In bsaul/elktoeChemistry: Data and code for Elktoe Chemistry project

In this document, we examine the proportion of observations censored (below the lower limit of detection) within each valve's layer. Elements with a high degree of censoring across all layers may be removed from further analysis.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  echo    = FALSE,
  message = FALSE
)

library(elktoeChemistry)
library(dplyr)
library(ggplot2)
analysis_data  <- readRDS(params$inputs$analysis)

Use the analysis_data function to observations for

all signal prefiltering methods (signal = "all")
all specimens (contrast = "all")
all available layers
group observations by layer across transects within a specimen (group_by_valve = TRUE)
require at least 5 observations within a layer (.min_n_obs)

dt <-
  analysis_data(
    contrast = "all",
    test_data_FUN = function(data) data, # just return the data unmodified
    elements  = "all", 
    signals   = "all",
    group_by_valve = TRUE,
    transect_opts  = list(
      .layers    = c("ipx", "ncr", "psm", "pio", "opx"),
      .min_n_obs = 5L
    )
  )

# Identify nacre wiithin the *yountest* annuli as "ncr_new"
# and nacre in older annuli as "ncr_old"
layer_modifier <- function(layer, annuli){
  case_when(
      layer == "ncr" & annuli == "A" ~ "ncr_new",
      layer == "ncr" & annuli != "A" ~ "ncr_old",
      TRUE ~ layer
  ) %>%
  factor(
    levels = c("ipx", "ncr_new", "ncr_old", "psm", "pio", "opx"),
    ordered =  TRUE)
}

Use this package's summarize_specimens function to compute summary statistics by layer within each specimen.

specimen_summaries_by_signal <- 
  dt %>%
  group_nest(signal) %>%
  mutate(
    data = purrr::map(
      .x = data, 
      .f = ~summarize_specimens(.x, modify_layer = layer_modifier))
  ) %>%
  tidyr::unnest(cols = c("data"))

Examine the average pc (proportion censored) for each element by layer.

avg_pc_by_signal <- 
  specimen_summaries_by_signal %>%
  group_by(species, signal, element, layer) %>%
  summarize(
    pc = mean(pc)
  )

ggplot(
  data = avg_pc_by_signal,
  aes(x = signal, y = pc)
) +
    geom_point(size = 0.5, shape = 1) +
  facet_grid(element ~ layer) +
  theme(
    strip.text.y = element_text(color = "blue", angle = 0)
  )

GAM no good.

specimen_summaries_by_signal %>%
  filter(signal == "base", species == "Arav") %>%
ggplot(
  aes(x = site, y = pc)
) + 
  geom_point(size = 0.5, shape = 1) +
  facet_grid(element ~ layer) +
  theme(
    strip.text.y = element_text(color = "blue", angle = 0),
    axis.text.x = element_text(angle = 90)
  )