Add Population Line(s) to an Existing Grid — plot

This function adds one (or multiple) linearized CDF(s) to an existing plot grid.

plot_pop(
  p_obj = NULL,
  x,
  dist_params_tbl,
  distribution = c("weibull", "lognormal", "loglogistic", "sev", "normal", "logistic",
    "exponential"),
  tol = 1e-06,
  title_trace = "Population",
  plot_method = c("plotly", "ggplot2")
)

Arguments

p_obj: A plot object to which the population line(s) is (are) added or NULL. If NULL the population line(s) is (are) plotted in an empty grid.
x: A numeric vector of length two or greater used for the x coordinates of the population line. If length(x) == 2 a sequence of length 200 between x[1] and x[2] is created. This sequence is equidistant with respect to the scale of the x axis. If length(x) > 2 the elements of x are the x coordinates of the population line.
dist_params_tbl: A data.frame. See 'Details'.
distribution: Supposed distribution of the random variable. The distinction between a threshold distribution and the respective standard variant is made with dist_params_tbl.
tol: The failure probability is restricted to the interval \([tol, 1 - tol]\). The default value is in accordance with the decimal places shown in the hover for plot_method = "plotly".
title_trace: A character string which is assigned to the legend trace.
plot_method: Package, which is used for generating the plot output. Only used when p_obj = NULL. If p_obj != NULL the plot object is used to determine the plot method.

Value

A plot object containing the linearized CDF(s).

Details

dist_params_tbl is a data.frame with parameter columns. An overview of the distribution-specific parameters and their order can be found in section 'Distributions'.

If only one population line should be displayed, a numeric vector is also supported. The order of the vector elements also corresponds to the table in section 'Distributions'.

Distributions

The following table summarizes the available distributions and their parameters

location parameter \(\mu\),
scale parameter \(\sigma\) or \(\theta\) and
threshold parameter \(\gamma\).

The column order within dist_params_tbl is given in the table header.

`distribution`	`dist_params_tbl[1]`	`dist_params_tbl[2]`	`dist_params_tbl[3]`
`"sev"`	\(\mu\)	\(\sigma\)	-
`"weibull"`	\(\mu\)	\(\sigma\)	(\(\gamma\))
`"normal"`	\(\mu\)	\(\sigma\)	-
`"lognormal"`	\(\mu\)	\(\sigma\)	(\(\gamma\))
`"logistic"`	\(\mu\)	\(\sigma\)	-
`"loglogistic"`	\(\mu\)	\(\sigma\)	(\(\gamma\))
`"exponential"`	\(\theta\)	(\(\gamma\))	-

Examples

x <- rweibull(n = 100, shape = 1, scale = 20000)

# Example 1 - Two-parametric straight line:
pop_weibull <- plot_pop(
  p_obj = NULL,
  x = range(x),
  dist_params_tbl = c(log(20000), 1),
  distribution = "weibull"
)

# Example 2 - Three-parametric curved line:
x2 <- rweibull(n = 100, shape = 1, scale = 20000) + 5000

pop_weibull_2 <- plot_pop(
  p_obj = NULL,
  x = x2,
  dist_params_tbl = c(log(20000 - 5000), 1, 5000),
  distribution = "weibull"
)

# Example 3 - Multiple lines:
pop_weibull_3 <- plot_pop(
  p_obj = NULL,
  x = x,
  dist_params_tbl = data.frame(
    p_1 = c(log(20000), log(20000), log(20000)),
    p_2 = c(1, 1.5, 2)
    ),
  distribution = "weibull",
  plot_method = "ggplot2"
)

# Example 4 - Compare two- and three-parametric distributions:
pop_weibull_4 <- plot_pop(
  p_obj = NULL,
  x = x,
  dist_params_tbl = data.frame(
    param_1 = c(log(20000), log(20000)),
    param_2 = c(1, 1),
    param_3 = c(NA, 2)
  ),
  distribution = "weibull"
)