duos_plot: Plot PDF or CDF
In reykp/BEDr: What the Package Does in One 'Title Case' Line

Description Usage Arguments Details Value Examples

Plots the posterior mean PDF or CDF based on the output from duos.

1	duos_plot(duos_output, type = "pdf", burnin = NA, cri = FALSE, data = FALSE, interact = FALSE)

`duos_output`	The list returned by `duos` containing the density estimate results.
`type`	The desired type of graph (see details).
`burnin`	The desired burnin to discard from the results. If no value is entered, the default is half the number of iterations.
`cri`	An option to include credible intervals. The default is FALSE.
`data`	An option to include data in the graph (see details).
`interact`	An option to make the plots interactive. The default is FALSE.

Options for type

The input from duos can be used to plot a Bayesian estimate of the PDF or the CDF.

"pdf": The density, f(x), is calculated at a grid of 999 points between 0 and 1 at each iteration. The density at each grid point is then averaged across the iterations to produce the results in the graph (DEFAULT).
"cdf": The CDF, F(x), is calculated at a grid of 999 points between 0 and 1 at each iteration. The CDF at each grid point is then averaged across the iterations to produce the results in the graph.

Options for cri

Credible intervals can also be added to the plot of the PDF or CDF.

"FALSE": No credible intervals lines are plotted (DEFAULT).
"TRUE": Credible interval lines are plotted in red on the PDF or CDF. These are calculated by taking the 0.025th and 0.975th quantiles of the iterations from duos after burnin.

Options for data

Incorporates the data into the PDF and CDF.

"FALSE": The data is not included (DEFAULT).
"TRUE": If the PDF is plotted, a histogram is overlaid with the density estimate. If the CDF is plotted, the empirical CDF is overlaid with the duos CDF estimate.

Options for interact

Provides the PDF or CDF plot in an interactive setting using plotly.

"FALSE": No interactivity (DEFAULT).
"TRUE": Allows for zooming in on locations and running mouse over plot to see PDF or CDF values.

A plot of the PDF or CDF estimate.

## --------------------------------------------------------------------------------
## Uniform Distribution
## --------------------------------------------------------------------------------

# First run 'duos' on data sampled from a Uniform(0,1) distribution with 50 data points.
y <- runif(50)
duos_unif <- duos(y = y, k = 4, MH_N = 20000)

# Plot the PDF with all default values
duos_plot(duos_unif)

# Plot the PDF with the data overlayed
duos_plot(duos_unif, data = TRUE)

# Plot the CDF with the credible intervals and the empirical CDF
duos_plot(duos_unif, type="cdf", cri=TRUE)

## --------------------------------------------------------------------------------
## Beta Distribution
## --------------------------------------------------------------------------------

# First run 'duos' on data sampled from a Beta(0.5,0.5) distribution with 300 data points.
y <- rbeta(300, 0.5, 0.5)
duos_arcsin <- duos(y, k = 10, MH_N = 20000)

# Plot the PDF with the data in an interactive setting
duos_plot(duos_arcsin, type = "pdf", data = TRUE, interact = TRUE)

# Plot the CDF with the empirical cdf
duos_plot(duos_arcsin, type = "cdf", data = TRUE)

## --------------------------------------------------------------------------------
## Bimodal Distribution
## --------------------------------------------------------------------------------

# Sample 150 random uniforms
u  <- runif(150)
y <- rep(NA,150)
# Sampling from the mixture
for(i in 1:150){
  if(u[i] < 0.3){
   y[i]  <-  rnorm(1, 0, 1)
  }else {
   y[i] <- rnorm(1, 4, 1)
  }
}

# First run 'duos' on data sampled from a bimodal distribution with 150 data points.
duos_bimodal <- duos(y, k = 8, MH_N = 20000, scale_l = 0.5*sd(y), scale_u = 0.5*sd(y))

# Plot the PDF with the data and credible intervals
duos_plot(duos_bimodal, data = TRUE, cri = TRUE)

#Plot the PDF with credible intervals and a histogram of the data
duos_plot(duos_bimodal, cri=TRUE, data=TRUE)

# Plot the CDF interactively
duos_plot(duos_bimodal, type = "cdf", interact = TRUE)