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2b_integrated_plots.R
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2b_integrated_plots.R
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#2b_plots for integrated sims
# Preliminaries -----------------------------------------------------------
library(tidyverse)
library(sizeSpectra) #bounded power law and MLE functions
library(tidybayes) # for nicer looking plots
source("custom_functions.R")
library(cowplot)
# load simulation data ----------------------------------------------------
steep_lambda <- readRDS("data_sim/steep_lambda_dat.rds")
est_lambda <- readRDS("data_sim/est_lambda_dat.rds")
med_lambda <- readRDS("data_sim/med_lambda_dat.rds")
rel_025 <- readRDS("data_sim/relationship_025_dat.rds")
rel_05 <- readRDS("data_sim/relationship_05_dat.rds")
rel_0 <- readRDS("data_sim/relationship_0_dat.rds")
shallow_lambda <- readRDS("data_sim/shallow_lambda_dat.rds")
angle_A <- steep_lambda %>%
calc_relationship_estimate(.)
angle_B <- med_lambda %>%
calc_relationship_estimate(.)
angle_C <- shallow_lambda %>%
calc_relationship_estimate(.)
angles <- bind_rows(steep = angle_A,
medium = angle_B,
shallow = angle_C,
.id = "id")
saveRDS(angles, file = "data_sim/angles.rds")
# figure 3 ####
(lambda_window <- angles %>%
mutate(
Model = case_when(name == "NAS" ~ "L2n", .default = name),
Model = factor(Model,
levels =
c("MLE",
"ELBn",
"L2n")),
sim = factor(id,
levels = c("steep",
"medium",
"shallow"))) %>%
ggplot(aes(x = estimate,
y = Model,
fill = Model))+
stat_halfeye(.width = c(0.66, 0.95)) +
scale_fill_manual(
values = c("#FF1984",
"#FF914A",
"#019AFF"),
breaks = c("L2n",
"ELBn",
"MLE")) +
theme_bw() +
geom_vline(
aes(xintercept = known_relationship),
linetype = "dashed") +
labs(x = "Relationship estimate") +
facet_wrap(sim~.,
ncol = 1) +
NULL)
ggsave(plot = lambda_window,
filename = "figures/lambda_angle_plot.png",
units = "in",
height = 6,
width = 6)
rel_data <- bind_rows(beta_0 = rel_0,
beta_025 = rel_025,
beta_05 = rel_05,
.id = "id")
saveRDS(rel_data, file = "data_sim/rel_data.rds")
# figure 4 ####
###### Flip facet-wrap rows, top = 0, bottom = -0.5
ab_data <- data.frame(
slope = c(0, -0.25, -0.5),
intercept = -1.5,
known_relationship = c(0, -0.25, -0.5)
)
ab_data <- ab_data %>%
expand_grid(method = c("MLE", "ELBn", "L2N"))
rel_data %>%
filter(rep<=100) %>%
mutate(
Model = case_when(name == "NAS" ~ "L2n", .default = name),
Model = factor(Model,
levels =
c("L2n",
"ELBn",
"MLE"
)),
# set levels of known relaitonship for facet_wrap() plotting order below
known_relationship = factor(known_relationship,
levels = c("0", "-0.25", "-0.5"))) %>%
filter(rep <= 500) %>%
ggplot(aes(x = env_gradient,
y = estimate,
group = rep,
color = Model)) +
scale_color_manual(
values = c("#FF1984", "#FF914A", "#019AFF")) +
stat_smooth(geom = "line",
method = "lm",
alpha = 0.15,
se = FALSE,
color = "black") +
geom_point(alpha = 0.1) +
facet_wrap(known_relationship~Model)+
theme_bw() +
theme(legend.position="none") +
labs(x = "Hypothetical environemntal gradient") +
geom_abline(data = ab_data,
aes(intercept = intercept, slope = known_relationship),
color = "red",
linetype = "dashed",
linewidth = 1.25,
alpha = 0.75)+
NULL
ggsave(filename = "figures/vary_beta_plot.png",
units = "in",
height = 6,
width = 6)
rel_data_summary = rel_data %>% calc_relationship_estimate(.)
saveRDS(rel_data_summary, file = "data_sim/rel_data_summary.rds")
# Fig 5 ####
rel_data_summary %>%
mutate(
Model = case_when(name == "NAS" ~ "L2n", .default = name),
Model = factor(Model,
levels =
c("MLE",
"ELBn",
"L2n"))) %>%
ggplot(aes(x = estimate,
y = Model,
fill = Model))+
stat_halfeye(.width = c(0.66, 0.95)) +
scale_fill_manual(
values = c("#FF1984",
"#FF914A",
"#019AFF"),
breaks = c("L2n",
"ELBn",
"MLE")) +
theme_bw() +
geom_vline(
aes(xintercept = known_relationship),
linetype = "dashed") +
labs(x = "Relationship estimate") +
facet_wrap(known_relationship~.,
ncol = 1) +
NULL
ggsave(filename = "figures/vary_beta_density_plot.png",
units = "in",
height = 6,
width = 6)
# Figure 2 ####
est_lambda %>%
mutate(
Model = case_when(name == "NAS" ~ "L2n", .default = name),
Model = factor(Model,
levels =
c("MLE",
"ELBn",
"L2n"))) %>%
ggplot(
aes(x = estimate,
y = Model,
fill = Model)) +
stat_halfeye(.width = c(0.66, 0.95)) +
scale_fill_manual(
values = c("#FF1984",
"#FF914A",
"#019AFF"),
breaks = c("L2n",
"ELBn",
"MLE")) +
theme_bw() +
geom_vline(aes(xintercept = known_b),
linetype = "dashed") +
labs(
x = "Lambda estimate") +
facet_wrap(~known_b, scales = "free") +
scale_x_continuous(labels = scales::label_number(accuracy = 0.1)) +
scale_y_discrete(breaks=c("MLE",
"ELBn",
"NAS"),
labels= NULL)
ggsave(filename = "figures/est_lambda_est_b_density.png",
units = "in",
height = 6,
width = 6)
# top panel of figure 2 ####
# add as an SI figure
est_lambda %>%
mutate(
Model = case_when(name == "NAS" ~ "L2n", .default = name),
Model = factor(Model,
levels =
c("MLE",
"ELBn",
"L2n"))) %>%
filter(known_b == -2.5 |
known_b == -2.25 |
known_b == -2 ) %>%
ggplot(
aes(x = estimate,
y = Model,
fill = Model)) +
stat_halfeye(.width = c(0.66, 0.95)) +
scale_fill_manual(
values = c("#FF1984",
"#FF914A",
"#019AFF"),
breaks = c("L2n",
"ELBn",
"MLE")) +
theme_bw() +
geom_vline(aes(xintercept = known_b),
linetype = "dashed") +
labs(
x = "Lambda estimate") +
facet_wrap(~known_b, scales = "free") +
scale_x_continuous(labels = scales::label_number(accuracy = 0.1)) +
scale_y_discrete(breaks=c("MLE",
"ELBn",
"NAS"),
labels= NULL)
ggsave(filename = "figures/SI_fig2_top_panel.png",
units = "in",
height = 6,
width = 6)
# replicates for SI ####
angles2 <- angles %>%
mutate(
Model = case_when(name == "NAS" ~ "L2n", .default = name),
Model = factor(Model,
levels =
c("MLE",
"ELBn",
"L2n")),
sim = factor(id,
levels = c("steep",
"medium",
"shallow"))) %>%
filter(known_relationship == -0.5)
# subsample pipeline
angles2 %>%
group_by(sim) %>%
filter(rep <= 10) %>%
mutate(rep_n = 10)
angles_list <- list()
rep_n <- c(10, 50, 100, 200, 250, 500, 750, 1000)
for (i in 1:length(rep_n)){
j = rep_n[i]
out <- angles2 %>%
group_by(sim) %>%
filter(rep <= j) %>%
mutate(rep_n = j)
angles_list[[i]] <- out
}
angles_list %>%
bind_rows() %>%
ggplot(aes(x = rep_n,
y = estimate,
color = Model))+
stat_pointinterval(position = "dodge") +
scale_color_manual(
values = c("#FF1984",
"#FF914A",
"#019AFF"),
breaks = c("L2n",
"ELBn",
"MLE")) +
theme_bw() +
geom_hline(
aes(yintercept = known_relationship),
linetype = "dashed") +
labs(x = "Number of replicates") +
facet_wrap(sim~.,
ncol = 1,
scales = "free_y") +
NULL
ggsave(filename = "figures/SI_vary_rep_n.png",
units = "in",
height = 6,
width = 6)