# Density plot for BNPdens class Extension of the `plot` method to the `BNPdens` class. The method `plot.BNPdens` returns suitable plots for a `BNPdens` object. See details. ```r ## 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. ## Returns A `ggplot2` object. ## 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. ## Examples ```r # 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) ```

plot.BNPdens function