Version:	0.1.9
Title:	Emax Model Analysis with 'Stan'
Description:	Perform sigmoidal Emax model fit using 'Stan' in a formula notation, without writing 'Stan' model code.
Encoding:	UTF-8
License:	GPL-3 | file LICENSE
LazyData:	true
ByteCompile:	true
Depends:	R (≥ 3.5.0), methods,Rcpp (≥ 1.0.0)
Imports:	rstan (≥ 2.26.0), rstantools (≥ 2.3.0), magrittr (≥ 1.5), dplyr (≥ 0.8.0), tidyr (≥ 1.0.0), purrr (≥ 0.3.0), ggplot2 (≥ 2.2.1), posterior, lifecycle
LinkingTo:	StanHeaders (≥ 2.26.0), rstan (≥ 2.26.0), BH (≥ 1.69.0-1), Rcpp (≥ 1.0.0), RcppEigen (≥ 0.3.3.5.0), RcppParallel (≥ 5.0.2)
SystemRequirements:	GNU make
NeedsCompilation:	yes
RoxygenNote:	7.3.2
Suggests:	testthat, knitr, rmarkdown, spelling, tibble, withr, tidybayes
VignetteBuilder:	knitr
Language:	en-US
URL:	https://github.com/yoshidk6/rstanemax, https://yoshidk6.github.io/rstanemax/
BugReports:	https://github.com/yoshidk6/rstanemax/issues
Packaged:	2025-02-17 06:44:26 UTC; yoshidk6
Author:	Kenta Yoshida [aut, cre], Danielle Navarro [aut], Trustees of Columbia University [cph] (src/init.cpp, tools/make_cc.R, R/stanmodels.R)
Maintainer:	Kenta Yoshida <yoshida.kenta.6@gmail.com>
Repository:	CRAN
Date/Publication:	2025-02-17 07:30:01 UTC

The 'rstanemax' package.

Description

Perform sigmoidal Emax model fit using Stan without writing Stan model code.

Author(s)

Maintainer: Kenta Yoshida yoshida.kenta.6@gmail.com (ORCID)

Authors:

• Danielle Navarro djnavarro@protonmail.com (ORCID)

Other contributors:

• Trustees of Columbia University (src/init.cpp, tools/make_cc.R, R/stanmodels.R) [copyright holder]

References

Stan Development Team (2018). RStan: the R interface to Stan. R package version 2.18.2. http://mc-stan.org

See Also

Useful links:

• https://github.com/yoshidk6/rstanemax

• https://yoshidk6.github.io/rstanemax/

• Report bugs at https://github.com/yoshidk6/rstanemax/issues

Convert stanemax object to a posterior draws object

Description

Convert stanemax object to a posterior draws object

Usage

as_draws(x, ...)

as_draws_list(x, ...)

as_draws_array(x, ...)

as_draws_df(x, ...)

as_draws_matrix(x, ...)

as_draws_rvars(x, ...)

## S3 method for class 'stanemax'
as_draws(x, inc_warmup = FALSE, ...)

## S3 method for class 'stanemaxbin'
as_draws(x, inc_warmup = FALSE, ...)

## S3 method for class 'stanemax'
as_draws_list(x, inc_warmup = FALSE, ...)

## S3 method for class 'stanemaxbin'
as_draws_list(x, inc_warmup = FALSE, ...)

## S3 method for class 'stanemax'
as_draws_array(x, inc_warmup = FALSE, ...)

## S3 method for class 'stanemaxbin'
as_draws_array(x, inc_warmup = FALSE, ...)

## S3 method for class 'stanemax'
as_draws_df(x, inc_warmup = FALSE, ...)

## S3 method for class 'stanemaxbin'
as_draws_df(x, inc_warmup = FALSE, ...)

## S3 method for class 'stanemax'
as_draws_matrix(x, inc_warmup = FALSE, ...)

## S3 method for class 'stanemaxbin'
as_draws_matrix(x, inc_warmup = FALSE, ...)

## S3 method for class 'stanemax'
as_draws_rvars(x, inc_warmup = FALSE, ...)

## S3 method for class 'stanemaxbin'
as_draws_rvars(x, inc_warmup = FALSE, ...)

Arguments

Value

A draws object of the appropriate subclass

See Also

draws subset_draws

Examples

## Not run: 
data(exposure.response.sample)
fit <- stan_emax(response ~ exposure, exposure.response.sample)
posterior::as_draws_list(fit)
posterior::as_draws_array(fit)
posterior::as_draws_df(fit)
posterior::as_draws_matrix(fit)
posterior::as_draws_rvars(fit)

## End(Not run)

Sample simulated data for exposure-response.

Description

Sample simulated data for exposure-response.

Usage

exposure.response.sample

Format

A data frame with columns:

dose

Dose levels used for simulation of pharmacokinetics

exposure

Simulated exposure

response

Simulated pharmacodynamic response

Examples

exposure.response.sample

Sample simulated data for exposure-response for binary endpoint

Description

Sample simulated data for exposure-response for binary endpoint

Usage

exposure.response.sample.binary

Format

A data frame with columns:

conc

Simulated exposure

y_logit

Simulated logit(p)

y_prob

Simulated p

y

Simulated event (1 or 0)

y_cov_logit

Simulated logit(p) for model with covariate

y_cov_prob

Simulated p for model with covariate

y_cov

Simulated event (1 or 0) for model with covariate

sex_num

1 or 0

sex

female or male

Examples

exposure.response.sample

Sample simulated data for exposure-response with covariates

Description

Sample simulated data for exposure-response with covariates

Usage

exposure.response.sample.with.cov

Format

A data frame with columns:

dose

Dose levels used for simulation of pharmacokinetics

conc

Simulated exposure

resp

Simulated pharmacodynamic response

cov1

Covariate 1 for e0

cov2

Covariate 2 for emax

cov3

Covariate 3 for ec50 (data type factor)

cov3num

Covariate 3 for ec50 (data type numeric)

Examples

exposure.response.sample

Extract posterior draws of key parameters

Description

Extract posterior draws of key parameters

Usage

extract_param(object)

Arguments

Value

A tibble containing posterior draws of key parameters. If covariate(s) are included in the model, posterior draws for different combinations of covariates are supplied in a long format - e.g. if there are posterior draws of 100 samples and 4 levels of the covariates, the returned tibble will have the length of 400

Calculate log-likelihoods from posterior samples. Data can be either original data used for model fit or new data.

Description

See rstantools::log_lik for more details.

Usage

log_lik(object, ...)

## S3 method for class 'stanemax'
log_lik(object, newdata = NULL, ...)

## S3 method for class 'stanemaxbin'
log_lik(object, newdata = NULL, ...)

Arguments

Value

S by N matrix of log-likelihoods, where each row corresponds to a draw from the posterior distribution and each column corresponds to a data point.

Outcome prediction from posterior distribution of parameters

Description

Compute outcome predictions using posterior samples. Exposure data for prediction can be either original data used for model fit or new data.

Usage

posterior_predict(object, ...)

posterior_epred(object, ...)

posterior_linpred(object, transform = FALSE, ...)

## S3 method for class 'stanemax'
posterior_predict(
  object,
  newdata = NULL,
  returnType = "matrix",
  newDataType = "raw",
  ...
)

## S3 method for class 'stanemaxbin'
posterior_predict(
  object,
  newdata = NULL,
  returnType = "matrix",
  newDataType = "raw",
  ...
)

## S3 method for class 'stanemax'
posterior_epred(object, newdata = NULL, newDataType = "raw", ...)

## S3 method for class 'stanemaxbin'
posterior_epred(object, newdata = NULL, newDataType = "raw", ...)

## S3 method for class 'stanemax'
posterior_linpred(
  object,
  transform = FALSE,
  newdata = NULL,
  newDataType = "raw",
  ...
)

## S3 method for class 'stanemaxbin'
posterior_linpred(
  object,
  transform = FALSE,
  newdata = NULL,
  newDataType = "raw",
  ...
)

posterior_predict_quantile(
  object,
  newdata = NULL,
  ci = 0.9,
  pi = 0.9,
  newDataType = c("raw", "modelframe")
)

Arguments

Details

Run vignette("emaxmodel", package = "rstanemax") to see how you can use the posterior prediction for plotting estimated Emax curve.

Value

An object that contain predicted response with posterior distribution of parameters. The default is a matrix containing predicted response for stan_emax() and .epred for stan_emax_binary(). Each row of the matrix is a vector of predictions generated using a single draw of the model parameters from the posterior distribution.

If either dataframe or tibble is specified, the function returns a data frame or tibble object in a long format - each row is a prediction generated using a single draw of the model parameters and a corresponding exposure.

Several types of predictions are generated with this function.

For continuous endpoint model (stan_emax()),

• .linpred & .epred: prediction without considering residual variability and is intended to provide credible interval of "mean" response.

• .prediction: include residual variability in its calculation, therefore the range represents prediction interval of observed response.

• respHat: replaced by .linpred and .epred

• response: replaced by .prediction

For binary endpoint model (stan_emax_binary()),

• .linpred: predicted probability on logit scale

• .epred: predicted probability on probability scale

• .prediction: predicted event (1) or non-event (0)

  The return object also contains exposure and parameter values used for calculation.

With posterior_predict_quantile() function, you can obtain quantiles of respHat and response as specified by ci and pi.

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

magrittr

%>%

Bayesian Emax model fit with Stan

Description

Run sigmoidal Emax model fit with formula notation

Usage

stan_emax(
  formula,
  data,
  gamma.fix = 1,
  e0.fix = NULL,
  emax.fix = NULL,
  priors = NULL,
  param.cov = NULL,
  ...
)

Arguments

Details

The following structure is used for the Emax model:

Response = e_0 + e_{max} \times exposure ^{\gamma} / (ec50 ^{\gamma} + exposure ^ {\gamma}) + \epsilon

\epsilon \sim N(0, \sigma^2)

Value

An object of class stanemax

Examples

## Not run: 
data(exposure.response.sample)
fit1 <- stan_emax(response ~ exposure,
  data = exposure.response.sample,
  # the next line is only to make the example go fast enough
  chains = 1, iter = 500, seed = 12345
)
print(fit1)

# Set priors manually, also estimate gamma instead of the default of fix to 1
fit2 <- stan_emax(response ~ exposure,
  data = exposure.response.sample, gamma.fix = NULL,
  priors = list(
    ec50 = c(100, 30), emax = c(100, 30), e0 = c(10, 5),
    gamma = c(0, 3), sigma = c(0, 30)
  ),
  # the next line is only to make the example go fast enough
  chains = 1, iter = 500, seed = 12345
)
print(fit2)

data(exposure.response.sample.with.cov)
# Specify covariates
fit3 <- stan_emax(
  formula = resp ~ conc, data = exposure.response.sample.with.cov,
  param.cov = list(emax = "cov2", ec50 = "cov3", e0 = "cov1"),
  # the next line is only to make the example go fast enough
  chains = 1, iter = 500, seed = 12345
)
print(fit3)

## End(Not run)

Bayesian Emax model fit with Stan for binary endpoint

Description

Bayesian Emax model fit with Stan for binary endpoint

Usage

stan_emax_binary(
  formula,
  data,
  gamma.fix = 1,
  e0.fix = NULL,
  emax.fix = NULL,
  priors = NULL,
  param.cov = NULL,
  ...
)

Arguments

Examples

## Not run: 
data(exposure.response.sample.binary)
fit1 <- stan_emax_binary(
  y ~ conc,
  data = exposure.response.sample.binary,
  # the next line is only to make the example go fast enough
  chains = 2, iter = 500, seed = 12345
)
print(fit1)

# Specify covariates
fit2 <- stan_emax_binary(
  formula = y ~ conc, data = exposure.response.sample.binary,
  param.cov = list(emax = "sex"),
  # the next line is only to make the example go fast enough
  chains = 2, iter = 500, seed = 12345
)
print(fit2)

## End(Not run)

Methods for stanemax objects

Description

Methods for stanemax objects

Usage

## S3 method for class 'stanemax'
print(x, digits_summary = 2, ...)

## S3 method for class 'stanemaxbin'
print(x, digits_summary = 2, ...)

extract_stanfit(x)

extract_obs_mod_frame(x)

## S3 method for class 'stanemax'
plot(x, show.ci = TRUE, show.pi = FALSE, ci = 0.9, pi = 0.9, ...)

## S3 method for class 'stanemaxbin'
plot(x, show.ci = TRUE, show.pi = FALSE, ci = 0.9, pi = 0.9, ...)

Arguments