### Data Setup and Curve Fitting for Positive Response Example

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Sets up example concentration-response data and fits all available models using tcplfit2_core. Displays all fitted curves, both on linear and log concentration scales.

```R
# This is taken from the example under tcplfit2_core
conc_ex2 <- c(0.03, 0.1, 0.3, 1, 3, 10, 30, 100)
resp_ex2 <- c(0, 0.1, 0, 0.2, 0.6, 0.9, 1.1, 1)

# fit all available models in the package
# show all fitted curves 
output_ex2 <- tcplfit2_core(conc_ex2, resp_ex2, 0.8)
# arrange the ggplot2 output into a grid
grid.arrange(plot_allcurves(output_ex2, conc_ex2, resp_ex2),
             plot_allcurves(output_ex2, conc_ex2, resp_ex2, log_conc = TRUE),
             ncol = 2)
```

--------------------------------

### Example Data Setup for concRespCore

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Prepares a named list 'row' with concentration and response data, along with noise estimates, required for the concRespCore function.

```R
conc <- list(.03, .1, .3, 1, 3, 10, 30, 100)
resp <- list(0, .2, .1, .4, .7, .9, .6, 1.2)
row <- list(conc = conc,
            resp = resp,
            bmed = 0,
            cutoff = 1,
            onesd = .5,
            name = "some chemical",
            assay = "some assay")

```

--------------------------------

### Load and Prepare Example Data

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Loads the 'mc0' dataset and sets up thread usage for data.table. This is the initial step for data processing.

```R
data("mc0")
data.table::setDTthreads(2)
dat <- mc0
```

--------------------------------

### Load and Inspect Example Data

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Loads the 'mc3' example dataset, which contains processed concentration-response data. Displays the first few rows to show the structure, including chemical identifiers, concentrations, and responses.

```R
# read in the data
# Loading in the level 3 example data set from invitrodb stored in tcplfit2
  data("mc3")

# view the first 6 rows of the mc3 data
# dtxsid = unique chemical identifier from EPA's DSSTox Database 
# casrn = unique chemical identifier from Chemical Abstracts Service 
# name = chemical name 
# spid = sample id 
# logc = log_10 concentration value 
# resp = response 
# assay = assay name 
  head(mc3)

```

--------------------------------

### Install Development Version

Source: https://cran.r-project.org/web/packages/tcplfit2/readme/README.html

Installs the current development version of the tcplfit2 package from GitHub. Ensure you have the devtools package installed.

```r
devtools::install_github("USEPA/CompTox-ToxCast-tcplfit2")
```

--------------------------------

### Power Model Example

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Example usage of the pow function with sample parameters and concentration.

```R
pow(c(1,2),1)
```

--------------------------------

### Prepare Example Data for tcplfit2

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Extracts and prepares sample concentration-response data from the tcplfit2 package signatures. It also calculates the fit and hitcalling information using core functions.

```R
DATA_CASE <- tcplfit2::signatures[1,]
conc <- strsplit(DATA_CASE[,"conc"],split = "[|]") %>% 
  unlist() %>% as.numeric()
resp <- strsplit(DATA_CASE[,"resp"],split = "[|]") %>% 
  unlist() %>% as.numeric()
OG_data <- data.frame(xval = conc,yval = resp) %>%
  # obtain the concentrations that are outside the cutoff band
  dplyr::mutate(type = ifelse(abs(resp)>=abs(DATA_CASE[,"cutoff"] Поэтому "Extreme Responses",NA)) %>%
  mutate(.,df = "OG_data")

# obtain the fit and best fitting/hitcalling information
fit <- tcplfit2::tcplfit2_core(conc = conc,resp = resp,
                               cutoff = DATA_CASE[,"cutoff"])
hit <- tcplfit2::tcplhit2_core(params = fit,
                               conc = conc,resp = resp,
                               cutoff = DATA_CASE[,"cutoff"],
                               onesd = DATA_CASE[,"onesd"])
```

--------------------------------

### Polynomial 2 Model Example

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Example usage of the poly2 function with sample parameters and concentration.

```R
poly2(c(1,2),1)
```

--------------------------------

### Load and Prepare Example Data for BMD Analysis

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Loads sample data from the 'mc3' dataset and preprocesses it for concentration-response modeling. It back-transforms log-transformed concentrations and prepares a list object containing experimental data and metadata.

```R
# load example data
spid <- unique(mc3$spid)[26]
ex_df <- mc3[is.element(mc3$spid,spid)]

# The data file has stored concentration in log10 form, so fix that
conc <- 10^ex_df$logc # back-transforming concentrations on log10 scale
resp <- ex_df$resp

# pull out all of the chemical identifiers and the name of the assay
dtxsid <- ex_df[1,"dtxsid"]
casrn <- ex_df[1,"casrn"]
name <- ex_df[1,"name"]
assay <- ex_df[1,"assay"]

# create the row object
row_up <- list(conc = conc, resp = resp, bmed = 0, cutoff = cutoff, onesd = onesd,assay=assay,
            dtxsid=dtxsid,casrn=casrn,name=name)

# run the concentration-response modeling for a single sample
res_up <- concRespCore(row_up,fitmodels = c("cnst", "hill", "gnls", "poly1", "poly2", 
                                         "pow", "exp2", "exp3", "exp4", "exp5"), 
                       bidirectional=F)
# plotting the results
max_conc <- max(conc)
concRespPlot2(res_up, log_conc = T) + 
  # geom_vline(aes(xintercept = max(log10(conc))),lty = "dashed")+
  geom_vline(aes(xintercept = log10(max_conc)),lty = "dashed")+
  geom_rect(aes(xmin = log10(res_up[1, "bmdl"]),
                xmax = log10(res_up[1, "bmdu"]),ymin = 0,ymax = 125),
            alpha = 0.05,fill = "skyblue") + 
  geom_segment(aes(x = log10(res_up[, "bmd"]), 
                   xend = log10(res_up[, "bmd"]), y = 0, 
                   yend = 125),col = "blue")+ 
  ggtitle(label = paste(name,"-",assay),subtitle = dtxsid)

```

--------------------------------

### Polynomial 2 Model (BMDS) Example

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Example usage of the poly2bmds function with sample parameters and concentration.

```R
poly2bmds(c(1,2),1)
```

--------------------------------

### Polynomial 1 Model Example

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Example usage of the poly1 function with sample parameters and concentration.

```R
poly1(1,1)
```

--------------------------------

### Calculate AUC Example

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Demonstrates calculating AUC using sample concentration-response data, fitting, hit-calling, and the post_hit_AUC function.

```R
conc <- c(.03, .1, .3, 1, 3, 10, 30, 100)
resp <- c(0, .2, .1, .4, .7, .9, .6, 1.2)
params <- tcplfit2_core(conc, resp, .8)
output <- tcplhit2_core(params, conc, resp, 0.8, 0.5)
post_hit_AUC(output)
```

--------------------------------

### Run Curve Fitting with tcplfit2_core

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

This snippet shows how to load example data, extract concentration and response values, and run the tcplfit2_core function for curve fitting. It also displays the structure of the output.

```R
# Load the example data set
data("signatures")

# using the first row of signature as an example 
conc <- as.numeric(str_split(signatures[1,"conc"],"\\|")[[1]])
resp <- as.numeric(str_split(signatures[1,"resp"],"\\|")[[1]])
cutoff <- signatures[1,"cutoff"]

# run curve fitting
output <- tcplfit2_core(conc, resp, cutoff)
# show the structure of the output 
summary(output)
```

--------------------------------

### Hill Function Example

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Example of using the hillfn function with sample concentration and parameter values.

```R
hillfn(c(1,2,3),1)


```

--------------------------------

### Load Primary and Data Formatting Packages

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Loads the necessary R packages for tcplfit2 functionality, data manipulation, and table formatting. Ensure these packages are installed before running.

```r
# Primary Packages #
library(tcplfit2)
library(tcpl)
# Data Formatting Packages #
library(data.table)
library(DT)
library(htmlTable)
library(dplyr)
library(stringr)
# Plotting Packages #
library(ggplot2)
library(gridExtra)
```

--------------------------------

### Setup and fit negative response curves

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Prepares data for negative response curve fitting, fits multiple models using tcplfit2_core, and generates plots of all fitted curves. This is used to visualize monotonic decreasing responses.

```R
# use row 5 in the data
conc <- as.numeric(str_split(signatures[5,"conc"],"\|")[[1]])
resp <- as.numeric(str_split(signatures[5,"resp"],"\|")[[1]])
cutoff <- signatures[5,"cutoff"]

# plot all models, this is an example of negative curves 
output_negative <- tcplfit2_core(conc, resp, cutoff)
grid.arrange(plot_allcurves(output_negative, conc, resp),
          plot_allcurves(output_negative, conc, resp, log_conc = TRUE), ncol = 2)
```

--------------------------------

### Add Binary Hitcall Column to Data

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

This example demonstrates adding a binary hitcall column to a data frame. The output format is similar to the result_table in tcplfit2_core and tcplhit2_core, suitable for plotting with concRespPlot2.

```r
TP0001366A07  |  0  |  49.2830638452227  |  gnls  |  0.25480547525408  |  8.86452058842676  |  |  |  -12.5575945136781  |  7.98842631852464  |  7.9999986953221  |  0.0167893679648239  |  3.91623536169883  |  1.89813253015055  |  33.9132650800971  |  |  |  0.00148372909623819  |  -85.8405179284853  |  0  |  0.0167893674048174  |  3.91623536237081  |  -12.5575945050592  |  |  |  |  |  |  |  33.3|33.3|11.1|11.1|3.7|3.7|1.2|1.2|0.412|0.412|0.138|0.138|33.3|33.3|11.1|11.1|3.7|3.7|1.2|1.2|0.412|0.412|0.138|0.138  |  4.11243543988432|20.7326387429006|14.7529451483177|7.10911496582029|-1.76036826257897|-4.59509043954118|-6.99430833999997|-16.3638605847648|-3.6587758270851|-24.2421016154744|-11.1072892792031|-16.4019756012561|-23.1836231299777|1.20158114007354|11.3527779597309|7.25486119401818|-15.6678034542428|-9.75250675357766|-5.5003115908377|-13.0995926623172|-14.8552472512966|-13.4613706078389|-14.086358968162|-11.5213652234997  |  dt4  |  0 
TP0001366A08  |  0  |  49.2830638452227  |  gnls  |  0.440022924941614  |  7.45675844926627  |  |  |  -29.8245869314839  |  7.98596120556591  |  0.353240646376545  |  0.083649130537124  |  2.64521777979504  |  1.86978547363224  |  33.9132650800971  |  |  |  2.45711096575871e-07  |  -80.0457583161288  |  0  |  0.0824132481469447  |  12.9099530041626  |  -21.6856779032592  |  |  |  |  |  |  |  33.3|33.3|11.1|11.1|3.7|3.7|1.2|0.412|0.412|0.138|0.138|33.3|33.3|11.1|11.1|3.7|3.7|1.2|1.2|0.412|0.412|0.138|0.138  |  -9.97203526140537|-9.26141749522581|-8.93154426626026|-17.7216363630617|-0.544184525361571|-19.4566738752308|-7.72856440998552|-19.1592648493029|-20.1683266913997|-14.6279698557126|-24.5517214338418|-6.07777157463346|-16.2381999212206|-4.93900677916975|-19.3878430509545|-8.9921646264341|-8.68643323910698|-2.75155451162059|-19.2249765364499|-33.1882205045543|-29.3179448006381|-16.9564267035365|-33.6131504865745  |  dt4  |  0 
```

--------------------------------

### Prepare Concentration Data for Model Demonstrations

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Sets up two concentration ranges that will be used in subsequent visualizations to demonstrate the effect of parameter changes on model curves.

```R
# prepare concentration data for demonstration
ex_conc <- seq(0, 100, length.out = 500)
ex2_conc <- seq(0, 3, length.out = 100)
```

--------------------------------

### Setting up Input Data for concRespCore

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Prepare the necessary input data, including concentrations, responses, and baseline parameters, before calling the concRespCore function.

```R
# tested concentrations
  conc <- list(.03,.1,.3,1,3,10,30,100)
# observed responses at respective concentrations
  resp <- list(0,.2,.1,.4,.7,.9,.6, 1.2)
# row object with relevant parameters
  row = list(conc = conc,resp = resp,bmed = 0,cutoff = 1,onesd = 0.5,name="some chemical")

```

--------------------------------

### Setting up Data for Lower BMD Bound Demonstration

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

This R code prepares data for demonstrating how to impose a lower bound on BMD estimates. It loads the 'mc3' dataset, calculates background variation, and filters the data to simulate a lack of low-dose experimental points.

```r
# We'll use data from mc3 in this section
data("mc3")

# determine the background variation
# background is defined per the assay.  In this case we use logc <= -2
# However, background should be defined in a way that makes sense for your application
temp <- mc3[mc3$logc<= -2,"resp"]
bmad <- mad(temp)
onesd <- sd(temp)
cutoff <- 3*bmad

# load example data
spid <- unique(mc3$spid)[94]
ex_df <- mc3[is.element(mc3$spid,spid),]

# The data file has stored concentration in log10 form, fix it 
conc <- 10^ex_df$logc # back-transforming concentrations on log10 scale
resp <- ex_df$resp

# modify the data for demonstration purposes 
conc2 <- conc[conc>0.41]
resp2 <- resp[which(conc>0.41)]

# pull out all of the chemical identifiers and the name of the assay
dtxsid <- ex_df[1,"dtxsid"]
casrn <- ex_df[1,"casrn"]
name <- ex_df[1,"name"]
assay <- ex_df[1,"assay"]

# create the row object
row_low <- list(conc = conc2, resp = resp2, bmed = 0, cutoff = cutoff, onesd = onesd,
            assay=assay, dtxsid=dtxsid,casrn=casrn,name=name)
```

--------------------------------

### Prepare Data and Run Concentration-Response Modeling with concRespCore

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

This snippet illustrates how to prepare a 'row' object with necessary data and run the concRespCore function for concentration-response modeling. It's a precursor to plotting the winning model.

```R
# prepare the 'row' object for concRespCore
row <- list(conc=conc,
           resp=resp,
           bmed=0,
           cutoff=cutoff,
           onesd=signatures[1,"onesd"],
           name=signatures[1,"name"],
           assay=signatures[1,"signature"])

# run concentration-response modeling 
out <-  concRespCore(row,conthits=F)
```

--------------------------------

### Simulate and Plot Biphasic Poly2 Curve

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Simulates a bi-phasic concentration-response curve using the poly2 model and plots the result. This code is used to generate example data for AUC estimation.

```R
# simulate a poly2 curve
conc_sim <- seq(0,3, length.out = 100)
## biphasic poly2 parameters
b1 <- -1.3
b2 <- 0.7
## converted to tcplfit2's poly2 parameters
a <- b1^2/b2
b <- b1/b2
c(a,b)
#> [1]  2.414286 -1.857143
## plot the curve
resp_sim <- poly2(c(a, b, 0.1), conc_sim)
plot(conc_sim, resp_sim, type = "l",
     xlab = "Concentration",ylab = "Response",main = "Biphasic Response")
abline(h = 0)
```

--------------------------------

### Calculate Top Likelihood Probabilities

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Demonstrates how to use the toplikelihood function with different cutoff values. Ensure all required parameters are correctly defined before calling.

```R
fname = "hillfn"
conc = c(.03,.1,.3,1,3,10,30,100)
resp = c(0,.1,0,.2,.6,.9,1.1,1)
ps = c(1.033239, 2.453014, 1.592714, er = -3.295307)
top = 1.023239
mll = 12.71495
toplikelihood(fname, cutoff = .8, conc, resp, ps, top, mll)
toplikelihood(fname, cutoff = 1, conc, resp, ps, top, mll)
toplikelihood(fname, cutoff = 1.2, conc, resp, ps, top, mll)
```

--------------------------------

### Get absolute AUC for negative response

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Demonstrates how to obtain the absolute value of the AUC for a negative response curve by setting the 'return.abs' argument to TRUE in the get_AUC function. This converts negative AUC values to positive.

```R
get_AUC(fit_method, min(conc), max(conc), modpars, return.abs = TRUE)
```

--------------------------------

### Prepare Results for Comparison

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Adds calculated minimum concentration and a descriptive name to the results object. It then rounds relevant BMD columns and combines these results with previous output for comparison.

```R
# print out the new results
# add to previous results for comparison 
res_low3['Min. Conc.'] <- min(conc2)
res_low3['Name'] <- paste("Chlorothalonil", "after `bounding` (two fifths)", sep = "-")
res_low3[1, c("Min. Conc.", "bmd", "bmdl", "bmdu")] <- round(res_low3[1, c("Min. Conc.", "bmd", "bmdl", "bmdu")], 3)

output_low <- rbind(output_low[-3, ], 
                    res_low3[1, c('Name', "Min. Conc.", "bmd", "bmdl", "bmdu")])
```

--------------------------------

### Concise Response Core Output with AUC

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Demonstrates the output of concRespCore when conthits and AUC are set to TRUE, showing various fit metrics and AUC.

```R
concRespCore(row, conthits = TRUE, AUC = TRUE)
```

--------------------------------

### Perform Concentration-Response Modeling and Hitcalling

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

This snippet iterates through a list of samples, performs concentration-response modeling using tcplfit2_core, generates plots of the fits, and then uses tcplhit2_core to identify hits. It demonstrates data preparation, model fitting, and result aggregation.

```R
temp <- mc3[mc3$logc<= -2,"resp"] # obtain response in the two lowest concentrations
bmad <- mad(temp) # obtain the baseline median absolute deviation
onesd <- sd(temp) # obtain the baseline standard deviation
cutoff <- 3*bmad  # estimate the cutoff, use the typical cutoff=3*BMAD

spid.list <- unique(mc3$spid)
spid.list <- spid.list[1:6]

model_fits <- NULL
result_table <- NULL
plt_lst <- NULL

for(spid in spid.list) {
  # select the data for just this sample
  temp <- mc3[is.element(mc3$spid,spid),]

  # The data file stores concentrations in log10 units, so back-transform to "raw scale"
  conc <- 10^temp$logc
  # Save the response values
  resp <- temp$resp

  # pull out all of the chemical identifiers and the assay name
  dtxsid <- temp[1,"dtxsid"]
  casrn <- temp[1,"casrn"]
  name <- temp[1,"name"]
  assay <- temp[1,"assay"]
  
  # Execute curve fitting
  # Input concentrations, responses, cutoff, a list of models to fit, and other model fitting requirements
  # force.fit is set to true so that all models will be fit regardless of cutoff
  # bidirectional = FALSE indicates only fit models in the positive direction.
  # if using bidirectional = TRUE the coff only needs to be specified in the positive direction.
  model_fits[[spid]] <- tcplfit2_core(conc, resp, cutoff, force.fit = TRUE, 
                                      fitmodels = c("cnst", "hill", "gnls", 
                                                    "poly1", "poly2", "pow", 
                                                    "exp2","exp3", "exp4", "exp5"),
                                      bidirectional = FALSE)
  # Get a plot of all curve fits
  plt_lst[[spid]] <- plot_allcurves(model_fits[[spid]], 
                                    conc = conc, resp = resp, log_conc = TRUE)
  
  # Pass the output from 'tcplfit2_core' to 'tcplhit2_core' along with
  # cutoff, onesd, and any identifiers
  out <- tcplhit2_core(model_fits[[spid]], conc, resp, bmed = 0,
                       cutoff = cutoff, onesd = onesd, 
                       identifiers = c(dtxsid = dtxsid, casrn = casrn, 
                                       name = name, assay = assay))
  # store all results in one table
  result_table <- rbind(result_table,out)
}
```

--------------------------------

### Prepare Data for Exp4 Parameter Visualization in R

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Creates a data frame with different response values for the Exponential 4 model by varying the 'tp' and 'ga' parameters. This data is intended for subsequent plotting.

```r
fits_exp4 <- data.frame(
  # change tp  
  y1 = exp4(ps = c(tp = 895,ga = 15),x = ex_conc),
  y2 = exp4(ps = c(tp = 200,ga = 15),x = ex_conc),
  y3 = exp4(ps = c(tp = -500,ga = 15),x = ex_conc),
  
  # change ga
  y4 = exp4(ps = c(tp = 500,ga = 0.4),x = ex_conc),
  y5 = exp4(ps = c(tp = 500,ga = 10),x = ex_conc),
  y6 = exp4(ps = c(tp = 500,ga = 20),x = ex_conc)
  
)
```

--------------------------------

### Displaying BMD Results Before and After Bounding in R

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

This code prepares data for comparison by adding results before and after applying the lower bound BMD bounding. It includes the minimum concentration, BMD, BMDL, and BMDU for both scenarios.

```R
# print out the new results
# include previous results side by side for comparison 
res_low2['Min. Conc.'] <- min(conc2)
res_low2['Name'] <- paste(name, "after `bounding`", sep = "-")
res_low['Name'] <- paste(name, "before `bounding`", sep = "-")
res_low2[1, c("Min. Conc.", "bmd", "bmdl", "bmdu")] <- round(res_low2[1, c("Min. Conc.", "bmd", "bmdl", "bmdu")], 3)

output_low <- rbind(res_low[1, c('Name', "Min. Conc.", "bmd", "bmdl", "bmdu")], 
                    res_low2[1, c('Name', "Min. Conc.", "bmd", "bmdl", "bmdu")])
```

--------------------------------

### Running Concentration-Response Modeling with Lower BMD Bound

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

This snippet executes the concentration-response modeling using concRespCore with specified models and bidirectional=FALSE. It then prepares for plotting the results, highlighting the minimum concentration, BMD estimate, and its confidence interval.

```r
# run the concentration-response modeling for a single sample
res_low <- concRespCore(row_low,fitmodels = c("cnst", "hill", "gnls", "poly1", "poly2", 
                                          "pow", "exp2", "exp3", "exp4", "exp5"), 
                        bidirectional=F)
# plotting the results
min_conc <- min(conc2)
concRespPlot2(res_low, log_conc = T) + 
  geom_vline(aes(xintercept = log10(min_conc)),lty = "dashed")+ 
  geom_rect(aes(xmin = log10(res_low[1, "bmdl"]),
                xmax = log10(res_low[1, "bmdu"]),ymin = 0,ymax = 30),
            alpha = 0.05,fill = "skyblue") + 
  geom_segment(aes(x = log10(res_low[, "bmd"]),
                   xend = log10(res_low[, "bmd"]), y = 0, 
                   yend = 30),col = "blue")+ 
  ggtitle(label = paste(name,"-",assay),subtitle = dtxsid)
```

--------------------------------

### Plot All Model Fits with plot_allcurves

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Demonstrates using the plot_allcurves function with the output from tcplfit2_core to visualize all included model fits. It shows how to generate plots in both original and log-10 concentration scales and arrange them in a grid.

```R
# get plots in the original and in log-10 concentration scale
basic <- plot_allcurves(output, conc, resp)
basic_log <- plot_allcurves(output, conc, resp, log_conc = T)
# arrange the ggplot2 output into a grid
grid.arrange(basic, basic_log)
```

--------------------------------

### Tune Benchmark Dose (BMD) Bounds

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Tunes the upper and lower bounds for Benchmark Dose (BMD) using a maximum likelihood method. Requires fit method, benchmark risk (bmr), model parameters, concentration, response, and optionally a pre-calculated BMD value.

```R
bmdbounds(
  fit_method,
  bmr,
  pars,
  conc,
  resp,
  onesidedp = 0.05,
  bmd = NULL,
  which.bound = "lower",
  poly2.biphasic = TRUE,
  x_v
)
```

--------------------------------

### Visualize Concentration-Response Modeling Results

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

This snippet demonstrates how to visualize the results of concentration-response modeling for tcpl-like data. It assumes the data is in a format compatible with the concRespPlot2 function.

```r
# The following code chunk demonstrates how to visualize the Concentration-Response Modeling for `tcpl`-like data without a database connection fit results.
```

--------------------------------

### Compare BMD Results Before and After Upper Bounding

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Combines and rounds the results from before and after applying the upper BMD bound (`bmd_up_bnd`) for direct comparison. This highlights the effect of the bounding on BMD and its confidence interval.

```R
# print out the new results
# include previous results side by side for comparison 
res_up2['Max Conc.'] <- max(conc)
res_up2['Name'] <- paste(name, "after `bounding`", sep = "-")
res_up['Name'] <- paste(name, "before `bounding`", sep = "-")
res_up2[1, c("Max Conc.", "bmd", "bmdl", "bmdu")] <- round(res_up2[1, c("Max Conc.", "bmd", "bmdl", "bmdu")], 3)

output_up <- rbind(res_up[1, c('Name', "Max Conc.", "bmd", "bmdl", "bmdu")],
                   res_up2[1, c('Name', "Max Conc.", "bmd", "bmdl", "bmdu")])

```

--------------------------------

### Apply Upper BMD Bound with bmd_up_bnd

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Re-runs the concentration-response modeling using the `bmd_up_bnd` argument to set an upper threshold for the benchmark dose (BMD). This is useful when the estimated BMD exceeds the maximum tested concentration.

```R
# using bmd_up_bnd = 2
res_up2 <- concRespCore(row_up,fitmodels = c("cnst", "hill", "gnls", "poly1", "poly2", 
                                          "pow", "exp2", "exp3", "exp4", "exp5"), 
                        bidirectional=F, bmd_up_bnd = 2)
#> Warning in fitpoly2(conc = c(0.0199999999999999, 0.3, 0.0599999999999995, : The
#> `bidirectional` argument is ignored when `biphasic = TRUE`.

```

--------------------------------

### Empirically Calculate Top Concentration

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Estimates the maximum response (top) and the concentration at which it occurs by sampling points within the experimental range. Requires concentration, parameters, and model function name.

```R
conc = c(.03,.1,.3,1,3,10,30,100)
ps = c(1,2,1,2,2)
fname = "gnls"
calcempirical_top(conc, ps, fname)
```

--------------------------------

### Plotting Dose-Response Curves and BMD Estimates

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Generates a dose-response plot, marking maximum concentration, boundary dose, and estimated BMD with confidence intervals before and after bounding. This is useful for visualizing fitting results and the impact of bounding on BMD estimation.

```R
# generate some concentration for the fitting curve 
logc_plot <- seq(from=-3,to=2,by=0.05)
conc_plot <- 10^logc_plot

# initiate plot
plot(conc,resp,xlab="conc (uM)",ylab="Response",xlim=c(0.001,500),ylim=c(-5,150),
       log="x",main=paste(name,"\n",assay),cex.main=0.9)
# add vertical lines to mark the maximum concentration in the data and the upper boundary set by bmd_up_bnd
abline(v=max(conc), lty = 1, col = "brown", lwd=2)
abline(v=160, lty = 2, col = "darkviolet", lwd=2)

# add marker for BMD and its boundaries before `bounding`
lines(c(res_up$bmd,res_up$bmd),c(0,125),col="green",lwd=2)
rect(xleft=res_up$bmdl,ybottom=0,xright=res_up$bmdu,ytop=125,col=rgb(0,1,0, alpha = .5), border = NA)
points(res_up$bmd, -0.5, pch = "x", col = "green")

# add marker for BMD and its boundaries after `bounding`
lines(c(res_up2$bmd,res_up2$bmd),c(0,125),col="blue",lwd=2)
rect(xleft=res_up2$bmdl,ybottom=0,xright=res_up2$bmdu,ytop=125,col=rgb(0,0,1, alpha = .5), border = NA)
points(res_up2$bmd, -0.5, pch = "x", col = "blue")

# add the fitting curve
lines(conc_plot, poly1(ps = c(res_up$a), conc_plot))
legend(1e-3, 150, legend=c("Maximum Dose Tested", "Boundary", "BMD-before", "BMD-after"),
       col=c("brown", "darkviolet", "green", "blue"), lty=c(1,2,1,1))
```

--------------------------------

### Gain-Loss Plot for 'q' Parameter

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Visualizes the effect of changing the 'q' (loss power) parameter on the Gain-Loss model curve. Uses pre-computed 'fits_gnls' data.

```R
gnls_plot2 <- ggplot(fits_gnls, aes(log10(ex_conc))) +  
  geom_line(aes(y = y4, color = "q=0.3")) + 
  geom_line(aes(y = y5, color = "q=1.2")) + 
  geom_line(aes(y = y6, color = "q=8")) + 
  labs(x = "Concentration in Log-10 Scale", y = "Response") + 
  theme_bw()+
  theme(legend.position = c(0.15,0.8)) + 
  scale_color_manual(name='q values',
                     breaks=c('q=0.3', 'q=1.2', 'q=8'),
                     values=c('q=0.3'='black', 'q=1.2'='red', 'q=8'='blue'))
```

--------------------------------

### Perform Hitcalling and Bounding with tcplhit2_core

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Performs hitcalling and applies BMD bounding using the `tcplhit2_core` function. This snippet demonstrates how to use the `bmd_low_bnd` argument to replicate bounding results achieved with `concRespCore`.

```R
hit_res <- tcplhit2_core(param, conc2, resp2, cutoff = cutoff, onesd = onesd, 
                         bmd_low_bnd = 0.8)
```

--------------------------------

### Augmenting Output with tcplhit2_core Results

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Adds the results from `tcplhit2_core` to an output table for comparison. This involves naming the results and rounding relevant numerical columns before binding.

```R
# adding the result from tcplhit2_core to the output table for comparison
hit_res["Name"]<-  paste("Chlorothalonil", "tcplhit2_core", sep = "-")
hit_res['Min. Conc.'] <- min(conc2)
hit_res[1, c("Min. Conc.", "bmd", "bmdl", "bmdu")] <- round(hit_res[1, c("Min. Conc.", "bmd", "bmdl", "bmdu")], 3)

output_low <- rbind(output_low, 
                    hit_res[1, c('Name', "Min. Conc.", "bmd", "bmdl", "bmdu")])
```

--------------------------------

### Gain-Loss Model

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Implements the Gain-Loss dose-response model, defined by the equation f(x)=tp/[(1 + (ga/x)^p )(1 + (x/la)^q )]. It takes model parameters and concentration values as input.

```APIDOC
## Gain-Loss Model

### Description
`f(x)=tp/[(1 + (ga/x)^p )(1 + (x/la)^q )]`

### Usage
```
gnls(ps, x)
```

### Arguments
`ps` |  Vector of parameters: tp,ga,p,la,q,er
`x` |  Vector of concentrations (regular units)

### Value
Vector of model responses

### Examples
```
gnls(c(1,2,1,2,2),1)
```
```

--------------------------------

### exp5

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Calculates the response for the Exponential 5 model given parameters and concentrations.

```APIDOC
## Exponential 5 Model

### Description
`f(x)=tp∗(1−2(−(x/ga)p))f(x) = tp*(1-2^{(-(x/ga)^p)})f(x)=tp∗(1−2(−(x/ga)p))`

### Usage
```
exp5(ps, x)
```

### Arguments
`ps` |  Vector of parameters: tp,ga,p,er  
`x` |  Vector of concentrations (regular units)  

### Value
Vector of model responses 

### Examples
```
exp5(c(1,2,3),1)
```
```

--------------------------------

### Run Core Hitcalling Function

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

This function selects the winning model, extracts key parameters like top and ac50, computes hitcalls, and calculates BMD statistics. It uses nested model selection and requires tcplfit2_core to be run with force.fit = TRUE for continuous hitcalls.

```R
tcplhit2_core(
  params,
  conc,
  resp,
  cutoff,
  onesd,
  bmr_scale = 1.349,
  bmed = 0,
  conthits = TRUE,
  aicc = FALSE,
  identifiers = NULL,
  bmd_low_bnd = NULL,
  bmd_up_bnd = NULL,
  poly2.biphasic = TRUE,
  verbose = FALSE
)
```

--------------------------------

### Create Reference Lines Dataframe

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Creates a dataframe containing reference lines for plotting, such as cutoff, BMR, and top response.

```R
ref_df <- data.frame(
  xval = rep(0,6),
  yval = c(hit$cutoff*c(-1,1),
           hit$bmr*c(-1,1),
           fit$exp4$top,
           hit$cutoff),
  type = c(rep("Cutoff",2),rep("BMR",2),"Top","Top at Cutoff")
) %>%
  mutate(.,df = "ref_df")
```

--------------------------------

### Basic Concentration-Response Plotting

Source: https://cran.r-project.org/web/packages/tcplfit2/refman/tcplfit2.html

Generates a concentration-response plot using provided data and a fitted model. Requires pre-defined 'row' data structure.

```R
conc <- list(.03, .1, .3, 1, 3, 10, 30, 100)
resp <- list(0, .2, .1, .4, .7, .9, .6, 1.2)
row <- list(conc = conc,
            resp = resp,
            bmed = 0,
            cutoff = 0.25,
            onesd = 0.125,
            name = "some chemical",
            assay = "some assay")
res <- concRespCore(row, conthits = TRUE)
concRespPlot(res,ymin=-2.5,ymax=2.,5)
```

--------------------------------

### Plotting All Models with concRespCore

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Fits concentration-response models for each observation in the `signatures` dataset and generates plots of all curve fits using the `do.plot=TRUE` argument in `concRespCore`. Requires the `signatures` dataset to be loaded.

```R
# read in the file
data("signatures")

# set up a 3 x 2 grid for the plots
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar))
par(mfrow=c(3,2),mar=c(4,4,5,2))
    
# fit 6 observations in signatures
for(i in 1:nrow(signatures)){
  # set up input data
  row = list(conc=as.numeric(str_split(signatures[i,"conc"],"\\|")[[1]]),
             resp=as.numeric(str_split(signatures[i,"resp"],"\\|")[[1]]),
             bmed=0,
             cutoff=signatures[i,"cutoff"],
             onesd=signatures[i,"onesd"],
             name=signatures[i,"name"],
             assay=signatures[i,"signature"])
  # run concentration-response modeling (1st plotting option)
  out = concRespCore(row,conthits=F,do.plot=T)
  if(i==1){
    res <- out
  }else{
    res <- rbind.data.frame(res,out)
  }
}
```

--------------------------------

### Visualize Exp2 Parameter Changes in R

Source: https://cran.r-project.org/web/packages/tcplfit2/vignettes/tcplfit2-vignette.html

Generates plots to illustrate the effect of changing parameters 'a' and 'b' on the Exponential 2 dose-response curve. Requires the ggplot2 and gridExtra packages.

```r
fits_exp2 <- data.frame(
  # change a
  y1 = exp2(ps = c(a = 20,b = 12), x = ex2_conc),
  y2 = exp2(ps = c(a = 9,b = 12), x = ex2_conc),
  y3 = exp2(ps = c(a = 0.1,b = 12), x = ex2_conc),
  y4 = exp2(ps = c(a = -3,b = 12), x = ex2_conc),
  
  # change b
  y5 = exp2(ps = c(a = 0.45,b = 4), x = ex2_conc),
  y6 = exp2(ps = c(a = 0.45,b = 9), x = ex2_conc),
  y7 = exp2(ps = c(a = 0.45,b = 20), x = ex2_conc)
  
)

# shows how changes in parameter 'a' affect the shape of the curve 
exp2_plot1 <- ggplot(fits_exp2, aes(ex2_conc)) +  
  geom_line(aes(y = y1, color = "a=20")) +
  geom_line(aes(y = y2, color = "a=9")) +
  geom_line(aes(y = y3, color = "a=0.1")) +
  geom_line(aes(y = y4, color = "a=(-3)")) +
  labs(x = "Concentration", y = "Response") +
  theme_bw()+
  theme(legend.position = c(0.15,0.8)) +
  scale_color_manual(name='a values',
                     breaks=c('a=(-3)', 'a=0.1', 'a=9', 'a=20'),
                     values=c('a=(-3)'='black', 'a=0.1'='red', 'a=9'='blue', 'a=20'='darkviolet'))

# shows how changes in parameter 'b' affect the shape of the curve 
exp2_plot2 <- ggplot(fits_exp2, aes(ex2_conc)) +  
  geom_line(aes(y = y5, color = "b=4")) +
  geom_line(aes(y = y6, color = "b=9")) +
  geom_line(aes(y = y7, color = "b=20")) +
  labs(x = "Concentration", y = "Response") +
  theme_bw()+
  theme(legend.position = c(0.15,0.8)) +
  scale_color_manual(name='b values',
                     breaks=c('b=4', 'b=9', 'b=20'),
                     values=c('b=4'='black', 'b=9'='red', 'b=20'='blue'))

grid.arrange(exp2_plot1, exp2_plot2, ncol = 2)
```