demo/create_figure_error_mean_dur_spec_alignment_length.R

In richelbilderbeek/wiritttea: WIRITTTE analysis

# Create 'figure_error_expected_mean_dur_spec_alignment_length'
library(wiritttea)
options(warn = 2) # Be strict
date <- "20170710"
nltt_stats_filename <- paste0("~/wirittte_data/nltt_stats_", date, ".csv")
parameters_filename <- paste0("~/GitHubs/wirittte_data/parameters_", date, ".csv")

args <- commandArgs(trailingOnly = TRUE)
if (length(args) > 0) nltt_stats_filename <- args[1]
if (length(args) > 1) parameters_filename <- args[2]

print(paste("nltt_stats_filename:", nltt_stats_filename))
print(paste("parameters_filename:", parameters_filename))

if (!file.exists(parameters_filename)) {
  stop("Please run analyse_parameters")
}

if (!file.exists(nltt_stats_filename)) {
  stop("Please run analyse_nltt_stats")
}

# Read parameters and nLTT stats
parameters <- wiritttea::read_collected_parameters(parameters_filename)
nltt_stats <- wiritttea::read_collected_nltt_stats(nltt_stats_filename, burn_in_fraction = 0.2)

print("Add mean duration of speciation to parameters")
parameters$mean_durspec <- PBD::pbd_mean_durspecs(
  eris = parameters$eri,
  scrs = parameters$scr,
  siris = parameters$siri
)

# Take the mean of the nLTT stats
`%>%` <- dplyr::`%>%`
nltt_stat_means <- nltt_stats %>% dplyr::group_by(filename, sti, ai, pi) %>%
       dplyr::summarise(mean = mean(nltt_stat), sd = sd(nltt_stat))
testit::assert(all(names(nltt_stat_means)
  == c("filename", "sti", "ai", "pi", "mean", "sd")))
head(nltt_stat_means, n = 10)
nrow(nltt_stat_means)


# Prepare parameters for merge
# parameters$filename <- row.names(parameters)
# parameters$filename <- as.factor(parameters$filename)

# Only select the columns we need
names(parameters)
parameters <- dplyr::select(parameters, c(filename, scr, mean_durspec, sequence_length))

head(nltt_stats)
nltt_stats <- dplyr::select(nltt_stats, c(filename, nltt_stat))

# Connect the mean nLTT stats and parameters
testit::assert("filename" %in% names(parameters))
testit::assert("filename" %in% names(nltt_stats))
testit::assert("filename" %in% names(nltt_stat_means))
df <- merge(x = parameters, y = nltt_stats, by = "filename", all = TRUE)
df_mean <- merge(x = parameters, y = nltt_stat_means, by = "filename", all = TRUE)

names(df)
head(df, n = 10)
names(df_mean)
head(df_mean, n = 10)

# Calculate mean BD error
scr_bd <- max(stats::na.omit(df$scr))
mean_bd_error_1000  <- mean(stats::na.omit(df[ df$scr == scr_bd & df$sequence_length == 1000 , ]$nltt_stat))
mean_bd_error_10000 <- mean(stats::na.omit(df[ df$scr == scr_bd & df$sequence_length == 10000, ]$nltt_stat))

print("Creating figure")

nltt_stat_cutoff <- 0.1

svg("~/figure_error_expected_mean_dur_spec_alignment_length.svg")
set.seed(42)
n_sampled <- 5000
n_data_points <- nrow(stats::na.omit(df))

options(warn = 1) # Allow points not to be plotted

ggplot2::ggplot(
  data = dplyr::sample_n(stats::na.omit(df), size = n_sampled), # Out of 7M
  ggplot2::aes(x = mean_durspec, y = nltt_stat, color = as.factor(sequence_length))
) + ggplot2::geom_jitter(width = 0.01, alpha = 0.2) +
  ggplot2::geom_smooth(method = "lm") +
  ggpmisc::stat_poly_eq(
    formula = y ~ x,
    eq.with.lhs = paste(latex2exp::TeX("$\\Delta_{nLTT}$"), "~`=`~"),
    eq.x.rhs = latex2exp::TeX(" \\bar{t_{ds}}"),
    ggplot2::aes(label = paste(..eq.label.., ..adj.rr.label.., sep = "~~~~")),
    parse = TRUE) +
  ggplot2::geom_smooth(method = "loess") +
  ggplot2::geom_hline(yintercept = mean_bd_error_1000, linetype = "dotted", color = scales::hue_pal()(2)[1]) +
  ggplot2::geom_hline(yintercept = mean_bd_error_10000, linetype = "dotted", color = scales::hue_pal()(2)[2]) +
  ggplot2::scale_y_continuous(limits = c(0, nltt_stat_cutoff)) + # Will have some outliers unplotted
  ggplot2::xlab(latex2exp::TeX(" t_\\bar{ds}} (million years)")) +
  ggplot2::ylab(latex2exp::TeX("$\\Delta_{nLTT}$")) +
  ggplot2::labs(
    title = "nLTT statistic\nfor different expected mean duration of speciation,\nfor different DNA alignment lengths",
    caption  = paste0("n = ", n_sampled, " / ", n_data_points, ", figure_error_expected_mean_dur_spec_alignment_length")
  ) +
  ggplot2::labs(color = latex2exp::TeX("$l_a$")) +
  ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))

options(warn = 2) # Be strict

dev.off()




svg("~/figure_error_expected_mean_dur_spec_mean_alignment_length.svg")

options(warn = 1) # Allow points to fall off plot range

ggplot2::ggplot(
  data = stats::na.omit(df_mean),
  ggplot2::aes(x = mean_durspec, y = mean, color = as.factor(sequence_length))
) + ggplot2::geom_point() +
  ggplot2::geom_smooth(method = "lm", size = 0.5) +
  ggpmisc::stat_poly_eq(
    formula = y ~ x,
    eq.with.lhs = paste(latex2exp::TeX("$\\bar{\\Delta_{nLTT}}$"), "~`=`~"),
    eq.x.rhs = latex2exp::TeX(" \\bar{t_{ds}}"),
    ggplot2::aes(label = paste(..eq.label.., ..adj.rr.label.., sep = "~~~~")),
    parse = TRUE) +
  ggplot2::scale_y_continuous(limits = c(0, nltt_stat_cutoff)) + # Will have some outliers unplotted
  ggplot2::geom_smooth(method = "loess", size = 0.5) +
  ggplot2::geom_hline(yintercept = mean_bd_error_1000, linetype = "dotted", color = scales::hue_pal()(2)[1]) +
  ggplot2::geom_hline(yintercept = mean_bd_error_10000, linetype = "dotted", color = scales::hue_pal()(2)[2]) +
  ggplot2::xlab(latex2exp::TeX(" t_\\bar{ds}} (million years)")) +
  ggplot2::ylab(latex2exp::TeX("$\\bar{\\Delta_{nLTT}}$")) +
  ggplot2::labs(
    title = "Mean nLTT statistic\nfor different expected mean duration of speciation,\nfor different DNA alignment lengths",
    caption  = "figure_error_expected_mean_dur_spec_mean_alignment_length"
  ) +
  ggplot2::labs(color = latex2exp::TeX("$l_a$")) +
  ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))

options(warn = 2) # Be strict

dev.off()