Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Calculate the Sum of Squared Error

Description

Calculate the Sum of Squared Error

Usage

SSE_GIA(par, data, GIA_fn = base_GIA, fn_list = NULL)

Arguments

Value

sum of square error between observed and estimated

Estimate GIA according to the base model

Description

Estimate GIA according to the base model

Usage

base_GIA(model_params, dose_A, dose_B, fn_list = NULL)

Arguments

Value

estimated GIA for each combination of dose A and dose B

Details

The equation is given in full as follows. The GIA (%) is given a as a function of the model parameters and the doses A_i and B_i, respectively. The doses scaled by the respective ED50s \beta_A and \beta_B are denoted by A_i^* and B_i^*, respectively. The parameters \gamma_A and \gamma_B are shape parameters. The parameters \tau_1 and \tau_2 are interaction parameters. Finally, \lambda_i is a weighted combination of dose A and dose B.

GIA_i = 100\%(1 - e^{-\psi_i})

\psi_i = \log(2)u_i^{v_i}

u_i = A^*_i + B_i^* + \tau_1 A^*_i B^*_i

v_i = \lambda_i \gamma_A + (1-\lambda_i) \gamma_B + \tau_1 \tau_2\lambda_i (1 - \lambda_i) \gamma_A \gamma_B

\lambda_i = \frac{A_i^*}{A_i^* + B_i^*}

A_i^* = A_i / \beta_A

B_i^* = B_i / \beta_B

Examples

model_params <- c("beta_A" = 1, "beta_B" = 2, "gamma_A" = .5,
"gamma_B" = .6,  "tau_1" = 1, "tau_2" = 0)
dose_A <- c(0, 1, 0)
dose_B <- c(0, 0, 1)
base_GIA(model_params, dose_A, dose_B)

Helper function for the bootstrap results

Description

Helper function for the bootstrap results

Usage

boot_GIA(
  par,
  gia_df,
  gia_est,
  n_boot = 100,
  alpha = 0.05,
  GIA_fn = base_GIA,
  S_fn = calc_S_base,
  fn_list = NULL,
  verbose = FALSE
)

Arguments

Value

a list with the following elements

• params_esta data frame of dimension # of params x 4 where each row in the data frame is a parameter and where the columns are the mean, lower, alpha/2 quantile,and upper,100 - alpha/2 quantile

• S_est a data frame of one row x 4 where we provide the mean, lower, and upper estimates

• GIA_estthe original data with additional columns of the mean, lower, and upper estimates for each dose combination

Calculate S generally

Description

Calculate S generally

Usage

calc_S(best_pars, S_fn = calc_S_base, fn_list = NULL)

Arguments

Value

Hewlett's S for the given model

Examples

best_pars <- c("tau_1" = 0,
              "tau_2" = 1,
              "gamma_A" = 1,
               "gamma_B" = 1)
calc_S_base(best_pars) # should be 1

Calculate S from given tau_1 for base model

Description

Calculate S from given tau_1 for base model

Usage

calc_S_base(best_pars, fn_list = NULL)

Arguments

Value

Hewlett's S for the base model.

Examples

best_pars <- c("tau_1" = 0,
              "tau_2" = 1,
              "gamma_A" = 1,
               "gamma_B" = 1)
calc_S_base(best_pars) # should be 1

CyRPA and RIPR

Description

The data is the raw data for a combination dose of CyRPA and RIPR.

well

one of iRBC (the max), uRBC (the min), RPMI (??), or comb (which is short for combination)

RIPR

dose of RIPR in mg/mL

CyRPA

dose of CyRPA in mg/mL

expxyrepz

the results from experiment x, sub item y, repetition z

Usage

cyrpa_ripr

Format

An object of class data.frame with 38 rows and 15 columns.

Examples

data("cyrpa_ripr")
head(cyrpa_ripr)

Helper function to generate code to run an experiment

Description

Helper function to generate code to run an experiment

Usage

design_experiment(
  levels_A = c(0, 1 * 2^(-4:2)),
  levels_B = c(0, 2 * 2^(-4:2)),
  par = c(beta_A = 1, beta_B = 2, gamma_A = 0.5, gamma_B = 0.5, tau_1 = 3, tau_2 = 0.05),
  n_rep = 1,
  n_sims = 100,
  noise_par = c(a0 = 3, a1 = 0.01)
)

Arguments

Details

prints out code to copy and paste into R to simulate the expected coverage of your experiment under your designed hypothesis

Function to design an experimental grid of combinations

Description

Function to design an experimental grid of combinations

Usage

design_grid(
  levels_A = c(0, 1 * 2^(-4:2)),
  levels_B = c(0, 2 * 2^(-4:2)),
  n_rep = 1
)

Arguments

Value

data frame with columns dose_A, dose_B, and GIA for all possible combinations

Take in dose A and dose B combinations and estimate GIA

Description

Take in dose A and dose B combinations and estimate GIA

Usage

estimate_GIA(model_params, dose_A, dose_B, fn = base_GIA, fn_list = NULL)

Arguments

Value

vector of the same size of dose_A and dose_B where each entry is the estimated GIA for the combination of dose A and dose B

Examples

model_params <- c("beta_A" = 1, "beta_B" = 2, "gamma_A" = .5,
"gamma_B" = .6,  "tau_1" = 1, "tau_2" = 0)
dose_A <- c(0, 1, 0)
dose_B <- c(0, 0, 1)
estimate_GIA(model_params, dose_A, dose_B)

Estimate the parameters for a given data set and model

Description

Estimate the parameters for a given data set and model

Usage

estimate_params(
  data,
  init_params = c(beta_A = 0.25, beta_B = 0.25, gamma_A = 0.5, gamma_B = 0.5, tau_1 = 0,
    tau_2 = 0),
  n_boot = 100,
  GIA_fn = base_GIA,
  S_fn = calc_S_base,
  fn_list = NULL,
  alpha = 0.05,
  verbose = FALSE
)

Arguments

Value

a list with the following elements

• params_esta data frame of dimension # of params x 4 where each row in the data frame is a parameter and where the columns are the mean, lower, alpha/2 quantile, and upper,100 - alpha/2 quantile

• S_est a data frame of one row x 4 where we provide the mean, lower, and upper estimates

• GIA_estthe original data with additional columns of the mean, lower, and upper estimates for each dose combination

• SSESum of Square Error for the model under the best (mean) parameters

Examples

df <- loewesadditivity::cyrpa_ripr
df$dose_A <- df$CyRPA
df$dose_B <- df$RIPR
data <- fortify_gia_data(df)
model_params <- c("beta_A" = .5, "beta_B" = .5,
                 "gamma_A" = .5, "gamma_B" = .5,
                 "tau_1" = 0, "tau_2" = 0)
n_boot <- 10
GIA_fn <- base_GIA
S_fn <- calc_S_base
fn_list <- NULL
alpha <- .05
verbose <- FALSE
out <- estimate_params(data = data,
init_params = model_params,
n_boot = n_boot,
GIA_fn = GIA_fn,
S_fn = S_fn,
fn_list = fn_list,
alpha = alpha,
verbose = verbose)
names(out)

Put GIA measurements into a dplyr format

Description

Put GIA measurements into a dplyr format

Usage

fortify_gia_data(data)

Arguments

Value

long data frame with columns well, dose_A, dose_B, plate, exp_num (experiment number), plate (a or b), rep_num (repetition number), gia_mean, and average iRBC and uRBC

Examples

df <- loewesadditivity::rh5_ama1ron2
df$dose_A <- df$RH5
df$dose_B <- df$AMA1RON2
fortified_df <- fortify_gia_data(df)
head(fortified_df)

Helper function to get the ED50 line

Description

Helper function to get the ED50 line

Usage

get_ed_line(
  grid_width = 50,
  par,
  GIA_fn = base_GIA,
  fn_list = NULL,
  ed_val = 50
)

Arguments

Value

data frame with the following columns

dose_A

dose of A (unscaled)

dose_B

dose of B (unscaled)

GIA

value of GIA %

Make a grid of points

Description

Make a grid of points

Usage

make_grid(n = 40, par, Amax = 2, Bmax = 2, n_reps = 1)

Arguments

Value

data frame with the following columns

dose_A

unscaled dose of A

dose_B

unscaled dose o B

rep

replicate number

Examples

n <- 40
 par <- c("beta_A" = 1, "beta_B" = 2)
out <- make_grid(n = 2, par = par)
exp_out <- data.frame(dose_A = c(0, 2, 0, 2),
                      dose_B = c(0, 0, 4, 4),
                      rep = 1)

Plot the surface and observations

Description

Plot the surface and observations

Usage

plot_curves(
  est_list,
  dose_A = "Dose A",
  dose_B = "Dose B",
  title = "Curves of Dose Combos",
  subtitle = "",
  base_size = 14
)

Arguments

Value

ggplot object

Examples

df <- loewesadditivity::cyrpa_ripr
df$dose_A <- df$CyRPA
df$dose_B <- df$RIPR
data <- fortify_gia_data(df)
model_params <- c("beta_A" = .5, "beta_B" = .5,
                 "gamma_A" = .5, "gamma_B" = .5,
                 "tau_1" = 0, "tau_2" = 0)
n_boot <- 10
GIA_fn <- base_GIA
S_fn <- calc_S_base
fn_list <- NULL
alpha <- .05
verbose <- FALSE
out <- estimate_params(data = data,
init_params = model_params,
n_boot = n_boot,
GIA_fn = GIA_fn,
S_fn = S_fn,
fn_list = fn_list,
alpha = alpha,
verbose = verbose)
plots <- plot_curves(out, dose_A = "CyRPA",
dose_B = "RIPR")

Plot the estimated isobologram

Description

Plot the estimated isobologram

Usage

plot_isobologram(
  est_list,
  dose_A = "Dose A",
  dose_B = "Dose B",
  GIA_fn = base_GIA,
  fn_list = NULL,
  title = "Isobologram Dose Combos",
  subtitle = "",
  base_size = 14
)

Arguments

Value

ggplot object

Examples

df <- loewesadditivity::cyrpa_ripr
df$dose_A <- df$CyRPA
df$dose_B <- df$RIPR
data <- fortify_gia_data(df)
model_params <- c("beta_A" = .5, "beta_B" = .5,
                 "gamma_A" = .5, "gamma_B" = .5,
                 "tau_1" = 0, "tau_2" = 0)
n_boot <- 10
GIA_fn <- base_GIA
S_fn <- calc_S_base
fn_list <- NULL
alpha <- .05
verbose <- FALSE
out <- estimate_params(data = data,
init_params = model_params,
n_boot = n_boot,
GIA_fn = GIA_fn,
S_fn = S_fn,
fn_list = fn_list,
alpha = alpha,
verbose = verbose)
plot_curves(out, dose_A = "CyRPA",
dose_B = "RIPR")

Plot the surface and observations

Description

Plot the surface and observations

Usage

plot_surface(
  est_list,
  GIA_fn = base_GIA,
  fn_list = NULL,
  xlab = "Dose A",
  ylab = "Dose B",
  title = "Surface Plot of Doses",
  subtitle = "",
  base_size = 14
)

Arguments

Value

ggplot object

Examples

df <- loewesadditivity::cyrpa_ripr
df$dose_A <- df$CyRPA
df$dose_B <- df$RIPR
data <- fortify_gia_data(df)
model_params <- c("beta_A" = .5, "beta_B" = .5,
                 "gamma_A" = .5, "gamma_B" = .5,
                 "tau_1" = 0, "tau_2" = 0)
n_boot <- 10
GIA_fn <- base_GIA
S_fn <- calc_S_base
fn_list <- NULL
alpha <- .05
verbose <- FALSE
out <- estimate_params(data = data,
init_params = model_params,
n_boot = n_boot,
GIA_fn = GIA_fn,
S_fn = S_fn,
fn_list = fn_list,
alpha = alpha,
verbose = verbose)
plot_surface(out)

RH5 and AMA1RON2

Description

The data is the raw data for a combination dose of RH5 and AMA1RON2. The data was collected by PEOPLE and on DATE on this GRANT.

Usage

rh5_ama1ron2

Format

a 38 x 15 data set where the columns are of the following format

well

one of iRBC (the max), uRBC (the min), RPMI (??), or comb (which is short for combination)

AMA1RON2

dose of AMA1RON2 in mg/mL

RH5

dose of RH5 in mg/mL

expxyrepz

the results from experiment x, sub item y, repetition z

Examples

data("rh5_ama1ron2")
head(rh5_ama1ron2)

RH5 and RH4

Description

The data is the raw data for a combination dose of RH5 and RH4. The data was originally published in Williams et al. (2018).

Usage

rh5_rh4

Format

a 48 x 3 data set where the columns are of the following format

RH4

dose of RH4 in mg/mL

RH5

dose of RH5 in mg/mL

GIA

Percent Growth inhibition assay averaged over two experiments

Examples

data("rh5_rh4")
head(rh5_rh4)

Simulate a GIA model with an assumed error structure

Description

Simulate a GIA model with an assumed error structure

Usage

simulate_coverage(
  n_sims = 10,
  n_boot = 100,
  verbose = TRUE,
  experimental_grid,
  model_par,
  alpha = 0.05,
  noise_par = c(a0 = 2, a1 = 0.01),
  GIA_fn = base_GIA,
  S_fn = calc_S_base,
  fn_list = NULL
)

Arguments

Value

list with the following entries

interaction_cov

This is the percent of times 0 was in the (1-alpha)% confidence interval for the interaction term "tau_1" from the simulated results

params_cov

This is the percent of times the true model parameter (those from model_par) lies in the (marginal) 95% confidence interval for that model parameter.

tau_pos

This is the percent of times the (1-alpha)% CI of "tau_1" was completely above 0.

tau_neg

This is the percent of times (1-alpha)% CI of "tau_1" is completely below zero

Examples

df <- loewesadditivity::cyrpa_ripr
df$dose_A <- df$CyRPA
df$dose_B <- df$RIPR
data <- fortify_gia_data(df)
model_params <- c("beta_A" = .247, "beta_B" = .224,
                  "gamma_A" = .734, "gamma_B" = .806,
                  "tau_1" = .28, "tau_2" = -.28)
experimental_grid <- make_grid(par = model_params,
                               n = 5)
n_boot <- 100
n_sims <- 10
GIA_fn <- base_GIA
S_fn <- calc_S_base
fn_list <- NULL
alpha <- .05
verbose <- TRUE
## NOT RUN
##out <- simulate_coverage(n_sims = n_sims,
  ##                      n_boot = n_boot,
   ##                     verbose = TRUE,
    ##                    experimental_grid = experimental_grid,
     ##                   model_par = model_params,
     ##                   alpha = .05,
   ##                     noise_par = c("a0" = 3, "a1" = .01),
   ##                     GIA_fn = base_GIA,
   ##                     fn_list = NULL)
##out