forked from Jpomz/detecting-spectra-differences
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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")
lambda_window <- angles %>%
mutate(Model = factor(name,
levels =
c("MLE",
"ELBn",
"NAS")),
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("#019AFF",
"#FF914A",
"#FF1984" )) +
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")
rel_data %>%
mutate(Model = factor(name,
levels =
c("MLE",
"ELBn",
"NAS"))) %>%
ggplot(aes(x = env_gradient,
y = estimate,
group = rep,
color = Model)) +
scale_color_manual(
values = c("#019AFF", "#FF914A", "#FF1984" )) +
stat_smooth(geom = "line",
method = "lm",
alpha = 0.15,
se = FALSE,
color = "black") +
geom_point(alpha = 0.5) +
facet_wrap(known_relationship~Model)+
# labs(title = sim_data$distribution,
# subtitle =
# paste0("m_range =(",
# sim_data$m_lower,
# ",",
# sim_data$m_upper,
# ") \nb = (",
# min(sim_data$known_b),
# ",",
# max(sim_data$known_b),
# ")")) +
theme_bw() +
theme(legend.position="none")+
NULL
ggsave(filename = "figures/vary_beta_plot.png",
units = "in",
height = 6,
width = 6)
rel_data %>%
calc_relationship_estimate(.) %>%
mutate(Model = factor(name,
levels =
c("MLE",
"ELBn",
"NAS"))) %>%
ggplot(aes(x = estimate,
y = Model,
fill = Model))+
stat_halfeye(.width = c(0.66, 0.95)) +
scale_fill_manual(
values = c("#019AFF",
"#FF914A",
"#FF1984" )) +
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)
est_lambda %>%
mutate(Model = factor(name,
levels =
c("MLE",
"ELBn",
"NAS"))) %>%
ggplot(
aes(x = estimate,
y = Model,
fill = Model)) +
stat_halfeye(.width = c(0.66, 0.95)) +
scale_fill_manual(
values = c("#019AFF", "#FF914A", "#FF1984" )) +
theme_bw() +
geom_vline(aes(xintercept = known_b),
linetype = "dashed") +
labs(
x = "Lambda estimate") +
facet_wrap(~known_b)
ggsave(filename = "figures/est_lambda_est_b_density_fixed_x.png",
units = "in",
height = 6,
width = 6)