift | 1994 - 2014 | 2.0170000 | 0.0000000 |
500 | C* drift | 2004 - 2014 | 0.9920000 | 0.0000000 |
500 | C* prediction error | 1994 - 2004 | -0.4765000 | 0.6072913 |
500 | C* prediction error | 1994 - 2014 | -1.9586667 | 1.0157582 |
500 | C* prediction error | 2004 - 2014 | -1.3673333 | 0.5803201 |
500 | C* prediction error rel | 1994 - 2004 | -4.0570456 | 5.1706367 |
500 | C* prediction error rel | 1994 - 2014 | -7.6991614 | 3.9927601 |
500 | C* prediction error rel | 2004 - 2014 | -9.9841791 | 4.2374600 |
500 | dcant offset | 1994 - 2004 | 0.8033333 | 0.4948000 |
500 | dcant offset | 1994 - 2014 | 0.7153333 | 0.7937472 |
500 | dcant offset | 2004 - 2014 | -0.0118333 | 0.4401247 |
500 | dcant offset rel | 1994 - 2004 | 6.8397900 | 4.2128568 |
500 | dcant offset rel | 1994 - 2014 | 2.8118449 | 3.1200756 |
500 | dcant offset rel | 2004 - 2014 | -0.0864062 | 3.2137621 |
500 | dcant offset rel corr | 1994 - 2004 | -1.8873279 | 4.2128568 |
500 | dcant offset rel corr | 1994 - 2014 | -5.1166143 | 3.1200756 |
500 | dcant offset rel corr | 2004 - 2014 | -7.3299258 | 3.2137621 |
500 | delta C* - mod_truth | 1994 - 2004 | 0.2920000 | 0.0000000 |
500 | delta C* - mod_truth | 1994 - 2014 | 0.5660000 | 0.0000000 |
500 | delta C* - mod_truth | 2004 - 2014 | 0.2740000 | 0.0000000 |
500 | mod_truth | 1994 - 2004 | 11.7450000 | 0.0000000 |
500 | mod_truth | 1994 - 2014 | 25.4400000 | 0.0000000 |
500 | mod_truth | 2004 - 2014 | 13.6950000 | 0.0000000 |
1000 | C* drift | 1994 - 2004 | 2.3950000 | 0.0000000 |
1000 | C* drift | 1994 - 2014 | 4.7230000 | 0.0000000 |
1000 | C* drift | 2004 - 2014 | 2.3280000 | 0.0000000 |
1000 | C* prediction error | 1994 - 2004 | -1.8035000 | 1.1899643 |
1000 | C* prediction error | 1994 - 2014 | -4.4195000 | 1.6120702 |
1000 | C* prediction error | 2004 - 2014 | -2.5370000 | 0.5293233 |
1000 | C* prediction error rel | 1994 - 2004 | -11.4872611 | 7.5793906 |
1000 | C* prediction error rel | 1994 - 2014 | -12.9501568 | 4.7237384 |
1000 | C* prediction error rel | 2004 - 2014 | -13.7670936 | 2.8723863 |
1000 | dcant offset | 1994 - 2004 | 0.7423333 | 1.1324389 |
1000 | dcant offset | 1994 - 2014 | 0.8190000 | 1.4120714 |
1000 | dcant offset | 2004 - 2014 | 0.1166667 | 0.3786744 |
1000 | dcant offset rel | 1994 - 2004 | 4.7282378 | 7.2129866 |
1000 | dcant offset rel | 1994 - 2014 | 2.3998593 | 4.1376956 |
1000 | dcant offset rel | 2004 - 2014 | 0.6330946 | 2.0548858 |
1000 | dcant offset rel corr | 1994 - 2004 | -10.5265393 | 7.2129866 |
1000 | dcant offset rel corr | 1994 - 2014 | -11.4396226 | 4.1376956 |
1000 | dcant offset rel corr | 2004 - 2014 | -11.9998553 | 2.0548858 |
1000 | delta C* - mod_truth | 1994 - 2004 | 1.5570000 | 0.0000000 |
1000 | delta C* - mod_truth | 1994 - 2014 | 3.1310000 | 0.0000000 |
1000 | delta C* - mod_truth | 2004 - 2014 | 1.5720000 | 0.0000000 |
1000 | mod_truth | 1994 - 2004 | 15.7000000 | 0.0000000 |
1000 | mod_truth | 1994 - 2014 | 34.1270000 | 0.0000000 |
1000 | mod_truth | 2004 - 2014 | 18.4280000 | 0.0000000 |
3000 | C* drift | 1994 - 2004 | 3.0410000 | 0.0000000 |
3000 | C* drift | 1994 - 2014 | 5.9850000 | 0.0000000 |
3000 | C* drift | 2004 - 2014 | 2.9440000 | 0.0000000 |
3000 | C* prediction error | 1994 - 2004 | -1.5758333 | 1.3453424 |
3000 | C* prediction error | 1994 - 2014 | -3.2355000 | 1.6481984 |
3000 | C* prediction error | 2004 - 2014 | -1.5461667 | 0.8327603 |
3000 | C* prediction error rel | 1994 - 2004 | -8.9332955 | 7.6266576 |
3000 | C* prediction error rel | 1994 - 2014 | -8.4108870 | 4.2845960 |
3000 | C* prediction error rel | 2004 - 2014 | -7.4235004 | 3.9982731 |
3000 | dcant offset | 1994 - 2004 | 1.3928333 | 1.4828720 |
3000 | dcant offset | 1994 - 2014 | 2.7761667 | 1.7122542 |
3000 | dcant offset | 2004 - 2014 | 1.5063333 | 0.8719045 |
3000 | dcant offset rel | 1994 - 2004 | 7.8958806 | 8.4063039 |
3000 | dcant offset rel | 1994 - 2014 | 7.2168209 | 4.4511132 |
3000 | dcant offset rel | 2004 - 2014 | 7.2322515 | 4.1862133 |
3000 | dcant offset rel corr | 1994 - 2004 | -9.3433485 | 8.4063039 |
3000 | dcant offset rel corr | 1994 - 2014 | -8.3415653 | 4.4511132 |
3000 | dcant offset rel corr | 2004 - 2014 | -6.9025671 | 4.1862133 |
3000 | delta C* - mod_truth | 1994 - 2004 | 1.9330000 | 0.0000000 |
3000 | delta C* - mod_truth | 1994 - 2014 | 3.9040000 | 0.0000000 |
3000 | delta C* - mod_truth | 2004 - 2014 | 1.9710000 | 0.0000000 |
3000 | mod_truth | 1994 - 2004 | 17.6400000 | 0.0000000 |
3000 | mod_truth | 1994 - 2014 | 38.4680000 | 0.0000000 |
3000 | mod_truth | 2004 - 2014 | 20.8280000 | 0.0000000 |
10000 | C* drift | 1994 - 2004 | 2.5830000 | 0.0000000 |
10000 | C* drift | 1994 - 2014 | 5.0640000 | 0.0000000 |
10000 | C* drift | 2004 - 2014 | 2.4810000 | 0.0000000 |
10000 | C* prediction error | 1994 - 2004 | -0.7246667 | 1.9870841 |
10000 | C* prediction error | 1994 - 2014 | -1.5733333 | 2.4007029 |
10000 | C* prediction error | 2004 - 2014 | -0.6671667 | 1.0375526 |
10000 | C* prediction error rel | 1994 - 2004 | -4.0479648 | 11.0997881 |
10000 | C* prediction error rel | 1994 - 2014 | -4.0276818 | 6.1457207 |
10000 | C* prediction error rel | 2004 - 2014 | -3.1528126 | 4.9031359 |
10000 | dcant offset | 1994 - 2004 | 1.6138333 | 2.1603017 |
10000 | dcant offset | 1994 - 2014 | 3.1661667 | 2.5364729 |
10000 | dcant offset | 2004 - 2014 | 1.7430000 | 1.0966235 |
10000 | dcant offset rel | 1994 - 2004 | 9.0148214 | 12.0673762 |
10000 | dcant offset rel | 1994 - 2014 | 8.1052829 | 6.4932876 |
10000 | dcant offset rel | 2004 - 2014 | 8.2368508 | 5.1822860 |
10000 | dcant offset rel corr | 1994 - 2004 | -5.4137340 | 12.0673762 |
10000 | dcant offset rel corr | 1994 - 2014 | -4.8583911 | 6.4932876 |
10000 | dcant offset rel corr | 2004 - 2014 | -3.4875478 | 5.1822860 |
10000 | delta C* - mod_truth | 1994 - 2004 | 1.3030000 | 0.0000000 |
10000 | delta C* - mod_truth | 1994 - 2014 | 2.6320000 | 0.0000000 |
10000 | delta C* - mod_truth | 2004 - 2014 | 1.3290000 | 0.0000000 |
10000 | mod_truth | 1994 - 2004 | 17.9020000 | 0.0000000 |
10000 | mod_truth | 1994 - 2014 | 39.0630000 | 0.0000000 |
10000 | mod_truth | 2004 - 2014 | 21.1610000 | 0.0000000 |
sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/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] kableExtra_1.3.4 geomtextpath_0.1.0 colorspace_2.0-2 marelac_2.1.10
[5] shape_1.4.6 ggforce_0.3.3 metR_0.11.0 scico_1.3.0
[9] patchwork_1.1.1 collapse_1.7.0 forcats_0.5.1 stringr_1.4.0
[13] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4
[17] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 gghalves_0.1.1 bit64_4.0.5 lubridate_1.8.0
[5] gsw_1.0-6 RColorBrewer_1.1-2 webshot_0.5.2 httr_1.4.2
[9] rprojroot_2.0.2 tools_4.1.2 backports_1.4.1 bslib_0.3.1
[13] utf8_1.2.2 R6_2.5.1 DBI_1.1.2 withr_2.4.3
[17] tidyselect_1.1.1 processx_3.5.2 bit_4.0.4 compiler_4.1.2
[21] git2r_0.29.0 textshaping_0.3.6 cli_3.1.1 rvest_1.0.2
[25] xml2_1.3.3 labeling_0.4.2 sass_0.4.0 scales_1.1.1
[29] checkmate_2.0.0 SolveSAPHE_2.1.0 callr_3.7.0 systemfonts_1.0.3
[33] digest_0.6.29 svglite_2.0.0 rmarkdown_2.11 oce_1.5-0
[37] pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
[41] fastmap_1.1.0 rlang_1.0.2 readxl_1.3.1 rstudioapi_0.13
[45] jquerylib_0.1.4 generics_0.1.1 farver_2.1.0 jsonlite_1.7.3
[49] vroom_1.5.7 magrittr_2.0.1 Rcpp_1.0.8 munsell_0.5.0
[53] fansi_1.0.2 lifecycle_1.0.1 stringi_1.7.6 whisker_0.4
[57] yaml_2.2.1 MASS_7.3-55 grid_4.1.2 parallel_4.1.2
[61] promises_1.2.0.1 crayon_1.4.2 haven_2.4.3 hms_1.1.1
[65] seacarb_3.3.0 knitr_1.37 ps_1.6.0 pillar_1.6.4
[69] reprex_2.0.1 glue_1.6.0 evaluate_0.14 getPass_0.2-2
[73] data.table_1.14.2 modelr_0.1.8 vctrs_0.3.8 tzdb_0.2.0
[77] tweenr_1.0.2 httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0
[81] polyclip_1.10-0 assertthat_0.2.1 xfun_0.29 broom_0.7.11
[85] later_1.3.0 viridisLite_0.4.0 ellipsis_0.3.2 here_1.0.1