plot.BNPdens {BNPmix}

Density plot for BNPdens class

Description

Extension of the plot method to the BNPdens class. The method plot.BNPdens returns suitable plots for a BNPdens object. See details.

Usage

## S3 method for class 'BNPdens'
plot(
  x,
  dimension = c(1, 2),
  col = "#0037c4",
  show_points = F,
  show_hist = F,
  show_clust = F,
  bin_size = NULL,
  wrap_dim = NULL,
  xlab = "",
  ylab = "",
  band = T,
  conf_level = c(0.025, 0.975),
  ...
)

Arguments

x

an object of class BNPdens;

dimension

if x is the output of a model fitted to multivariate data, dimensions specifies the two dimensions for the bivariate contour plot (if they are equal, a marginal univarite plot is returned);

col

the color of the lines;

show_points

if TRUE, the function plots also the observations, default FALSE;

show_hist

if TRUE, and the model is univariate, the function plots also the histogram of the data, default FALSE;

show_clust

if TRUE the function plots also the points colored with respect to the estimated partition, default FALSE;

bin_size

if show_hist = TRUE, it correponds to the size of each bin, default range(data) / 30;

wrap_dim

bivariate vector, if x is the output of DDPdensity, it correponds to the number of rows and columns in the plot. Default c(ngroup, 1);

xlab

label of the horizontal axis;

ylab

label of the vertical axis;

band

if TRUE and x is the output of a univariate model or of DDPdensity, the plot method displays quantile-based posterior credible bands along with estimated densities;

conf_level

bivariate vector, order of the quantiles for the posterior credible bands. Default c(0.025, 0.975);

...

additional arguments to be passed.

Details

If the BNPdens object is generated by PYdensity, the function returns the univariate or bivariate estimated density plot. If the BNPdens object is generated by PYregression, the function returns the scatterplot of the response variable jointly with the covariates (up to four), coloured according to the estimated partition. up to four covariates. If x is a BNPdens object generated by DDPdensity, the function returns a wrapped plot with one density per group. The plots can be enhanced in several ways: for univariate densities, if show_hist = TRUE, the plot shows also the histogram of the data; if show_points = TRUE, the plot shows also the observed points along the x-axis; if show_points = TRUE and show_clust = TRUE, the points are colored according to the partition estimated with the partition function. For multivariate densities: if show_points = TRUE, the plot shows also the scatterplot of the data; if show_points = TRUE and show_clust = TRUE, the points are colored according to the estimated partition.

Value

A ggplot2 object.

Examples

# PYdensity example
data_toy <- c(rnorm(100, -3, 1), rnorm(100, 3, 1))
grid <- seq(-7, 7, length.out = 50)
est_model <- PYdensity(y = data_toy,
 mcmc = list(niter = 200, nburn = 100, nupd = 100),
 output = list(grid = grid))
class(est_model)
plot(est_model)

# PYregression example
x_toy <- c(rnorm(100, 3, 1), rnorm(100, 3, 1))
y_toy <- c(x_toy[1:100] * 2 + 1, x_toy[101:200] * 6 + 1) + rnorm(200, 0, 1)
grid_x <- c(0, 1, 2, 3, 4, 5)
grid_y <- seq(0, 35, length.out = 50)
est_model <- PYregression(y = y_toy, x = x_toy,
mcmc = list(niter = 200, nburn = 100),
output = list(grid_x = grid_x, grid_y = grid_y))
summary(est_model)
plot(est_model)

# DDPdensity example
data_toy <- c(rnorm(50, -4, 1), rnorm(100, 0, 1), rnorm(50, 4, 1))
group_toy <- c(rep(1,100), rep(2,100))
grid <- seq(-7, 7, length.out = 50)
est_model <- DDPdensity(y = data_toy, group = group_toy,
mcmc = list(niter = 200, nburn = 100, napprox_unif = 50),
output = list(grid = grid))
summary(est_model)
plot(est_model)