```
# S3 method for p
plot(x, y = NULL, n_extra_grid = 1001, ...)
# S3 method for d
plot(x, y = NULL, n_extra_grid = 1001, ...)
# S3 method for q
plot(x, y = NULL, n_extra_grid = 1001, ...)
# S3 method for r
plot(x, y = NULL, n_sample = 1000, ...)
# S3 method for p
lines(x, n_extra_grid = 1001, ...)
# S3 method for d
lines(x, n_extra_grid = 1001, ...)
# S3 method for q
lines(x, n_extra_grid = 1001, ...)
```

x | Pdqr-function to plot. |
---|---|

y | Argument for compatibility with |

n_extra_grid | Number of extra grid points at which to evaluate
pdqr-function (see Details). Supply |

... | Other arguments for |

n_sample | Size of a sample to be generated for plotting histogram in case of an r-function. |

Output of invisible() without arguments, i.e.
`NULL`

without printing.

Main idea of plotting pdqr-functions is to use plotting mechanisms for appropriate numerical data.

Plotting of type **discrete** functions:

P-functions are plotted as step-line with jumps at points of "x" column of "x_tbl" metadata.

D-functions are plotted with vertical lines at points of "x" column of "x_tbl" with height equal to values from "prob" column.

Q-functions are plotted as step-line with jumps at points of "cumprob" column of "x_tbl".

R-functions are plotted by generating sample of size

`n_sample`

and calling hist() function.

Plotting of type **continuous** functions:

P-functions are plotted in piecewise-linear fashion at their values on compound grid: sorted union of "x" column from "x_tbl" metadata and sequence of length

`n_extra_grid`

consisting from equidistant points between edges of support. Here extra grid is needed to show curvature of lines between "x" points from "x_tbl" (see Examples).D-functions are plotted in the same way as p-functions.

Q-functions are plotted similarly as p- and d-functions but grid consists from union of "cumprob" column of "x_tbl" metadata and equidistant grid of length

`n_extra_grid`

from 0 to 1.R-functions are plotted the same way as type "discrete" ones: as histogram of generated sample of size

`n_sample`

.

Other pdqr methods for generic functions:
`methods-group-generic`

,
`methods-print`

```
# Usage of `n_extra_grid` is important in case of "continuous" p- and
# q-functions
simple_p <- new_p(data.frame(x = c(0, 1), y = c(0, 1)), "continuous")
plot(simple_p, main = "Case study of n_extra_grid argument")
```