plot.modelFits: plot.modelFits

In BayesianMCPMod: Simulate, Evaluate, and Analyze Dose Finding Trials with Bayesian MCPMod

plot.modelFits

Description

Plot function based on the ggplot2 package. Providing visualizations for each model and a average Fit. Black lines show the fitted dose response models and an AIC based average model. Dots indicate the posterior median and vertical lines show corresponding credible intervals (i.e. the variability of the posterior distribution of the respective dose group). To assess the uncertainty of the model fit one can in addition visualize credible bands (default coloring as orange shaded areas). The calculation of these bands is performed via the getBootstrapQuantiles() function. The default setting is that these credible bands are not calculated.

Usage

## S3 method for class 'modelFits'
plot(
  x,
  gAIC = TRUE,
  avg_fit = TRUE,
  cr_intv = TRUE,
  alpha_CrI = 0.05,
  cr_bands = FALSE,
  alpha_CrB = c(0.05, 0.5),
  n_bs_smpl = 1000,
  acc_color = "orange",
  ...
)

Arguments

x	An object of type modelFits
gAIC	Logical value indicating whether gAIC values are shown in the plot. Default TRUE
avg_fit	Logical value indicating whether average fit is presented in the plot. Default TRUE
cr_intv	Logical value indicating whether credible intervals are included in the plot. Default TRUE
alpha_CrI	Numerical value of the width of the credible intervals. Default is set to 0.05 (i.e 95% CI are shown).
cr_bands	Logical value indicating whether bootstrapped based credible bands are shown in the plot. Default FALSE
alpha_CrB	Numerical vector of the width of the credible bands. Default is set to 0.05 and 0.5 (i.e 95% CB and median are shown).
n_bs_smpl	Number of bootstrap samples being used. Default set to 1000.
acc_color	Color of the credible bands. Default set to "orange"
...	optional parameter to be passed.

Value

A ggplot2 object

Examples

posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2),
                       DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2),
                       DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) ,  
                       DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) ,
                       DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2))
models <- c("exponential", "linear")
dose_levels <- c(0, 1, 2, 4, 8)
fit <- getModelFits(models      = models,
                    posterior   = posterior_list,
                    dose_levels = dose_levels,
                    simple      = TRUE)
                           
plot(fit) 

BayesianMCPMod documentation built on May 29, 2024, 9:14 a.m.