---
title: "Linakis et al. (Submitted): Analysis and Figure Generation"
author: "Matt Linakis"
date: "January 30, 2020"
output: rmarkdown::html_vignette
vignette: >
 %\VignetteIndexEntry{Linakis et al. (Submitted): Analysis and Figure Generation}
 %\VignetteEngine{knitr::rmarkdown}
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---
mlinak01@gmail.com

## 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.

## Prepare for session
### Load the relevant libraries
```{r setup}
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)
# Delete all objects from memory:
rm(list=ls())
# We love to give warning messages whenever assumptions are used by HTTK,
# but they will overwhelm the output of this vignette so we turn them
# off:
options(warn = -1)
```
###Get metabolism and concentration data 
```{r data}
met_data <- metabolism_data_Linakis2020
conc_data <- concentration_data_Linakis2020
```

## 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}
# 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}
# 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
```{r runmodel}
plist <- list()
simlist <- list()
obslist <- list()
for(i in 1:nrow(unique_scenarios)){
  #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
    name <- paste0("out",i)
    if(as.character(unique_scenarios$CONC_SPECIES[i]) == "Human"){
      solve <- assign(name, solve_gas_pbtk(
        chem.cas = unique_scenarios$CASRN[i], 
        days = (unique_scenarios$TIME[i]+unique_scenarios$EXP_LENGTH[i]), 
# Make sure we get conc's at the observed times:
        times=signif(obslist[[i]]$TIME,4), 
        tsteps = 500, 
        exp.conc = ((as.numeric(unique_scenarios$DOSE[i])*1e20*1000)/24450)/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]), 
        vmax.km = F, 
        vmax = met_data$VMAX[met_data$CASRN %in% unique_scenarios$CASRN[i] & 
        met_data$SPECIES == unique_scenarios$CONC_SPECIES[i]], 
        km = met_data$KM[met_data$CASRN %in% unique_scenarios$CASRN[i] & 
        met_data$SPECIES == unique_scenarios$CONC_SPECIES[i]],
        suppress.messages=T))
    } else {
      solve <- assign(name, solve_gas_pbtk(
        chem.cas = unique_scenarios$CASRN[i], 
        days = (unique_scenarios$TIME[i]+unique_scenarios$EXP_LENGTH[i]), 
# Make sure we get conc's at the observed times:
        times=signif(obslist[[i]]$TIME,4),
        tsteps = 500, 
        exp.conc = ((as.numeric(unique_scenarios$DOSE[i])*1e20*1000)/24450)/1e20, 
        exp.duration = unique_scenarios$EXP_LENGTH[i]*24, 
        period = (unique_scenarios$TIME[i]+unique_scenarios$EXP_LENGTH[i])*24, 
        species = as.character(unique_scenarios$CONC_SPECIES[i]), 
        vmax.km = T, 
        vmax = met_data$VMAX[met_data$CASRN %in% unique_scenarios$CASRN[i] & 
        met_data$SPECIES == unique_scenarios$CONC_SPECIES[i]], 
        km = met_data$KM[met_data$CASRN %in% unique_scenarios$CASRN[i] &
        met_data$SPECIES == unique_scenarios$CONC_SPECIES[i]],
        suppress.messages=T))
    }
    #browser()
    solve <- as.data.frame(solve)
    # Sets the output units appropriate for the sampling matrix
    if (unique_scenarios$SAMPLING_MATRIX[i] == "VBL" | 
      unique_scenarios$SAMPLING_MATRIX[i] == "BL" | 
      unique_scenarios$SAMPLING_MATRIX[i] == "BL (+W)")
    {
      solve$simconc <- solve$Cven
      solve$unit <- "uM"
    } else if (unique_scenarios$SAMPLING_MATRIX[i] == "ABL") {
      solve$simconc <- solve$Cart
      solve$unit <- "uM"
    } else if (unique_scenarios$SAMPLING_MATRIX[i] == "EB" |
      unique_scenarios$SAMPLING_MATRIX[i] == "EEB" | 
      unique_scenarios$SAMPLING_MATRIX[i] == "EB (+W)")
    {
      solve$simconc <- solve$Cendexh * 24.45
      solve$unit <- "ppm"
    } else if (unique_scenarios$SAMPLING_MATRIX[i] == "MEB") {
      solve$simconc <- solve$Cmixexh * 24.45
      solve$unit <- "ppm"
    } else if (unique_scenarios$SAMPLING_MATRIX[i] == "PL"){
      solve$simconc <- solve$Cplasma
      solve$unit <- "uM"
    } else {
      solve$simconc <- NA
      solve$unit <- NA
    }
    simlist[[i]] <- solve
    plot.data <- solve
    name1 <- paste0("c.vs.t",i)
#Right now this is only calculating real concentrations according to mg/L in blood
    plots <- assign(name1, ggplot(plot.data, aes(time*24, simconc)) + 
      geom_line() + 
      xlab("Time (h)") + 
      ylab(paste0("Simulated ", 
        unique_scenarios$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(text = element_text(size=10))+
      theme_bw()) 
    plist[[i]] <- plots
  #}, error = function(e){})
}
rm(list=ls(pattern='out'))
rm(list=ls(pattern='c.vs.t'))
```

Create a list to hold the combined observations and predictions for each scenario:
```{r init_dataset}
# Creation of simulated vs. observed concentration dataset
unique_scenarios$RSQD <- 0
unique_scenarios$RMSE <- 0
unique_scenarios$AIC <- 0
simobslist <- list()
obvpredlist <- list()
```
Merge the simulations and observations on the basis of simualation time:
```{r merge_datasets}
for(i in 1:length(simlist))
{
  obsdata <- as.data.frame(obslist[[i]])
  simdata <- as.data.frame(simlist[[i]])
# skips over anything for which there was no observed data or 
# insufficient information to run simulation:
  if (!is.null(simlist[[i]]) & !is.null(obslist[[i]]))
  { 
# Make sure we are looking at consistent time points:
    simobscomb <- simdata[simdata$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) ==
      "ORIG_CONC_U"] <- 
      "orig_conc_u"
    simobscomb <- suppressWarnings(merge(obsdata[,c(
      "time",
      "obstime",
      "obsconc",
      "chem",
      "dose",
      "explen",
      "species",
      "matrix",
      "mw",
      "orig_conc_u"
      )], simobscomb, by="time", all.x=T))

# 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 the appropriate matric (for example, exhaled breath) for each observation:
```{r match_matrix}
for(i in 1:length(simobslist))
  if (nrow(simobslist[[i]])>0)
  {
    simobscomb <- simobslist[[i]]
  # Match the matrix for each observation:    
    for (j in 1:nrow(simobscomb))
      if(!is.na(simobscomb$matrix[j]))
      {
        if (simobscomb$matrix[j] == "VBL" | 
            simobscomb$matrix[j] == "BL" | 
            simobscomb$matrix[j] == "BL (+W)")
        {
          simobscomb$simconc[j] <- simobscomb$Cven[j]
        } else if (simobscomb$matrix[j] == "ABL") {
          simobscomb$simconc[j] <- simobscomb$Cart[j]
        } else if (simobscomb$matrix[j] == "EB" | 
                   simobscomb$matrix[j] == "EEB" | 
                   simobscomb$matrix[j] == "EB (+W)") {
          simobscomb$simconc[j] <- simobscomb$Cendexh[j] * 24.45
        } else if (simobscomb$matrix[j] == "MEB") {
          simobscomb$simconc[j] <- simobscomb$Cendexh[j] * 24.45
        } else if (simobscomb$matrix[j] == "PL") {
          simobscomb$simconc[j] <- simobscomb$Cplasma[j]
        } else {
          simobscomb$simconc[j] <- NA
        }
      }
    simobslist[[i]] <- simobscomb
  }
```
Identify which quartile each observation occured in with respect to the latest (maximum)
observed time
```{r time_quartile, eval=F}
for(i in 1:length(simobslist))
  if (nrow(simobslist[[i]])>0)
  {
    simobscomb <- simobslist[[i]]
    for (j in 1:nrow(simobscomb))
    { 
      max.time <- max(simobscomb$time,na.rm=T)
      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}
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=T))
      {
        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}
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$simconc)^2) /
          nrow(simobscomb)))
      ) + ((44+1)*2) #44 is the number of parameters from inhalation_inits.R
    simobslist[[i]] <- simobscomb
  }
```
Make a plot for each scenario
```{r make_plots}
for(i in 1:length(simobslist))
  if (nrow(simobslist[[i]])>0)
  {
    simobscomb <- simobslist[[i]]
    obvpredplot <- ggplot(simobscomb, aes(x = simconc, y = obsconc)) + 
      geom_point() + 
      geom_abline() + 
      xlab("Simulated Concentrations (uM)") + 
      ylab("Observed Concentrations (uM)") + 
      ggtitle(paste0(
        unique_scenarios$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))
    obvpredlist[[i]] <- obvpredplot
  }

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))
```

### Creation of simulated concentration/time plots
```{r simCvtplots}
for (i in 1:length(plist))
{
  plist[[i]] <- plist[[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))
}
```

### Regressions
Other analytics including linear regression on overall concentration vs. time observed vs. predicted
```{r regression}
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 = T))
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 = T)
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$obsconc > 0])^2,
       na.rm=T) / 
     nrow(simobsfull[
       !is.na(simobsfull$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

### Concentration vs. time
```{r cvtplots, eval = F}
# Create pdfs of simulated concentration-time plots against observed c-t values
pdf("simdataplots.pdf")
for (i in 1:length(plist)) {
  print(plist[[i]])
}
dev.off()
```
```{r obsvspredplots, eval = F}
# Create pdfs of observed vs. predicted concentration plots
pdf("obvpredplots.pdf", width = 10, height = 10)
for (i in 1:length(obvpredlist)) {
  print(obvpredlist[[i]])
}
dev.off()
```

### Figure 2: overall observed vs. predicted plot
```{r obspredFig2}
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 = F, aes(color = 'Overall')) + 
  geom_smooth(method = 'lm', se = F, aes(color = species)) + 
  geom_text(
    x = Inf, 
    y = -Inf, 
    hjust = 1.05, 
    vjust = -0.15, 
#    size = 6, 
    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 = T,
#    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 = 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))
fig2 #Display plot in R
#pdf("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}
# Create data and run calculations for populating plots
cmaxfull <- subset(simobsfull, !duplicated(simobsfull$AUCobs) & simobsfull$Cmaxobs != 0)
cmaxlm <- lm(log10(cmaxfull$Cmaxobs)~log10(cmaxfull$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)
auclm <- lm(log10(aucfull$AUCobs)~log10(aucfull$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 = T))
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$AUCobs))^2, na.rm = T))
aucmiss <- nrow(aucfull[
  abs(log10(aucfull$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}
cmaxp <- ggplot(data = cmaxfull, aes(x = log10(Cmaxsim), y = log10(Cmaxobs))) +
  geom_point(color = 
    ifelse(abs(log10(cmaxfull$Cmaxsim)  -log10(cmaxfull$Cmaxobs))>=1, "red","black"))  +
  geom_abline()  + 
  xlab("Log(Simulated Max Concentration)") + 
  ylab("Log(Observed Max Concentration)") + 
  theme_bw() + 
  geom_smooth(method = 'lm', se = F, aes(color = 'Overall')) + 
  geom_smooth(method = 'lm', se = F, 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))>=1 & 
      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$Cmaxobs))>=1 &
      log10(cmaxfull$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))<=-1,], 
    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))>=1, "red","black"))  +
  geom_abline()  + 
  xlab("Log(Simulated AUC)") + 
  ylab("Log(Observed AUC)") + 
  theme_bw() + 
  geom_smooth(method = 'lm', se = F, aes(color = "Overall")) + 
  geom_smooth(method = 'lm', se = F, 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))>=1,], 
    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))<=-1,], 
    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
#pdf("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=F}
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 = F, 
    parse = F) +
  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
#pdf("Figure3.pdf", width = 40, height = 13)
#print(fig3)
#dev.off()
```

### Figures S1A-S1D: Separation by time quartile and physicochemical properties
```{r FigS1, eval = FALSE}
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
#pdf("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 = FALSE}
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)$r.squared,digits = 2)
  sensregrmse[i] <- round(sqrt(mean(lm$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 = T)), 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$Cmaxobs>0]))^2, na.rm = T))
  sensaucslope[i] <- round(summary(sensauclm)$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$AUCobs>0]))^2, na.rm = T))
}
sensitivitydf <- data.frame(Chemical <- as.character(senschem),
                            sensSlope <- as.numeric(sensslope),
                            sensRsq <- as.numeric(sensrsq),
                            sensRegrmse <- as.numeric(sensregrmse),
                            sensTotrmse <- as.numeric(senstotalrmse),
                            sensPmiss <- as.numeric(senspmiss),
                            sensCmaxslope <- as.numeric(senscmaxslope),
                            sensCmaxrsq <- as.numeric(senscmaxrsq),
                            sensCmaxrmse <- as.numeric(senstotalrmsecmax),
                            sensAUCslope <- as.numeric(sensaucslope),
                            sensAUCrsq <- as.numeric(sensaucrsq),
                            sensAUCrmse <- as.numeric(senstotalrmseauc),
                            stringsAsFactors=FALSE)
sensitivitydf <- subset(sensitivitydf,!duplicated(sensitivitydf$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 = F)
```

### Supplemental Table 1
```{r SupTable1, eval = FALSE}
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 = F)
```
