View source: R/class_generic.R
plot.BNPdens | R Documentation |
Extension of the plot
method to the BNPdens
class. The method plot.BNPdens
returns suitable plots for a BNPdens
object. See details.
## 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), ... )
x |
an object of class |
dimension |
if |
col |
the color of the lines; |
show_points |
if |
show_hist |
if |
show_clust |
if |
bin_size |
if |
wrap_dim |
bivariate vector, if |
xlab |
label of the horizontal axis; |
ylab |
label of the vertical axis; |
band |
if |
conf_level |
bivariate vector, order of the quantiles for the posterior credible bands. Default |
... |
additional arguments to be passed. |
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.
A ggplot2
object.
# 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)
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