knitr::opts_chunk$set(echo = FALSE) library(drake) library(dplyr) library(ggplot2) all_di <- read.csv(here::here("analysis", "reports", "all_di.csv"), stringsAsFactors = F) all_di_old <- read.csv(here::here("all_di.csv"), stringsAsFactors = F) fia_small_provisional <- filter(all_di_old, dat == "fia_small") all_di <- bind_rows(all_di, fia_small_provisional) all_di <- all_di %>% mutate(log_nparts = log(gmp:::as.double.bigz(nparts)), log_nsamples = log(nsamples), log150_nparts = log(gmp:::as.double.bigz(nparts), base = 150)) %>% mutate(prop_found = exp(log_nsamples - log_nparts)) %>% mutate(found_all = prop_found==1) %>% filter(s0 >= 2, n0 > s0) %>% mutate(dat = ifelse(substr(dat, 0, 3) == "fia", "fia", dat))
Here is where our communities fall in S and N space:
ggplot(filter(all_di, singletons == F), aes(x = log(s0), y = log(n0), color = dat)) + geom_point(alpha = .8) + theme_bw() + theme(legend.position = "top") + scale_color_viridis_d()
Here is how that translates into the size of the feasible set:
ggplot(filter(all_di, singletons == F), aes(x = log(s0), y = log(n0), color = log150_nparts)) + geom_point(alpha = .5) + theme_bw() + theme(legend.position = "top") + scale_color_viridis_c(option = "magma", end = .9, begin = .1) ggplot(filter(all_di, singletons == F), aes(x = log_nparts, y = ..count..)) + geom_density() + theme_bw() + geom_vline(xintercept = c(10, 15)) ggplot(filter(all_di, singletons == F), aes(x = log_nparts, y = ..count..)) + geom_density() + theme_bw() + geom_vline(xintercept = c(10, 15)) + facet_wrap(vars(dat), scales = "free")
ggplot(filter(all_di, s0 > 2, !singletons), aes(x = skew_percentile)) + geom_histogram() + theme_bw() + ggtitle("Skew - all sites") ggplot(filter(all_di, s0 > 2, !singletons), aes(x = skew_percentile)) + geom_histogram() + theme_bw() + facet_wrap(vars(dat), scales = "free_y") + ggtitle("Skew - all sites") ggplot(filter(all_di,!singletons), aes(x = simpson_percentile)) + geom_histogram() + theme_bw() + ggtitle("Simpson - all sites") ggplot(filter(all_di,!singletons), aes(x = simpson_percentile)) + geom_histogram() + theme_bw() + ggtitle("Simpson - all sites") + facet_wrap(vars(dat), scales = "free_y")
fia_max_s <- max(filter(all_di, !singletons, dat == "fia")$s0) fia_max_n <- max(filter(all_di,!singletons, dat == "fia")$n0) fia_sized <- filter(all_di, s0 <= fia_max_s, !singletons, n0 <= fia_max_n) %>% mutate(fia_yn = dat == "fia") %>% mutate(random_5th = as.integer(sample(2, size = n(), replace = T, prob = c(0.035, 0.965)))) ggplot(fia_sized, aes(x = log(s0), y= log(n0), color = dat)) + geom_point() + theme_bw() + scale_color_viridis_d() ggplot(fia_sized, aes(x = log(s0), y= log(n0), color = dat)) + geom_point() + theme_bw() + scale_color_viridis_d() + facet_wrap(vars(fia_yn), scales = "fixed") mean(fia_sized$dat == "fia") mean(fia_sized$random_5th == "2")
96.5% of the datasets with state variables comparable to the FIA ranges are FIA.
ggplot(filter(fia_sized, s0 > 2), aes(x = skew_percentile)) + geom_histogram() + theme_bw() + ggtitle("Skew - all sites") ggplot(filter(fia_sized, s0 > 2), aes(x = skew_percentile)) + geom_histogram() + theme_bw() + facet_wrap(vars(fia_yn), scales = "free_y") + ggtitle("Skew - FIA true/false") # # fia_sized <- fia_sized %>% # mutate(small_n = log(n0) <= 3.5) # # ggplot(filter(fia_sized, s0 > 2), aes(x = skew_percentile)) + # geom_histogram() + theme_bw() + # facet_grid(rows = vars(fia_yn), cols = vars(small_n), scales = "free_y") + # ggtitle("Skew - FIA true/false (rows), n <= 20 true/false (cols)") # # ggplot(filter(fia_sized, s0 > 2), aes(x = skew_percentile)) + geom_histogram() + theme_bw() + facet_wrap(vars(random_5th), scales = "free_y") + ggtitle("Skew - randomly pulling out 4%") ggplot(filter(fia_sized, s0 > 2), aes(x = simpson_percentile)) + geom_histogram() + theme_bw() + ggtitle("Simpson all sites") ggplot(filter(fia_sized, s0 > 2), aes(x = simpson_percentile)) + geom_histogram() + theme_bw() + facet_wrap(vars(fia_yn), scales = "free_y") + ggtitle("Simpson - FIA true/false") ggplot(filter(fia_sized, s0 > 2), aes(x = simpson_percentile)) + geom_histogram() + theme_bw() + facet_wrap(vars(random_5th), scales = "free_y") + ggtitle("Simpson - a random 4%")
ggplot(filter(fia_sized, s0 > 2), aes(x = log(s0), y= log(n0), color = skew_percentile)) + geom_point() + theme_bw() + scale_color_viridis_c() ggplot(fia_sized, aes(x = log(s0), y= log(n0), color = simpson_percentile)) + geom_point() + theme_bw() + scale_color_viridis_c()
Does skew behave differently if n0 <0 r exp(3.5)
? Just from looking at the map.
fia_sized <- fia_sized %>% mutate(small_n = log(n0) <= 3.5) fia_sized %>% group_by(small_n) %>% summarize(prop_fia = mean(fia_yn)) ggplot(filter(fia_sized, small_n, s0 > 2), aes(x = skew_percentile)) + geom_histogram() + theme_bw() + #facet_wrap(vars(fia_yn), scales = "free_y") + ggtitle("Small N") ggplot(filter(fia_sized, !small_n, s0 > 2), aes(x = skew_percentile)) + geom_histogram() + theme_bw() + #facet_wrap(vars(fia_yn), scales = "free_y") + ggtitle("NOT Small N") ggplot(filter(fia_sized, !small_n, s0 > 2), aes(x = skew_percentile)) + geom_histogram() + theme_bw() + facet_wrap(vars(fia_yn), scales = "free_y") + ggtitle("NOT Small N, FIA true/false") ggplot(filter(fia_sized, small_n, s0 > 2), aes(x = skew_percentile)) + geom_histogram() + theme_bw() + facet_wrap(vars(fia_yn), scales = "free_y") + ggtitle("Small N")
ggplot(filter(fia_sized, small_n, s0 > 2), aes(x = simpson_percentile)) + geom_histogram() + theme_bw() + #facet_wrap(vars(fia_yn), scales = "free_y") + ggtitle("Small N") ggplot(filter(fia_sized, !small_n, s0 > 2), aes(x = simpson_percentile)) + geom_histogram() + theme_bw() + #facet_wrap(vars(fia_yn), scales = "free_y") + ggtitle("NOT Small N") ggplot(filter(fia_sized, !small_n, s0 > 2), aes(x = simpson_percentile)) + geom_histogram() + theme_bw() + facet_wrap(vars(fia_yn), scales = "free_y") + ggtitle("NOT Small N, FIA true/false") ggplot(filter(fia_sized, small_n, s0 > 2), aes(x = simpson_percentile)) + geom_histogram() + theme_bw() + facet_wrap(vars(fia_yn), scales = "free_y") + ggtitle("Small N")
ggplot(filter(all_di, s0 > 2, !singletons, n0 >= 3.5), aes(x = skew_percentile)) + geom_histogram() + theme_bw() ggplot(filter(all_di, !singletons, n0 >= 3.5), aes(x = simpson_percentile)) + geom_histogram() + theme_bw()
ggplot(filter(all_di, !singletons, s0 > fia_max_s, n0 > fia_max_n), aes(x = skew_percentile)) + geom_histogram() + theme_bw() ggplot(filter(all_di, !singletons, s0 > fia_max_s, n0 > fia_max_n), aes(x = skew_percentile)) + geom_histogram() + theme_bw() + facet_wrap(vars(dat), scales = "free_y") ggplot(filter(all_di, !singletons, s0 > fia_max_s, n0 > fia_max_n), aes(x = simpson_percentile)) + geom_histogram() + theme_bw() ggplot(filter(all_di, !singletons, s0 > fia_max_s, n0 > fia_max_n), aes(x = simpson_percentile)) + geom_histogram() + theme_bw() + facet_wrap(vars(dat), scales = "free_y")
ggplot(filter(fia_sized, dat %in% c("fia", "portal_rodents")), aes(x = log(s0), y = log(n0), color = dat)) + geom_point(alpha = .3) + geom_point(data = filter(all_di, dat == "portal_rodents")) + theme_bw() ggplot(filter(fia_sized, dat %in% c("fia", "portal_rodents"), s0 > 2), aes(x = skew_percentile)) + geom_histogram() + facet_wrap(vars(dat, small_n), scales = "free_y") + theme_bw()
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