---
title: "Linakis et al. (2020): High Throughput Inhalation Model"
author: "Matt Linakis"
date: "January 30, 2020"
output: rmarkdown::html_vignette
vignette: >
 %\VignetteIndexEntry{g) Linakis (2020): High Throughput Inhalation Model}
 %\VignetteEngine{knitr::rmarkdown}
 %\VignetteEncoding{UTF-8}
---
*Please send questions to mlinak01@gmail.com*

from "Development and evaluation of a high throughput inhalation model for 
organic chemicals"

Matthew W. Linakis, Risa R. Sayre, Robert G. Pearce, Mark A. Sfeir, 
Nisha S. Sipes, Heather A. Pangburn, Jeffery M. Gearhart, and John F. Wambaugh 

Journal of Exposure Science & Environmental Epidemiology volume 30, 
pages 866–877 (2020)

https://doi.org/10.1038/s41370-020-0238-y

## Abstract

Currently it is difficult to prospectively estimate human toxicokinetics 
(particularly for novel chemicals) in a high-throughput manner. The R software 
package httk has been developed, in part, to address this deficiency, and the 
aim of this investigation was to develop a generalized inhalation model for httk. 
The structure of the inhalation model was developed from two previously published 
physiologically-based models from Jongeneelen et al. (2011) and Clewell et al. 
(2001) while calculated physicochemical data was obtained from EPA's CompTox 
Chemicals Dashboard. In total, 142 exposure scenarios across 41 volatile organic 
chemicals were modeled and compared to published data. The slope of the regression 
line of best fit between log-transformed simulated and observed combined measured 
plasma and blood concentrations was 0.59 with an r2= 0.54 and a Root Mean Square 
Error (RMSE) of direct comparison between the log-transformed simulated and 
observed values of 0.87. Approximately 3.6% (n = 73) of the data points analyzed 
were > 2 orders of magnitude different than expected. The volatile organic 
chemicals examined in this investigation represent small, generally lipophilic 
molecules. Ultimately this paper details a generalized inhalation component that 
integrates with the httk physiologically-based toxicokinetic model to provide 
high-throughput estimates of inhalation chemical exposures.

## HTTK Version

This vignette was created with **httk** v2.0.0. Although we attempt to maintain 
backward compatibility, if you encounter issues with the latest release of 
**httk**
and cannot easily address the changes, historical versions of httk are 
available from: https://cran.r-project.org/src/contrib/Archive/httk/

## Prepare for session
R package **knitr** generates html and PDF documents from this RMarkdown file,
Each bit of code that follows is known as a "chunk". We start by telling 
**knitr** how we want our chunks to look.
```{r configure_knitr, eval = TRUE}
knitr::opts_chunk$set(echo = TRUE, fig.width=5, fig.height=4)
```

### Clear the memory
It is a bad idea to let variables and other information from previous R
sessions float around, so we first remove everything in the R memory.
```{r clear_memory, eval = TRUE}
rm(list=ls()) 
```

### eval = execute.vignette
If you are using the RMarkdown version of this vignette (extension, .RMD) you
will be able to see that several chunks of code in this vignette
have the statement "eval = execute.vignette". The next chunk of code, by default,
sets execute.vignette = FALSE.  This means that the code is included (and necessary) but was
not run when the vignette was built. We do this because some steps require 
extensive computing time and the checks on CRAN limit how long we can spend
building the package. If you want this vignette to work, you must run all code,
either by cutting and pasting it into R. Or, if viewing the .RMD file, you can
either change execute.vignette to TRUE or press "play" (the green arrow) on
each chunk in RStudio.
```{r runchunks, eval = TRUE}
# Set whether or not the following chunks will be executed (run):
execute.vignette <- FALSE
```

### Load the relevant libraries
We use the command 'library()' to load various R packages for our analysis.
If you get the message "Error in library(X) : there is no package called 'X'"
then you will need to install that package: 

From the R command prompt:

install.packages("X")

Or, if using RStudio, look for 'Install Packages' under 'Tools' tab.
```{r setup, eval = execute.vignette}
knitr::opts_chunk$set(echo = TRUE, fig.width=5, fig.height=4)
library(httk)
library(ggplot2)
library(gridExtra)
library(cowplot)
library(ggrepel)
library(dplyr)
library(stringr)
library(forcats)
library(smatr)
```
### Get metabolism and concentration data 
```{r data, eval = execute.vignette}
met_data <- metabolism_data_Linakis2020
conc_data <- concentration_data_Linakis2020
#conc_data <- subset(concentration_data_Linakis2020, !(SAMPLING_MATRIX %in%
#                      c("EEB","MEB","EB")))
#conc_data <- concentration_data_Linakis2020
#  subset(concentration_data_Linakis2020, !(SOURCE_CVT %in% c(
#  "11453305")))
conc_data[,"DOSE_U"] <- ifelse(conc_data[,"DOSE_U"] == "ppm",yes = "ppmv",conc_data[,"DOSE_U"]) 
conc_data[,"ORIG_CONC_U"] <- ifelse(conc_data[,"ORIG_CONC_U"] == "ppm",yes = "ppmv",conc_data[,"ORIG_CONC_U"]) 
# Not sure what to do with percent:
conc_data <- subset(conc_data,toupper(ORIG_CONC_U) != "PERCENT")
# Rename this column:
colnames(conc_data)[colnames(conc_data)=="ORIG_CONC_U"] <- "CONC_U"
conc_data$ORIGINAL_CONC_U <- conc_data$CONC_U
conc_data$ORIGINAL_CONC <- conc_data$CONCENTRATION


```

Sets the units based on the sampling matrix (gas/liquid):
  BL : blood
  EEB : end-exhaled breath
  MEB : mixed exhaled breath
  VBL : venous blood
  ABL : arterial blood
  EB : unspecified exhaled breath sample (assumed to be EEB)
  PL: plasma
  +W with work/exercise
    
Normalize units for gaseous samples to ppmv:
```{r convert_to_ppmweight, eval = execute.vignette}
gas.media <- c("EB","MEB","EEB","EB (+W)")
gas.units <- unique(subset(conc_data,
  SAMPLING_MATRIX %in% gas.media)$CONC_U)
target.unit <- "ppmv"
for (this.unit in gas.units)
  if (this.unit != target.unit)
  {
    these.chems <- unique(subset(conc_data,
      SAMPLING_MATRIX %in% gas.media &
      CONC_U==this.unit)$DTXSID)    
    for (this.chem in these.chems)  
    {
      this.factor <- convert_units(
        input.units=this.unit, output.units=target.unit, dtxsid=this.chem)
      print(paste("Use",this.factor,"to convert",this.unit,"to",target.unit))
      
      # Scale the observation    
      conc_data[conc_data$DTXSID==this.chem &
                  conc_data$SAMPLING_MATRIX %in% gas.media &
                  conc_data$CONC_U==this.unit,"CONCENTRATION"] <-
        this.factor * conc_data[
          conc_data$DTXSID==this.chem &
            conc_data$SAMPLING_MATRIX %in% gas.media &
            conc_data$CONC_U==this.unit,"CONCENTRATION"]
      # Change the reported unit
      conc_data[conc_data$DTXSID==this.chem &
                  conc_data$SAMPLING_MATRIX %in% gas.media &
                  conc_data$CONC_U==this.unit,"CONC_U"] <-
        target.unit

    }
  }
```

Normalize the units for tissue samples to uM:
```{r normalize_other_units, eval = execute.vignette}
tissue.media <- c("VBL","BL","ABL","PL","BL (+W)")
tissue.units <- unique(subset(conc_data,
  SAMPLING_MATRIX %in% tissue.media)$CONC_U)
target.unit <- "uM"
for (this.unit in tissue.units)
  if (this.unit != target.unit)
  {
    these.chems <- unique(subset(conc_data,
      SAMPLING_MATRIX %in% tissue.media &
      CONC_U==this.unit)$DTXSID)    
    for (this.chem in these.chems)  
    {
      this.factor <- convert_units(
        input.units=this.unit, output.units=target.unit, dtxsid=this.chem)
      print(paste("Use",this.factor,"to convert",this.unit,"to",target.unit))
      
      # Scale the observation    
      conc_data[conc_data$DTXSID==this.chem &
                  conc_data$SAMPLING_MATRIX %in% tissue.media &
                  conc_data$CONC_U==this.unit,"CONCENTRATION"] <-
        this.factor * conc_data[
          conc_data$DTXSID==this.chem &
            conc_data$SAMPLING_MATRIX %in% tissue.media &
            conc_data$CONC_U==this.unit,"CONCENTRATION"]
      # Change the reported unit
      conc_data[conc_data$DTXSID==this.chem &
                  conc_data$SAMPLING_MATRIX %in% tissue.media &
                  conc_data$CONC_U==this.unit,"CONC_U"] <-
        target.unit

    }
  }
```

## ANALYSIS

Identify chemicals currently in our metabolism data that we don't have good
concentration/time data for and remove them from our training dataset

### Data summary for chemical properties
```{r summary, eval = execute.vignette}
# Small molecule chemicals
summary(met_data$AVERAGE_MASS)
# Generally more lipophilic chemicals
summary(met_data$OCTANOL_WATER_PARTITION_LOGP_OPERA_PRED)
# Unsurprisingly then, the chemicals are generally less water-soluble
summary(met_data$WATER_SOLUBILITY_MOL.L_OPERA_PRED)
# ~60% of samples in humans
table(conc_data$CONC_SPECIES)/nrow(conc_data)*100
# ~72% of samples are from blood
table(conc_data$SAMPLING_MATRIX)/nrow(conc_data)*100
```

### Exposure scenarios
```{r scenarios, eval = execute.vignette}
# Create a dataframe with 1 row for each unique external exposure scenario
unique_scenarios <- conc_data[with(conc_data,                             
  order(PREFERRED_NAME,
        CONC_SPECIES,
        SAMPLING_MATRIX,
        as.numeric(as.character(DOSE)),EXP_LENGTH,-TIME)),] %>%
  distinct(DTXSID,DOSE,DOSE_U,EXP_LENGTH,CONC_SPECIES,SAMPLING_MATRIX, .keep_all = TRUE)
```

### Observations and Predictions
Create a list of dataframes of observed and predicted concentrations
for each unique external exposure scenario (corresponding to 142 studies)
```{r runmodel, eval = execute.vignette}
simlist <- list()
obslist <- list()
for(i in 1:nrow(unique_scenarios)){
#for(i in 50){
#  tryCatch({
    relconc <- subset(conc_data,conc_data$DTXSID == unique_scenarios$DTXSID[i] & 
      conc_data$DOSE == unique_scenarios$DOSE[i] & 
      conc_data$EXP_LENGTH == unique_scenarios$EXP_LENGTH[i] & 
      conc_data$CONC_SPECIES == unique_scenarios$CONC_SPECIES[i] & 
      conc_data$SAMPLING_MATRIX == unique_scenarios$SAMPLING_MATRIX[i])
    obslist[[i]] <- relconc
    solve <- suppressWarnings(as.data.frame(solve_gas_pbtk(
        chem.cas = unique_scenarios$CASRN[i], 
        days = (unique_scenarios$TIME[i]+unique_scenarios$EXP_LENGTH[i]), 
# Make sure we get conc's at the observed times:
        times=unique(c(0,signif(obslist[[i]]$TIME,4))), 
        tsteps = 500,
        input.units = unique_scenarios$DOSE_U[i],
        exp.conc = as.numeric(unique_scenarios$DOSE[i]), # SED (06-22-2021) think this should input ppmv
        # exp.conc = ((as.numeric(unique_scenarios$DOSE[i])*1e20*1000)/PPMVCONVERT*1000)/1e20,
        exp.duration = unique_scenarios$EXP_LENGTH[i]*24, 
        period = (unique_scenarios$TIME[i]+unique_scenarios$EXP_LENGTH[i])*24, 
        species = as.character(unique_scenarios$CONC_SPECIES[i]),
        monitor.vars = c(
          "Cven", 
          "Clung", 
          "Cart", 
          "Cplasma", 
          "Calvppmv", 
          "Cendexhppmv", 
          "Cmixexhppmv", 
          "Calv", 
          "Cendexh", 
          "Cmixexh", 
          "Cmuc", 
          "AUC"),
        vmax.km = FALSE,
        suppress.messages = TRUE)))

    this.Rb2p <- suppressWarnings(available_rblood2plasma(
      chem.cas=unique_scenarios$CASRN[i],
      species=as.character(unique_scenarios$CONC_SPECIES[i])))
    solve$Rb2p <- this.Rb2p
    
    # Sets the output appropriate for the sampling matrix
    # BL : blood
    # EEB : end-exhaled breath
    # MEB : mixed exhaled breath
    # VBL : venous blood
    # ABL : arterial blood
    # EB : unspecified exhaled breath sample (assumed to be EEB)
    # PL: plasma
    # +W with work/exercise
    if (unique_scenarios$SAMPLING_MATRIX[i] == "VBL" | 
      unique_scenarios$SAMPLING_MATRIX[i] == "BL" | 
      unique_scenarios$SAMPLING_MATRIX[i] == "BL (+W)")
    {
      solve$simconc <- solve$Cven*this.Rb2p
      solve$unit <- "uM"
    } else if (unique_scenarios$SAMPLING_MATRIX[i] == "ABL") {
      solve$simconc <- solve$Cart*this.Rb2p
      solve$unit <- "uM"
    } else if (unique_scenarios$SAMPLING_MATRIX[i] == "EB" |
      unique_scenarios$SAMPLING_MATRIX[i] == "EEB" | 
      unique_scenarios$SAMPLING_MATRIX[i] == "EB (+W)")
    {
      if (unique_scenarios[i,"CONC_U"] == "ppmv")
      {
        solve$simconc <- solve$Cendexhppmv
        solve$unit <- "ppmv"
      } else if (unique_scenarios[i,"CONC_U"] == "uM") {
        solve$simconc <- solve$Cendexh
        solve$unit <- "uM"
      }
    } else if (unique_scenarios$SAMPLING_MATRIX[i] == "MEB") {
      if (unique_scenarios[i,"CONC_U"] == "ppmv")
      {
        solve$simconc <- solve$Cmixexhppmv
        solve$unit <- "ppmv"
      } else if (unique_scenarios[i,"CONC_U"] == "uM") {
        solve$simconc <- solve$Cmixexh
        solve$unit <- "uM"
      }
    } else if (unique_scenarios$SAMPLING_MATRIX[i] == "PL"){
      solve$simconc <- solve$Cplasma
      solve$unit <- "uM"
    } else {
      solve$simconc <- NA
      solve$unit <- NA
    }
    simlist[[i]] <- solve
}
```


Create a predicted vs. observed plot for each study:
```{r Makestudyplots, eval = execute.vignette}
cvtlist <- list()
for(i in 1:nrow(unique_scenarios))
{
    plot.data <- simlist[[i]]
    relconc <- obslist[[i]]

#Right now this is only calculating real concentrations according to mg/L in blood
    cvtlist[[i]] <- ggplot(plot.data, aes(time*24, simconc)) + 
      geom_line() + 
      xlab("Time (h)") + 
      ylab(paste0("Simulated ", 
        unique_scenarios$SAMPLING_MATRIX[i], 
        "\nConcentration (" , 
        solve$unit, ")")) + 
      ggtitle(paste0(
        unique_scenarios$PREFERRED_NAME[i],
        " (", 
        unique_scenarios$CONC_SPECIES[i], 
        ", ",
        round(as.numeric(unique_scenarios$DOSE[i]), digits = 2),
        unique_scenarios$DOSE_U[i], 
        " for ",
        round(unique_scenarios$EXP_LENGTH[i]*24, digits = 2),
        "h in ", 
        unique_scenarios$SAMPLING_MATRIX[i], ")")) + 
      geom_point(data = relconc, aes(TIME*24,CONCENTRATION)) + 
      theme(plot.title = element_text(face = 'bold', size = 20),
        axis.title.x = element_text(face = 'bold', size = 20), 
        axis.text.x = element_text(size=16), 
        axis.title.y = element_text(face = 'bold', size = 20), 
        axis.text.y = element_text(size = 16),
        legend.title = element_text(face = 'bold', size = 16),
        legend.text = element_text(face = 'bold',size = 14))+
      theme_bw() 
}
```

Create a list to hold the combined observations and predictions for each scenario:
```{r init_dataset, eval = execute.vignette}
# Creation of simulated vs. observed concentration dataset
unique_scenarios$RSQD <- 0
unique_scenarios$RMSE <- 0
unique_scenarios$AIC <- 0
simobslist <- list()
obvpredlist <- list()
```

Merge the simulations and observations on the basis of simulation time:
```{r merge_datasets, eval = execute.vignette}
for(i in 1:length(simlist))
{
  obsdata <- as.data.frame(obslist[[i]])
  simdata <- as.data.frame(simlist[[i]])
# skips over anything for which there was no observed data or 
# insufficient information to run simulation:
  if (!is.null(simlist[[i]]) & !is.null(obslist[[i]]))
  { 
# Make sure we are looking at consistent time points:
    simobscomb <- simdata[simdata$time %in% signif(obsdata$TIME,4),]
    obsdata <- subset(obsdata,signif(TIME,4) %in% simobscomb$time)
# Merge with obsdata
    colnames(obsdata)[colnames(obsdata) ==
      "TIME"] <- 
      "obstime"
# Round to match sim time:
    obsdata$time <- signif(obsdata$obstime,4)
    colnames(obsdata)[colnames(obsdata) ==
      "CONCENTRATION"] <- 
      "obsconc"
    colnames(obsdata)[colnames(obsdata) ==
      "PREFERRED_NAME"] <- 
      "chem"
    colnames(obsdata)[colnames(obsdata) ==
      "DOSE"] <- 
      "dose"
    colnames(obsdata)[colnames(obsdata) ==
      "EXP_LENGTH"] <- 
      "explen"
    colnames(obsdata)[colnames(obsdata) ==
      "CONC_SPECIES"] <- 
      "species"
    colnames(obsdata)[colnames(obsdata) ==
      "SAMPLING_MATRIX"] <- 
      "matrix"
    colnames(obsdata)[colnames(obsdata) ==
      "AVERAGE_MASS"] <- 
      "mw"
    colnames(obsdata)[colnames(obsdata) ==
      "CONC_U"] <- 
      "CONC_U"
    simobscomb <- suppressWarnings(merge(obsdata[,c(
      "time",
      "obstime",
      "obsconc",
      "chem",
      "dose",
      "explen",
      "species",
      "matrix",
      "mw",
      "CONC_U",
      "ORIGINAL_CONC_U"
      )], simobscomb, by="time", all.x=TRUE))

# Merge with met_data
    this.met_data <- subset(met_data,
      PREFERRED_NAME == simobscomb[1,"chem"] &
      SPECIES == simobscomb[1,"species"])
    colnames(this.met_data)[colnames(this.met_data)=="CHEM_CLASS"] <-
      "chemclass"
    colnames(this.met_data)[colnames(this.met_data) ==
      "OCTANOL_WATER_PARTITION_LOGP_OPERA_PRED"] <-
      "logp"
    colnames(this.met_data)[colnames(this.met_data) ==
      "WATER_SOLUBILITY_MOL.L_OPERA_PRED"] <-
      "sol"
    colnames(this.met_data)[colnames(this.met_data) ==
      "HENRYS_LAW_ATM.M3.MOLE_OPERA_PRED"] <-
      "henry"
    colnames(this.met_data)[colnames(this.met_data) ==
      "VMAX"] <-
      "vmax"
    colnames(this.met_data)[colnames(this.met_data) ==
      "KM"] <-
      "km"
    simobscomb <- suppressWarnings(cbind(simobscomb,this.met_data[c(
      "chemclass",
      "logp",
      "sol",
      "henry",
      "vmax",
      "km")]))
    simobslist[[i]] <- simobscomb
  }
}
```

Identify which quartile each observation occurred in with respect to the latest (maximum)
observed time
```{r time_quartile, eval = execute.vignette}
for(i in 1:length(simobslist))
  if (nrow(simobslist[[i]])>0)
  {
    simobscomb <- simobslist[[i]]
    for (j in 1:nrow(simobscomb))
    { 
      max.time <- max(simobscomb$time,na.rm=TRUE)
      if (is.na(max.time)) simobscomb$tquart <- NA
      else if (max.time == 0) simobscomb$tquart <- "1"
      else if (!is.na(simobscomb$time[j])) 
      {
        simobscomb$tquart[j] <- as.character(1 +
          floor(simobscomb$time[j]/max.time/0.25))
        simobscomb$tquart[simobscomb$tquart=="5"] <-
          "4"
      } else simobscomb$tquart[j] >- NA
    }
    simobslist[[i]] <- simobscomb
  }
```

Calculate the area under the curve (AUC)
```{r calc_AUC, eval = execute.vignette}
for(i in 1:length(simobslist))
  if (nrow(simobslist[[i]])>0)
  {
    simobscomb <- simobslist[[i]]
# Calculat the AUC with the trapezoidal rule:    
    if (nrow(simobscomb)>1) 
    {
      for (k in 2:max(nrow(simobscomb)-1,2,na.rm=TRUE))
      {
        simobscomb$obsAUCtrap[1] <- 0
        simobscomb$simAUCtrap[1] <- 0
        if (min(simobscomb$time) <= (simobscomb$explen[1]*1.03) & 
            nrow(simobscomb) >=2)
        {
          simobscomb$obsAUCtrap[k] <- simobscomb$obsAUCtrap[k-1] + 
            0.5*(simobscomb$time[k] - simobscomb$time[k-1]) * 
            (simobscomb$obsconc[k] + simobscomb$obsconc[k-1])
          simobscomb$simAUCtrap[k] <- simobscomb$simAUCtrap[k-1] + 
            0.5*(simobscomb$time[k]-simobscomb$time[k-1]) * 
            (simobscomb$simconc[k] + simobscomb$simconc[k-1])
        } else {
          simobscomb$obsAUCtrap <- 0
          simobscomb$simAUCtrap <- 0
        }
      }
    } else {
      simobscomb$obsAUCtrap <- 0
      simobscomb$simAUCtrap <- 0
    }
    simobscomb$AUCobs <- max(simobscomb$obsAUCtrap)
    simobscomb$AUCsim <- max(simobscomb$simAUCtrap)
    simobscomb$calcAUC <- max(simobscomb$AUC)
    if (min(simobscomb$time) <= simobscomb$explen[1]*1.03)
    {
      simobscomb$Cmaxobs <- max(simobscomb$obsconc)
      simobscomb$Cmaxsim <- max(simobscomb$simconc)
    } else {
      simobscomb$Cmaxobs <- 0
      simobscomb$Cmaxsim <- 0
    }
    simobslist[[i]] <- simobscomb
  }
```

Calculate performance statistics
```{r calc_error, eval = execute.vignette}
for(i in 1:length(simobslist))
  if (nrow(simobslist[[i]])>0)
  {
    simobscomb <- simobslist[[i]]
    unique_scenarios$RSQD[i] <- 1 - (
      sum((simobscomb$obsconc - simobscomb$simconc)^2) / 
      sum((simobscomb$obsconc-mean(simobscomb$obsconc))^2)
      )
    unique_scenarios$RMSE[i] <- 
      sqrt(mean((simobscomb$simconc - simobscomb$obsconc)^2))
    unique_scenarios$AIC[i] <- 
      nrow(simobscomb)*(
        log(2*pi) + 1 +
        log((sum((simobscomb$obsconc-simobscomb$simconc)^2) /
          nrow(simobscomb)))
      ) + ((44+1)*2) #44 is the number of parameters from inhalation_inits.R
    simobslist[[i]] <- simobscomb
  }
```

Combine individual studies into single table
```{r combine_studies, eval = execute.vignette}
obsvpredlist <- list()
for(i in 1:length(simobslist))
  if (nrow(simobslist[[i]])>0)
  {
    simobscomb <- simobslist[[i]]
    obsvpredlist[[i]] <- ggplot(simobscomb, aes(x = simconc, y = obsconc)) + 
      geom_point() + 
      geom_abline() + 
      xlab("Simulated Concentrations (uM)") + 
      ylab("Observed Concentrations (uM)") + 
      ggtitle(paste0(
        unique_scenarios$PREFERRED_NAME[i],
        " (", 
        unique_scenarios$CONC_SPECIES[i],
        ", ",
        round(as.numeric(unique_scenarios$DOSE[i]), digits = 2),
        unique_scenarios$DOSE_U[i], 
        " for ",
        round(unique_scenarios$EXP_LENGTH[i]*24, digits = 2),
        "h in ", 
        unique_scenarios$SAMPLING_MATRIX[i], ")")) + 
      theme_bw() + 
      theme(plot.title = element_text(face = 'bold', size = 20),
        axis.title.x = element_text(face = 'bold', size = 20), 
        axis.text.x = element_text(size=16), 
        axis.title.y = element_text(face = 'bold', size = 20), 
        axis.text.y = element_text(size = 16),
        legend.title = element_text(face = 'bold', size = 16),
        legend.text = element_text(face = 'bold',size = 14))
  }
```


### Write out the study-level figures:

```{r obsvspredplots, eval = execute.vignette}
# Create pdfs of observed vs. predicted concentration plots
dir.create("Linakis2020")
pdf("Linakis2020/obvpredplots.pdf", width = 10, height = 10)
for (i in 1:length(obsvpredlist)) {
  print(obsvpredlist[[i]])
}
dev.off()
```

### Finish simulated concentration/time plots
```{r annotate_study_stats, eval = execute.vignette}
for (i in 1:length(cvtlist))
{
  cvtlist[[i]] <- cvtlist[[i]] + 
    geom_text(
      x = Inf, 
      y = Inf, 
      hjust = 1.3, 
      vjust = 1.3, 
#      size = 6, 
      label = paste0(
        "RMSE: ", 
        round(unique_scenarios$RMSE[i],digits = 2),
        "\nAIC: ", 
        round(unique_scenarios$AIC[i],digits = 2)))# + 
#    theme(
#      plot.title = element_text(face = 'bold', size = 15),
#      axis.title.x = element_text(face = 'bold', size = 20), 
#      axis.text.x = element_text(size=16), 
#      axis.title.y = element_text(face = 'bold', size = 20), 
#      axis.text.y = element_text(size = 16),
#      legend.title = element_text(face = 'bold', size = 16),
#      legend.text = element_text(face = 'bold',size = 14))
}
```

```{r cvtplots, eval = execute.vignette}
# Create pdfs of simulated concentration-time plots against observed c-t values
pdf("Linakis2020/simdataplots.pdf")
for (i in 1:length(cvtlist)) {
  print(cvtlist[[i]])
}
dev.off()
```

Combine obs. vs. pred. into single table:
```{r combine_obs_vs_pred, eval = execute.vignette}
simobsfull <- do.call("rbind",simobslist)
simobsfullrat <- subset(simobsfull, simobsfull$species == "Rat")
simobsfullhum <- subset(simobsfull, simobsfull$species == "Human")
unique_scenarios <- subset(unique_scenarios,!is.na(unique_scenarios$RSQD))
```

The observations in simobsfull are in both uM and ppmv -- standardize to uM
```{r Standardize Units, eval = execute.vignette}
these.chems <- unique(subset(simobsfull,unit=="ppmv")$chem)
for (this.chem in these.chems)
{
  # Use HTTK unit conversion:
  this.factor <- convert_units(
    input.units="ppmv", output.units="um", chem.name=this.chem)
      
  # Scale the observation    
  simobsfull[simobsfull$chem==this.chem & 
              simobsfull$unit=="ppmv","obsconc"] <-
    this.factor * simobsfull[
      simobsfull$chem==this.chem & 
        simobsfull$unit=="ppmv","obsconc"]
  # Scale the prediction    
  simobsfull[simobsfull$chem==this.chem & 
              simobsfull$unit=="ppmv","simconc"] <-
    this.factor * simobsfull[
      simobsfull$chem==this.chem & 
        simobsfull$unit=="ppmv","simconc"]
  # Change the reported unit
  simobsfull[simobsfull$chem==this.chem & 
              simobsfull$unit=="ppmv","unit"] <-
    "uM"
}
```


### Regressions
Other analytics including linear regression on overall concentration vs. time observed vs. predicted
```{r regression, eval = execute.vignette}
table(unique_scenarios$CONC_SPECIES)
nrow(simobsfull) - nrow(simobsfull[
  !is.na(simobsfull$simconc) & 
  simobsfull$simconc > 0 & 
  simobsfull$obsconc > 0,])
pmiss <- (nrow(simobsfull) - 
  nrow(simobsfull[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc > 0,])) /
  nrow(simobsfull) * 100
missdata <- (simobsfull[
  is.na(simobsfull$simconc) | 
  simobsfull$simconc <= 0 | 
  simobsfull$obsconc <= 0,])
t0df <- simobsfull[simobsfull$obstime == 0,]
lmall <- lm(
#log transforms:
  log10(simobsfull$obsconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc > 0]) ~ 
#log transforms:
  log10(simobsfull$simconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc > 0])) 
#Linear binned 1
lmsub1 <- lm(
  simobsfull$obsconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc < 0.1] ~ 
  simobsfull$simconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc < 0.1])
#Linear binned 2
lmsub2 <- lm(
  simobsfull$obsconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc >= 0.1 & 
    simobsfull$obsconc < 10] ~ 
  simobsfull$simconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc >= 0.1 & 
    simobsfull$obsconc < 10]) 
#Linear binned 3
lmsub3 <- lm(
  simobsfull$obsconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc >= 10] ~ 
  simobsfull$simconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc >= 10]) 
lmrat <- lm(
  log10(simobsfullrat$obsconc[
    !is.na(simobsfullrat$simconc) & 
    simobsfullrat$simconc > 0 & 
    simobsfullrat$obsconc > 0]) ~ 
  log10(simobsfullrat$simconc[
    !is.na(simobsfullrat$simconc) & 
    simobsfullrat$simconc > 0 & 
    simobsfullrat$obsconc > 0]))
unique(simobsfullrat$chem)
lmhum <- lm(
  log10(simobsfullhum$obsconc[
    !is.na(simobsfullhum$simconc) & 
    simobsfullhum$simconc > 0 & 
    simobsfullhum$obsconc > 0]) ~ 
  log10(simobsfullhum$simconc[
    !is.na(simobsfullhum$simconc) & 
    simobsfullhum$simconc > 0 & 
    simobsfullhum$obsconc > 0]))
unique(simobsfullhum$chem)
concregslope <- summary(lmall)$coef[2,1]
concregr2 <- summary(lmall)$r.squared
concregrmse <- sqrt(mean(lmall$residuals^2))
totalrmse <- sqrt(mean((
  log10(simobsfull$simconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc > 0]) - 
  log10(simobsfull$obsconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc > 0]))^2, 
   na.rm = TRUE))
totalmae <- mean(abs(
  log10(simobsfull$simconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc > 0]) - 
  log10(simobsfull$obsconc[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc > 0 & 
    simobsfull$obsconc > 0])), 
  na.rm = TRUE)
totalaic <- nrow(
  simobsfull[
    !is.na(simobsfull$simconc) & 
    simobsfull$simconc >0 & 
    simobsfull$obsconc > 
    0,]) *
  (log(2*pi) + 
     1 +
     log((sum(
       (simobsfull$obsconc[
         !is.na(simobsfull$simconc) & 
         simobsfull$simconc > 0 & 
         simobsfull$obsconc > 0] - 
       simobsfull$simconc[
         !is.na(simobsfull$simconc) & 
         simobsfull$simconc > 0 & 
         simobsfull$obsconc > 0])^2,
       na.rm=TRUE) / 
     nrow(simobsfull[
       !is.na(simobsfull$simconc) & 
       simobsfull$simconc > 0 & 
       simobsfull$obsconc > 0,])))) + 
  ((44+1)*2) #44 is the number of parameters from inhalation_inits.R
mispred <- table(abs(
  log10(simobsfull$simconc) -
  log10(simobsfull$obsconc))>2 & 
  simobsfull$simconc>0)
mispred[2]
mispred[2] / nrow(simobsfull[
  !is.na(simobsfull$simconc) & 
    simobsfull$simconc >0 & 
    simobsfull$obsconc > 0,])*100
overpred <- table(
  log10(simobsfull$simconc) -
  log10(simobsfull$obsconc)>2 & 
  simobsfull$simconc>0)
overpred[2]
overpred[2] / nrow(simobsfull[
  !is.na(simobsfull$simconc) & 
  simobsfull$simconc >0 & 
  simobsfull$obsconc > 0,])*100
underpred <- table(
  log10(simobsfull$obsconc) - 
  log10(simobsfull$simconc)>2 & 
  simobsfull$simconc>0)
underpred[2]
underpred[2] / nrow(simobsfull[
  !is.na(simobsfull$simconc) & 
  simobsfull$simconc >0 & 
  simobsfull$obsconc > 0,])*100
mispredhalf <- table(abs(
  log10(simobsfull$simconc) -
  log10(simobsfull$obsconc))>0.5 & 
  simobsfull$simconc>0)
mispredhalf[2]
mispredhalf[2] / nrow(simobsfull[
  !is.na(simobsfull$simconc) & 
  simobsfull$simconc >0 & 
  simobsfull$obsconc > 0,])*100
overpredhalf <- table(
  log10(simobsfull$simconc) - 
  log10(simobsfull$obsconc)>0.5 & 
  simobsfull$simconc>0)
overpredhalf[2]
overpredhalf[2] / nrow(simobsfull[
  !is.na(simobsfull$simconc) & 
  simobsfull$simconc >0 & 
  simobsfull$obsconc > 0,])*100
underpredhalf <- table(
  log10(simobsfull$obsconc) - 
  log10(simobsfull$simconc)>0.5 & 
  simobsfull$simconc>0)
underpredhalf[2]
underpredhalf[2] / nrow(simobsfull[
  !is.na(simobsfull$simconc) & 
  simobsfull$simconc > 0 & 
  simobsfull$obsconc > 0,])*100
chemunderpred <- subset(simobsfull,
  log10(simobsfull$simconc) -
  log10(simobsfull$obsconc) < 0 & 
  simobsfull$simconc > 0)
table(chemunderpred$chemclass) / table(simobsfull$chemclass)*100
```
## TABLE AND PLOT GENERATION

### Figure 2: overall observed vs. predicted plot
```{r obspredFig2, eval = execute.vignette}
fig2 <- ggplot(
  data = simobsfull[
    simobsfull$simconc > 0 & 
    simobsfull$obsconc > 0,], 
  aes(x = log10(simconc), y = log10(obsconc))) + 
  geom_point(
    color = ifelse(
      abs(
        log10(simobsfull[
          simobsfull$simconc > 0 & 
          simobsfull$obsconc > 0,]$simconc) -
        log10(simobsfull[
          simobsfull$simconc > 0 & 
          simobsfull$obsconc > 0,]$obsconc)) >2,
      'red',
      'black')) + 
  geom_abline() + 
  xlab("Log(Simulated Concentrations)") + 
  ylab("Log(Observed Concentrations)") + 
  theme_bw() + 
  geom_smooth(method = 'lm',se = FALSE, aes(color = 'Overall')) + 
  geom_smooth(method = 'lm', se = FALSE, aes(color = species)) + 
  geom_text(
    x = Inf, 
    y = -Inf, 
    hjust = 1.05, 
    vjust = -0.15, 
    size = 8, 
    label = paste0(
  #    "Regression slope: ", 
#      round(summary(lmall)$coef[2,1],digits = 2),
      "\nRegression R^2: ", 
      round(summary(lmall)$r.squared,digits = 2),
      "\nRegression RMSE: ", 
      round(sqrt(mean(lmall$residuals^2)),digits = 2),
      "\nRMSE (Identity): ", 
      round(totalrmse,digits = 2)
 #     "\n% Missing:", 
#      round(pmiss, digits = 2), "%")
    )) + 
  #geom_text(
  #  data = simobsfull[
  #    abs(log10(simobsfull$simconc) - log10(simobsfull$obsconc))>7 & 
  #    simobsfull$simconc>0 & simobsfull$obsconc > 0,], 
  #  aes(label = paste(chem,species,matrix)),
   ## fontface = 'bold',
  #  check_overlap = TRUE,
#    size = 3.5, 
  #  hjust = 0.5, 
  #  vjust = -0.8) + 
  scale_color_discrete(name = 'Species', breaks = c("Overall","Human","Rat")) +
  theme(
    plot.title = element_text(face = 'bold', size = 15),
    axis.title.x = element_text(face = 'bold', size = 30), 
    axis.text.x = element_text(size=16), 
    axis.title.y = element_text(face = 'bold', size = 30), 
    axis.text.y = element_text(size = 16),
    legend.title = element_text(face = 'bold', size = 24),
    legend.text = element_text(face = 'bold',size = 24))
fig2 #Display plot in R
```

```{r write_figure2, eval = execute.vignette}
pdf("Linakis2020/Figure2.pdf", width = 10, height = 10)
print(fig2)
dev.off()
```

### Create and read out plots of overall cvt, cmax, and auc observed vs. pred
```{r calculations, eval = execute.vignette}
# Create data and run calculations for populating plots
cmaxfull <- subset(simobsfull, !duplicated(simobsfull$AUCobs) & simobsfull$Cmaxobs != 0)
cmaxobs <- cmaxfull$Cmaxobs
cmaxsim <- cmaxfull$Cmaxsim
cmaxobs <- cmaxobs[!is.nan(cmaxsim)]
cmaxsim <- cmaxsim[!is.nan(cmaxsim)]
cmaxsim[!is.finite(log10(cmaxsim))] <- NA
cmaxlm <- lm(log10(cmaxobs)~log10(cmaxsim), na.action = na.exclude)
cmaxvcbkg <- subset(cmaxfull, 
  paste(
    cmaxfull$chem, 
    cmaxfull$dose, 
    cmaxfull$explen, 
    cmaxfull$species, 
    cmaxfull$matrix) %in% 
  paste(
    t0df$chem, 
    t0df$dose, 
    t0df$explen, 
    t0df$species,
    t0df$matrix))
cmaxvcbkg$cmaxcbkgratio <- cmaxvcbkg$Cmaxobs / cmaxvcbkg$obsconc
cmaxvcbkg$adjustedCmaxsim <- cmaxvcbkg$Cmaxsim - cmaxvcbkg$obsconc
aucfull <-subset(simobsfull, 
  !duplicated(simobsfull$AUCobs) & 
  simobsfull$AUCobs != 0)
aucobs <- aucfull$AUCobs
aucsim <- aucfull$AUCsim
aucobs <- aucobs[!is.nan(aucsim)]
aucsim <- aucsim[!is.nan(aucsim)]
aucsim[!is.finite(log10(aucsim))] <- NA
auclm <- lm(log10(aucobs)~log10(aucsim), na.action = na.exclude)
cmaxslope <- summary(cmaxlm)$coef[2,1]
cmaxrsq <- summary(cmaxlm)$r.squared
totalrmsecmax <- sqrt(mean((log10(cmaxfull$Cmaxsim) - 
  log10(cmaxfull$Cmaxobs))^2, na.rm = TRUE))
cmaxmiss <- nrow(cmaxfull[
  abs(log10(cmaxfull$Cmaxsim) - 
  log10(cmaxfull$Cmaxobs)) > 1,])
cmaxmissp <- nrow(cmaxfull[
  abs(log10(cmaxfull$Cmaxsim) - 
  log10(cmaxfull$Cmaxobs)) > 1,]) /
  nrow(cmaxfull) * 100
cmaxmisschem <- table(cmaxfull[
  abs(log10(cmaxfull$Cmaxsim) - 
  log10(cmaxfull$Cmaxobs)) > 1,]$chem)
aucslope <- summary(auclm)$coef[2,1]
aucrsq <- summary(auclm)$r.squared
totalrmseauc <- sqrt(mean((
  log10(aucfull$AUCsim) - 
  log10(aucfull$AUCobs))^2, na.rm = TRUE))
aucmiss <- nrow(aucfull[
  abs(log10(aucfull$AUCsim) - 
  log10(aucfull$AUCobs)) > 1,])
aucmissp <- nrow(aucfull[
  abs(log10(aucfull$AUCsim) - 
  log10(aucfull$AUCobs)) > 1,]) / 
  nrow(aucfull) * 100
aucmisschem <- table(aucfull[
  abs(log10(aucfull$AUCsim) - 
  log10(aucfull$AUCobs)) > 1,]$chem)
```

### Figure 4: Cmax and AUC observed vs. Predicted Values
```{r Fig4plot, eval = execute.vignette}
cmaxp <- ggplot(data = cmaxfull, aes(x = log10(Cmaxsim), y = log10(Cmaxobs))) +
  geom_point(color = 
    ifelse(abs(log10(cmaxfull$Cmaxsim)  -log10(cmaxfull$Cmaxobs))>=2, "red","black"))  +
  geom_abline()  + 
  xlab("Log(Simulated Max Concentration)") + 
  ylab("Log(Observed Max Concentration)") + 
  theme_bw() + 
  geom_smooth(method = 'lm', se = FALSE, aes(color = 'Overall')) + 
  geom_smooth(method = 'lm', se = FALSE, aes(color = species)) +
  geom_text(x = Inf, 
    y = -Inf, 
    hjust = 1.05, 
    vjust = -0.15, 
#    size = 6, 
    label = paste0("Regression slope: ", 
      round(summary(cmaxlm)$coef[2,1],digits = 2),
      "\nRegression R^2: ", 
      round(summary(cmaxlm)$r.squared,digits = 2))) +
  geom_text_repel(
    data = cmaxfull[
      (log10(cmaxfull$Cmaxsim)-log10(cmaxfull$Cmaxobs))>=2 & 
      log10(cmaxfull$Cmaxsim) > 2,], 
    aes(label = paste(chem,species,matrix)), 
    force = 2, 
 #   size = 5.3, 
    fontface = 'bold', 
    color = 'black', 
    hjust = -0.05, 
    vjust = 0.5) + 
  scale_y_continuous(lim = c (-1,5)) + 
  scale_x_continuous(lim = c(-1,5)) + 
  geom_text_repel(
    data = cmaxfull[
      (log10(cmaxfull$Cmaxsim)-log10(cmaxfull$Cmaxobs))>=2 &
      log10(cmaxfull$Cmaxsim) <= 2,], 
    aes(label = paste(chem,species,matrix)), 
    nudge_x = 0.0,
    nudge_y = -0.2, 
    force = 2, 
#    size = 5.3, 
    fontface = 'bold', 
    color = 'black', 
    hjust = -0.05, 
    vjust = 0.5) + 
  geom_text(
    data = cmaxfull[
      (log10(cmaxfull$Cmaxsim)-log10(cmaxfull$Cmaxobs))<=-2,], 
    aes(label = paste(chem,species,matrix)), 
#    size = 5.3, 
    fontface = 'bold', 
    color = 'black', 
    hjust = 0.5, 
    vjust = -0.7) + 
  scale_color_discrete(
    name = 'Species', 
    breaks = c("Overall","Human","Rat")) #+ 
#  theme(plot.title = element_text(face = 'bold', size = 10),
#    axis.title.x = element_text(face = 'bold', size = 10), 
#    axis.text.x = element_text(size=8), 
#    axis.title.y = element_text(face = 'bold', size = 10), 
#    axis.text.y = element_text(size = 8),
#    legend.title = element_text(face = 'bold', size = 8),
#    legend.text = element_text(face = 'bold',size = 8))
cmaxp
aucp <- ggplot(
  data = aucfull, 
  aes(x = log10(AUCsim), y = log10(AUCobs))) + 
  geom_point(color = 
    ifelse(abs(log10(aucfull$AUCsim)-log10(aucfull$AUCobs))>=2, "red","black"))  +
  geom_abline()  + 
  xlab("Log(Simulated AUC)") + 
  ylab("Log(Observed AUC)") + 
  theme_bw() + 
  geom_smooth(method = 'lm', se = FALSE, aes(color = "Overall")) + 
  geom_smooth(method = 'lm', se = FALSE, aes(color = species)) + 
  geom_text(
    x = Inf, 
    y = -Inf, 
    hjust = 1.05, 
    vjust = -0.15, 
#    size = 6, 
    label = paste0(
      "Regression slope: ", 
      round(summary(auclm)$coef[2,1],digits = 2),
      "\nRegression R^2: ", 
      round(summary(auclm)$r.squared,digits = 2))) + 
  geom_text_repel(
    data = aucfull[(log10(aucfull$AUCsim)-log10(aucfull$AUCobs))>=2,], 
    aes(label = paste(chem,species,matrix)), 
#    size = 5.3, 
    fontface = 'bold', 
    color = 'black', 
    hjust = -0.05, 
    vjust = 0.5) + 
  scale_y_continuous(lim = c (-2,4)) + 
  scale_x_continuous(lim = c(-2,4)) + 
  geom_text(
    data = aucfull[(log10(aucfull$AUCsim)-log10(aucfull$AUCobs))<=-2,], 
    aes(label = paste(chem,species,matrix)), 
#    size = 5.3, 
    fontface = 'bold', 
    color = 'black', 
    hjust = 0.5, 
    vjust = -0.8) + 
  scale_color_discrete(name = 'Species', breaks = c("Overall","Human","Rat")) #+
#  theme(
#    plot.title = element_text(face = 'bold', size = 15),
#    axis.title.x = element_text(face = 'bold', size = 20), 
#    axis.text.x = element_text(size=16), 
#    axis.title.y = element_text(face = 'bold', size = 20), 
#    axis.text.y = element_text(size = 16),
#    legend.title = element_text(face = 'bold', size = 16),
#    legend.text = element_text(face = 'bold',size = 14))
aucp
```

```{r write_figure4, eval = execute.vignette}
pdf("Linakis2020/Figure4.pdf", width = 20, height = 10)
plot_grid(cmaxp,aucp,nrow = 1, labels = c('A','B'), label_size = 30)
dev.off()
```

### Figure 3: Separation by chemical class
```{r Fig3plot, eval = execute.vignette}
simobsfull$aggscen <- as.factor(paste(simobsfull$chem, 
  simobsfull$species, 
  simobsfull$matrix))
chem.lm <- lm(
  log10(simconc) - log10(obsconc) ~ 
  aggscen, 
  data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,])
chem.res <- resid(chem.lm)
# Number of observations per chemical class
numpt <- simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,] %>%
  group_by(chemclass) %>% summarize(n = paste("n =", length(simconc))) 
fig3 <- ggplot(
  data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,], 
  aes(x = aggscen, y = log10(simconc)-log10(obsconc), fill = chemclass)) +
  geom_boxplot() +
  geom_hline(yintercept = 0) +
  geom_hline(yintercept = 2, linetype = 2)+
  geom_hline(yintercept = -2, linetype = 2)+
  xlab("Exposure Scenario") +
  ylab("Log(Simulated Concentration)-\nLog(Observed Concentration)\n") +
  facet_wrap(vars(chemclass), scales = 'free_x', nrow = 1) + #35 by 12 for poster
  theme_bw() +
  geom_text(
    data = numpt, 
    aes(x = Inf, y = -Inf, hjust = 1.05, vjust = -0.5, label = n), 
    size = 10, 
    colour = 'black', 
    inherit.aes = FALSE, 
    parse = FALSE) +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1,vjust=0.5,size = 20, face = 'bold'),
    strip.text.x = element_text(face = 'bold', size = 24), 
    legend.position = 'none', 
    axis.title.x = element_text(face = 'bold', size = 30), 
    axis.title.y = element_text(face = 'bold', size = 30), 
    axis.text.y = element_text(face = 'bold',size = 25, color = 'black'))
fig3
```

```{r write_figure3, eval = execute.vignette}
pdf("Linakis2020/Figure3.pdf", width = 40, height = 13)
print(fig3)
dev.off()
```

### Figures S1A-S1D: Separation by time quartile and physicochemical properties
```{r FigS1, eval = execute.vignette}
figs1a <- ggplot(
    data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,], 
    aes(x = tquart, y = log10(simconc)-log10(obsconc))) +
  geom_boxplot()+
  geom_hline(yintercept = 0)+
  geom_hline(yintercept = 2, linetype = 2)+
  geom_hline(yintercept = -2, linetype = 2)+
  xlab("\nTime Quartile\n") +
  ylab("Log(Simulated Concentration)-\nLog(Observed Concentration)\n") +
  theme_bw()+
  theme(
    axis.text.x = element_text(size = 20, face = 'bold'), 
    strip.text.x = element_text(face = 'bold', size = 20), 
    legend.position = 'none', 
    axis.title.x = element_text(face = 'bold', size = 20), 
    axis.title.y = element_text(face = 'bold', size = 20), 
    axis.text.y = element_text(size = 20, face = 'bold'))
figs1a
figs1b <- ggplot(
    data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,], 
    aes(x = mw, y = log10(simconc)-log10(obsconc))) +
  geom_point()+
  geom_hline(yintercept = 0)+
  geom_hline(yintercept = 2, linetype = 2)+
  geom_hline(yintercept = -2, linetype = 2)+
  xlab("\nMolecular Weight (g/mol)\n") +
  ylab("\nLog(Simulated Concentration)-\nLog(Observed Concentration)\n") +
  theme_bw()+
  theme(
    axis.text.x = element_text(size = 20, face = 'bold'), 
    strip.text.x = element_text(face = 'bold', size = 20), 
    legend.position = 'none', 
    axis.title.x = element_text(face = 'bold', size = 20), 
    axis.title.y = element_text(face = 'bold', size = 20), 
    axis.text.y = element_text(size = 20, face = 'bold'))
figs1b
figs1c <- ggplot(
    data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,], 
    aes(x = logp, y = log10(simconc)-log10(obsconc))) +
  geom_point()+
  geom_hline(yintercept = 0)+
  geom_hline(yintercept = 2, linetype = 2)+
  geom_hline(yintercept = -2, linetype = 2)+
  xlab("\nLog P") +
  ylab("Log(Simulated Concentration)-\nLog(Observed Concentration)\n") +
  theme_bw() +
  theme(
    axis.text.x = element_text(size = 20, face = 'bold'), 
    strip.text.x = element_text(face = 'bold', size = 20), 
    legend.position = 'none', 
    axis.title.x = element_text(face = 'bold', size = 20), 
    axis.title.y = element_text(face = 'bold', size = 20), 
    axis.text.y = element_text(size = 20, face = 'bold'))
figs1c
figs1d <- ggplot(
    data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,], 
    aes(x = sol, y = log10(simconc)-log10(obsconc))) +
  geom_point()+
  geom_hline(yintercept = 0)+
  geom_hline(yintercept = 2, linetype = 2)+
  geom_hline(yintercept = -2, linetype = 2)+
  xlab("\nSolubility (mol/L)") +
  ylab("\nLog(Simulated Concentration)-\nLog(Observed Concentration)\n") +
  scale_x_log10()+
  theme_bw() +
  theme(
    axis.text.x = element_text(size = 20, face = 'bold'), 
    strip.text.x = element_text(face = 'bold', size = 20), 
    legend.position = 'none', 
    axis.title.x = element_text(face = 'bold', size = 20), 
    axis.title.y = element_text(face = 'bold', size = 20), 
    axis.text.y = element_text(size = 20, face = 'bold'))
figs1d
```

```{r write_figures1, eval = execute.vignette}
pdf("Linakis2020/FigureS1.pdf", width = 20, height = 20)
plot_grid(figs1a,figs1b,figs1c,figs1d,nrow = 2, labels = c('A','B','C','D'), label_size = 30)
dev.off()
```

### Supplemental Table 2: Leave-one-out Chemical Sensitivity Analysis
```{r SupTable2, eval = execute.vignette}
senschem <- list()
sensslope <- list()
sensrsq <- list()
sensregrmse <- list()
senstotalrmse <- list()
senspmiss <- list()
senscmaxslope <- list()
senscmaxrsq <- list()
senstotalrmsecmax <- list()
sensaucslope <- list()
sensaucrsq <- list()
senstotalrmseauc <- list()
for (i in 1:nrow(simobsfull))
{
  simobsfullsens <- subset(simobsfull,simobsfull$chem != simobsfull$chem[i])
  senschem[i] <- as.character(simobsfull$chem[i])
  senslm <- lm(
    log10(simobsfullsens$obsconc[
      !is.na(simobsfullsens$simconc) & 
      simobsfullsens$simconc > 0 & 
      simobsfullsens$obsconc > 0]) ~ 
    log10(simobsfullsens$simconc[
      !is.na(simobsfullsens$simconc) & 
      simobsfullsens$simconc >0 & 
      simobsfullsens$obsconc > 0]))
  sensslope[i] <- round(summary(senslm)$coef[2,1],digits = 2)
  sensrsq[i] <- round(summary(senslm)$r.squared,digits = 2)
  sensregrmse[i] <- round(sqrt(mean(senslm$residuals^2)),digits = 2)
  senstotalrmse[i] <- round(sqrt(mean((
    log10(simobsfullsens$simconc[
      !is.na(simobsfullsens$simconc) & 
      simobsfullsens$simconc >0 & 
      simobsfullsens$obsconc > 0]) -   
    log10(simobsfullsens$obsconc[
      !is.na(simobsfullsens$simconc) & 
        simobsfullsens$simconc >0 & 
        simobsfullsens$obsconc > 0]))^2, 
    na.rm = TRUE)), digits = 2)
  senspmiss[i] <- round((nrow(simobsfullsens) - 
    nrow(simobsfullsens[
      !is.na(simobsfullsens$simconc) & 
      simobsfullsens$simconc >0 & 
      simobsfullsens$obsconc > 0,])) / nrow(simobsfullsens) * 100, 
    digits = 2)
  senscmaxfull <- subset(simobsfullsens, !duplicated(simobsfullsens$Cmaxobs))
  senscmaxlm <- lm(
    log10(senscmaxfull$Cmaxobs[senscmaxfull$Cmaxobs>0]) ~
    log10(senscmaxfull$Cmaxsim[senscmaxfull$Cmaxobs>0]), 
    na.action = na.exclude)
  sensaucfull <-subset(simobsfullsens, !duplicated(simobsfullsens$AUCobs))
  sensauclm <- lm(
    log10(aucfull$AUCobs[aucfull$AUCobs>0]) ~
    log10(aucfull$AUCsim[aucfull$AUCobs>0]), 
    na.action = na.exclude)
  senscmaxslope[i] <- round(summary(senscmaxlm)$coef[2,1],digits = 2)
  senscmaxrsq[i] <- round(summary(senscmaxlm)$r.squared,digits = 2)
  senstotalrmsecmax[i] <- sqrt(mean((log10(senscmaxfull$Cmaxsim[senscmaxfull$Cmaxobs>0]) - log10(senscmaxfull$Cmaxobs[senscmaxfull$Cmaxobs>0]))^2, na.rm = TRUE))
  sensaucslope[i] <- round(summary(sensauclm)$coef[2,1],digits = 2)
  sensaucrsq[i] <- round(summary(sensauclm)$r.squared,digits = 2)
  senstotalrmseauc[i] <- sqrt(mean((log10(sensaucfull$AUCsim[sensaucfull$AUCobs>0]) - log10(sensaucfull$AUCobs[sensaucfull$AUCobs>0]))^2, na.rm = TRUE))
}
sensitivitydf <- data.frame(Chemical <- as.character(senschem),
                            sensSlope <- as.numeric(sensslope),
                            sensRsq <- as.numeric(sensrsq),
                            sensRegrmse <- as.numeric(sensregrmse),
                            sensTotrmse <- as.numeric(senstotalrmse),
                            sensPmiss <- as.numeric(senspmiss),
                            sensCmaxslope <- as.numeric(senscmaxslope),
                            sensCmaxrsq <- as.numeric(senscmaxrsq),
                            sensCmaxrmse <- as.numeric(senstotalrmsecmax),
                            sensAUCslope <- as.numeric(sensaucslope),
                            sensAUCrsq <- as.numeric(sensaucrsq),
                            sensAUCrmse <- as.numeric(senstotalrmseauc),
                            stringsAsFactors=FALSE)
sensitivitydf <- subset(sensitivitydf,!duplicated(sensitivitydf$Chemical....as.character.senschem.))
names(sensitivitydf) <- c('Chemical Dropped','Regression Slope','Regression R^2','Regression RMSE','Orthogonal RMSE', 'Percent Data Censored','Cmax Regression Slope','Cmax Regression R^2','Cmax Orthogonal RMSE','AUC Regression Slope','AUC Regression R^2','AUC Orthogonal RMSE')
notdropped <- c('None',concregslope,concregr2,concregrmse,totalrmse,pmiss,cmaxslope,cmaxrsq,totalrmsecmax,aucslope,aucrsq,totalrmseauc)
sensitivitydf <- rbind(notdropped, sensitivitydf)
sensitivitydf[,-1] <- sapply(sensitivitydf[,-1],as.numeric)
sensitivitydf[,-1] <- round(sensitivitydf[,-1],2)
  # Clean up and write file
rm(chem.lm, obvpredplot, p, pdata1, plot.data, plots, relconc, sensaucfull, sensauclm, sensaucrsq, sensaucslope, senschem, senscmaxfull, senscmaxlm, senscmaxrsq, senscmaxslope, senslm, senspmiss, sensregrmse, sensrsq, sensslope, senstotalrmse, senstotalrmseauc, senstotalrmsecmax, solve, AUCrmse, AUCrsq, AUCslope, chem.res, Chemical, Cmaxrmse, Cmaxrsq, Cmaxslope, colors, count, i, j, k, met_col, name, name1, Pmiss, Regrmse, Rsq, Slope, rem, Totrmse)

write.csv(sensitivitydf, 'supptab2.csv',row.names = FALSE)
```

### Supplemental Table 1
```{r SupTable1, eval = execute.vignette}
supptab1 <- subset(unique_scenarios, !duplicated(unique_scenarios$PREFERRED_NAME) | !duplicated(unique_scenarios$SOURCE_CVT) | !duplicated(unique_scenarios$CONC_SPECIES))
for(i in 1:nrow(supptab1)){
  tryCatch({
    supptab1$Metabolism_Source[i] <- met_data$SOURCE_MET[met_data$DTXSID %in% supptab1$DTXSID[i] & met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
    supptab1$Log_P[i] <- met_data$OCTANOL_WATER_PARTITION_LOGP_OPERA_PRED[met_data$DTXSID %in% supptab1$DTXSID[i]& met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
    supptab1$Solubility[i] <- met_data$WATER_SOLUBILITY_MOL.L_OPERA_PRED[met_data$DTXSID %in% supptab1$DTXSID[i]& met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
    supptab1$Blood_Air_Partition_Coefficient[i] <- met_data$CALC_PBA[met_data$DTXSID %in% supptab1$DTXSID[i]& met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
    supptab1$Chem_Class[i] <- met_data$CHEM_CLASS[met_data$DTXSID %in% supptab1$DTXSID[i] & met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
    supptab1$Species[i] <- met_data$SPECIES[met_data$DTXSID %in% supptab1$DTXSID[i] & met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
    supptab1$Vmax[i] <- met_data$VMAX[met_data$DTXSID %in% supptab1$DTXSID[i] & met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
    supptab1$Km[i] <- met_data$KM[met_data$DTXSID %in% supptab1$DTXSID[i] & met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
  }, error = function(e){})
}
supptab1 <- supptab1[c('PREFERRED_NAME','DTXSID','CASRN','Chem_Class','AVERAGE_MASS','Log_P','Solubility','Blood_Air_Partition_Coefficient','Species','Vmax','Km','Metabolism_Source','SOURCE_CVT')]
names(supptab1) <- c('Chemical','DTXSID','CASRN','CAMEO Chemical Class','Molecular Weight (g/mol)','Log P','Solubility (mol/L)','Blood Air Partition Coefficient','Available Species Data','Vmax (pmol/min/10^6 cells)','KM (uM)','Metabolism Data Source','Concentration-Time Data Source')
write.csv(supptab1, "supptab1.csv", row.names = FALSE)
```
