Column inventories
Jens Daniel Müller
03 November, 2021
Last updated: 2021-11-03
Checks: 7 0
Knit directory: emlr_obs_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | e534f51 | jens-daniel-mueller | 2021-11-02 | Build site. |
| html | 57cfc36 | jens-daniel-mueller | 2021-11-01 | Build site. |
| html | 4331a22 | jens-daniel-mueller | 2021-10-29 | Build site. |
| html | ae5ae64 | jens-daniel-mueller | 2021-10-26 | Build site. |
| html | 581baa0 | jens-daniel-mueller | 2021-10-07 | Build site. |
| html | a7af62f | jens-daniel-mueller | 2021-10-06 | Build site. |
| html | f9b4f93 | jens-daniel-mueller | 2021-10-05 | Build site. |
| html | 960d158 | jens-daniel-mueller | 2021-09-29 | Build site. |
| html | 0573621 | jens-daniel-mueller | 2021-09-29 | Build site. |
| html | 73154c7 | jens-daniel-mueller | 2021-09-22 | Build site. |
| Rmd | 8f1a837 | jens-daniel-mueller | 2021-09-22 | new vif analysis added |
| html | ec4f702 | jens-daniel-mueller | 2021-09-22 | Build site. |
| Rmd | 1abd051 | jens-daniel-mueller | 2021-09-22 | rebuildt with vif results |
| Rmd | f474699 | jens-daniel-mueller | 2021-08-17 | ensemble filtering |
| html | bd9ce6b | jens-daniel-mueller | 2021-08-02 | Build site. |
| Rmd | 29564fb | jens-daniel-mueller | 2021-08-02 | rebuildt with vif analysis |
1 Read files
# identify required version IDs
Version_IDs_1 <- list.files(
path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = "v_15")
Version_IDs_2 <- list.files(
path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = "v_25")
Version_IDs <- c(Version_IDs_1, Version_IDs_2)
rm(Version_IDs_1, Version_IDs_2)
# 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_inv <-
read_csv(paste(path_version_data,
"dcant_inv.csv",
sep = ""))
dcant_inv_mod_truth <-
read_csv(paste(path_version_data,
"dcant_inv_mod_truth.csv",
sep = "")) %>%
filter(method == "total") %>%
select(-method)
dcant_inv_bias <-
read_csv(paste(path_version_data,
"dcant_inv_bias.csv",
sep = ""))
dcant_inv <- bind_rows(dcant_inv,
dcant_inv_mod_truth)
dcant_inv <- dcant_inv %>%
mutate(Version_ID = i_Version_IDs)
dcant_inv_bias <- dcant_inv_bias %>%
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,
vif_max = params_local$vif_max
)
tref <- read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
params_local <- params_local %>%
mutate(median_year_1 = sort(tref$median_year)[1],
median_year_2 = sort(tref$median_year)[2],
duration = median_year_2 - median_year_1,
period = paste(median_year_1, "-", median_year_2))
if (exists("dcant_inv_all")) {
dcant_inv_all <- bind_rows(dcant_inv_all, dcant_inv)
}
if (!exists("dcant_inv_all")) {
dcant_inv_all <- dcant_inv
}
if (exists("dcant_inv_bias_all")) {
dcant_inv_bias_all <- bind_rows(dcant_inv_bias_all, dcant_inv_bias)
}
if (!exists("dcant_inv_bias_all")) {
dcant_inv_bias_all <- dcant_inv_bias
}
if (exists("params_local_all")) {
params_local_all <- bind_rows(params_local_all, params_local)
}
if (!exists("params_local_all")) {
params_local_all <- params_local
}
}[1] "v_1501"
[1] "v_1502"
[1] "v_1503"
[1] "v_1504"
[1] "v_1505"
[1] "v_2501"
[1] "v_2502"
[1] "v_2503"
[1] "v_2504"
[1] "v_2505"rm(dcant_inv, dcant_inv_bias, dcant_inv_mod_truth,
params_local, tref)
params_local_all <- params_local_all %>%
mutate(vif_max = fct_inseq(as.factor(as.character(vif_max))))
dcant_inv_all <- full_join(dcant_inv_all,
params_local_all)
dcant_inv_bias_all <- full_join(dcant_inv_bias_all,
params_local_all)dcant_inv_all <- dcant_inv_all %>%
filter(inv_depth == params_global$inventory_depth_standard)2 Individual cases
2.1 Absoulte values
dcant_inv_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(period) %>%
group_split() %>%
# head(1) %>%
map(
~ p_map_cant_inv(df = .x,
var = "dcant",
subtitle_text = paste("period:",
unique(.x$period))) +
facet_grid(vif_max ~ data_source)
)[[1]]
[[2]]
2.2 Biases
dcant_inv_bias_all %>%
p_map_cant_inv(var = "dcant_bias",
col = "bias",
subtitle_text = "data_source: mod - mod_truth") +
facet_grid(vif_max ~ period)
dcant_inv_bias_all %>%
ggplot(aes(dcant_bias)) +
scale_color_brewer(palette = "Set1") +
geom_density() +
facet_grid(vif_max ~ period) +
coord_cartesian(xlim = c(-10,10))
dcant_inv_ensemble_member <- dcant_inv_bias_all %>%
group_by(period, vif_max) %>%
summarise(dcant_sd = sd(dcant_bias)) %>%
ungroup()
dcant_inv_ensemble_member %>%
ggplot(aes(vif_max, dcant_sd, col = period)) +
geom_point()
3 Ensemble
dcant_inv_ensemble <- dcant_inv_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(lat, lon, data_source, period) %>%
summarise(dcant_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_range = max(dcant)- min(dcant)) %>%
ungroup()3.1 Mean
p_map_cant_inv(df = dcant_inv_ensemble,
var = "dcant_mean",
subtitle_text = paste("Ensemble means")) +
facet_grid(period ~ data_source)
3.2 Mean bias
dcant_inv_ensemble_bias <- full_join(
dcant_inv_ensemble %>%
filter(data_source == "mod") %>%
select(lat, lon, period, dcant_mean, dcant_sd),
dcant_inv_all %>%
filter(data_source == "mod_truth",
Version_ID %in% c("v_1501", "v_2501")) %>%
select(lat, lon, period, dcant)
)
dcant_inv_ensemble_bias <- dcant_inv_ensemble_bias %>%
mutate(dcant_mean_bias = dcant_mean - dcant)
dcant_inv_ensemble_bias %>%
p_map_cant_inv(var = "dcant_mean_bias",
col = "bias",
subtitle_text = "Ensemble mean - mod_truth") +
facet_grid(. ~ period)
3.3 Standard deviation
p_map_cant_inv(
df = dcant_inv_ensemble,
var = "dcant_sd",
breaks = c(seq(0, 4, 0.4), Inf),
subtitle_text = paste("Ensemble SD")
) +
facet_grid(period ~ data_source)
3.4 SD vs bias
dcant_inv_ensemble_bias %>%
ggplot(aes(dcant_mean_bias, dcant_sd)) +
geom_bin2d() +
scale_fill_viridis_c() +
facet_grid(. ~ period)
dcant_inv_ensemble_bias %>%
select(dcant_mean, dcant_mean_bias, period) %>%
pivot_longer(dcant_mean:dcant_mean_bias,
names_to = "estimate",
values_to = "value") %>%
ggplot(aes(value, col=estimate, linetype = period)) +
scale_color_brewer(palette = "Set1") +
geom_density()
dcant_inv_ensemble %>%
ggplot(aes(dcant_sd)) +
geom_histogram() +
facet_grid(data_source ~ period) +
coord_cartesian(ylim = c(0,50))
3.5 Range
p_map_cant_inv(
df = dcant_inv_ensemble,
var = "dcant_range",
breaks = c(seq(0,8,0.8), Inf),
subtitle_text = paste("Ensemble range")
) +
facet_grid(period ~ data_source)
4 Cases vs ensemble
4.1 Offset from mean
dcant_inv_all <- full_join(dcant_inv_all,
dcant_inv_ensemble)
dcant_inv_all <- dcant_inv_all %>%
mutate(dcant_offset = dcant - dcant_mean)
dcant_inv_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(period) %>%
group_split() %>%
# head(1) %>%
map(
~ p_map_cant_inv(df = .x,
var = "dcant_offset",
col = "bias",
subtitle_text = paste("period:",
unique(.x$period))) +
facet_grid(vif_max ~ data_source)
)[[1]]
[[2]]
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] ggforce_0.3.3 metR_0.9.0 scico_1.2.0 patchwork_1.1.1
[5] collapse_1.5.0 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.5
[9] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.3
[13] ggplot2_3.3.5 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 sass_0.4.0 viridisLite_0.3.0
[4] jsonlite_1.7.1 modelr_0.1.8 bslib_0.2.5.1
[7] assertthat_0.2.1 highr_0.8 blob_1.2.1
[10] cellranger_1.1.0 yaml_2.2.1 pillar_1.6.2
[13] backports_1.1.10 lattice_0.20-41 glue_1.4.2
[16] RcppEigen_0.3.3.7.0 digest_0.6.27 RColorBrewer_1.1-2
[19] promises_1.1.1 polyclip_1.10-0 checkmate_2.0.0
[22] rvest_0.3.6 colorspace_2.0-2 htmltools_0.5.1.1
[25] httpuv_1.5.4 Matrix_1.2-18 pkgconfig_2.0.3
[28] broom_0.7.9 haven_2.3.1 scales_1.1.1
[31] tweenr_1.0.2 whisker_0.4 later_1.2.0
[34] git2r_0.27.1 farver_2.0.3 generics_0.1.0
[37] ellipsis_0.3.2 withr_2.3.0 cli_3.0.1
[40] magrittr_1.5 crayon_1.3.4 readxl_1.3.1
[43] evaluate_0.14 fs_1.5.0 fansi_0.4.1
[46] MASS_7.3-53 xml2_1.3.2 RcppArmadillo_0.10.1.2.0
[49] tools_4.0.3 data.table_1.14.0 hms_0.5.3
[52] lifecycle_1.0.0 munsell_0.5.0 reprex_0.3.0
[55] compiler_4.0.3 jquerylib_0.1.4 rlang_0.4.11
[58] grid_4.0.3 rstudioapi_0.13 labeling_0.4.2
[61] rmarkdown_2.10 gtable_0.3.0 DBI_1.1.0
[64] R6_2.5.0 lubridate_1.7.9 knitr_1.33
[67] utf8_1.1.4 rprojroot_2.0.2 stringi_1.5.3
[70] parallel_4.0.3 Rcpp_1.0.5 vctrs_0.3.8
[73] dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.25