plot.metadiag {bamdit}

Generic plot function for metadiag object in bamdit

Description

This function plots the observe data in the ROC (Receiving Operating Charachteristics) space with the posterior predictive contours. The predictive curves are approximated using a non-parametric smoother or with a parametric model. For the parametric model the current implementation supports only a logistic link function. The marginal posterior predictive distributions are ploted outside the ROC space.

Usage

## S3 method for class 'metadiag'
plot(
  x,
  parametric.smooth = TRUE,
  level = c(0.5, 0.75, 0.95),
  limits.x = c(0, 1),
  limits.y = c(0, 1),
  kde2d.n = 25,
  color.line = "red",
  title = paste("Posterior Predictive Contours (50%, 75% and 95%)"),
  marginals = TRUE,
  bin.hist = 30,
  color.hist = "lightblue",
  S = 500,
  color.pred.points = "lightblue",
  color.data.points = "blue",
  ...
)

Arguments

x

The object generated by the metadiag function.

parametric.smooth

Indicates if the predictive curve is a parametric or non-parametric.

level

Credibility levels of the predictive curve. If parametric.smooth = FALSE, then the probability levels are estimated from the nonparametric surface.

limits.x

Numeric vector of length 2 specifying the x-axis limits. The default value is c(0, 1).

limits.y

Numeric vector of length 2 specifying the x-axis limits. The default value is c(0, 1).

kde2d.n

The number of grid points in each direction for the non-parametric density estimation. Can be scalar or a length-2 inter vector.

color.line

Color of the predictive contour line.

title

Optional parameter for setting a title in the plot.

marginals

Plot the posterior marginal predictive histograms.

bin.hist

Number of bins of the marginal histograms.

color.hist

Color of the histograms.

S

Number of predictive rates to be plotted.

color.pred.points

Color of the posterior predictive rates.

color.data.points

Color of the data points.

...

...

See Also

metadiag.

Examples

## Not run: 
library(bamdit)
data("glas")
glas.t <- glas[glas$marker == "Telomerase", 1:4]
glas.m1 <- metadiag(glas.t,                # Data frame
                    re = "normal",         # Random effects distribution
                    re.model = "DS",       # Random effects on D and S
                    link = "logit",        # Link function
                    sd.Fisher.rho   = 1.7, # Prior standard deviation of correlation
                    nr.burnin = 1000,      # Iterations for burnin
                    nr.iterations = 10000, # Total iterations
                    nr.chains = 2,         # Number of chains
                    r2jags = TRUE)         # Use r2jags as interface to jags


 plot(glas.m1,                    # Fitted model
      level = c(0.5, 0.75, 0.95), # Credibility levels
      parametric.smooth = TRUE)   # Parametric curve

# Plot results: based on a non-parametric smoother of the posterior predictive rates .......

plot(glas.m1,                    # Fitted model
     level = c(0.5, 0.75, 0.95), # Credibility levels
     parametric.smooth = FALSE)  # Non-parametric curve

# Using the pipe command in the package dplyr and changing some colors .......

library(dplyr)

glas.t %>%
 metadiag(re = "normal", re.model ="SeSp") %>%
   plot(parametric.smooth = FALSE,
          S = 100,
          color.data.points = "green",
          color.pred.points = "blue",
          color.line = "black")


## End(Not run)