plot_sim_cdf: Plot Simulated (Empirical) Cumulative Distribution Function...
In SimMultiCorrData: Simulation of Correlated Data with Multiple Variable Types

Description Usage Arguments Value References See Also Examples

This plots the cumulative distribution function of simulated continuous, ordinal, or count data using the empirical cdf Fn (see stat_ecdf). Fn is a step function with jumps i/n at observation values, where i is the number of tied observations at that value. Missing values are ignored. For observations y = (y1, y2, ..., yn), Fn is the fraction of observations less or equal to t, i.e., Fn(t) = sum[yi <= t]/n. If calc_cprob = TRUE and the variable is continuous, the cumulative probability up to y = delta is calculated (see sim_cdf_prob) and the region on the plot is filled with a dashed horizontal line drawn at Fn(delta). The cumulative probability is stated on top of the line. This fill option does not work for ordinal or count variables. The function returns a ggplot2-package object so the user can modify as necessary. The graph parameters (i.e. title, color, fill, hline) are ggplot2-package parameters. It works for valid or invalid power method pdfs.

plot_sim_cdf(sim_y, title = "Empirical Cumulative Distribution Function",
  ylower = NULL, yupper = NULL, calc_cprob = FALSE, delta = 5,
  color = "dark blue", fill = "blue", hline = "dark green",
  text.size = 11, title.text.size = 15, axis.text.size = 10,
  axis.title.size = 13)

`sim_y`	a vector of simulated data
`title`	the title for the graph (default = "Empirical Cumulative Distribution Function")
`ylower`	the lower y value to use in the plot (default = NULL, uses minimum simulated y value)
`yupper`	the upper y value (default = NULL, uses maximum simulated y value)
`calc_cprob`	if TRUE (default = FALSE) and `sim_y` is continuous, `sim_cdf_prob` is used to find the empirical cumulative probability up to y = delta and the region on the plot is filled with a dashed horizontal line drawn at Fn(delta)
`delta`	the value y at which to evaluate the cumulative probability (default = 5)
`color`	the line color for the cdf (default = "dark blue")
`fill`	the fill color if `calc_cprob` = TRUE (default = "blue)
`hline`	the dashed horizontal line color drawn at `delta` if `calc_cprob` = TRUE (default = "dark green")
`text.size`	the size of the text displaying the cumulative probability up to `delta` if `calc_cprob` = TRUE
`title.text.size`	the size of the plot title
`axis.text.size`	the size of the axes text (tick labels)
`axis.title.size`	the size of the axes titles

A ggplot2-package object.

Please see the references for plot_cdf.

Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009.

ecdf, sim_cdf_prob, ggplot2-package, stat_ecdf, geom_abline, geom_ribbon

## Not run: 
# Logistic Distribution: mean = 0, variance = 1
seed = 1234

# Find standardized cumulants
stcum <- calc_theory(Dist = "Logistic", params = c(0, 1))

# Simulate without the sixth cumulant correction
# (invalid power method pdf)
Logvar1 <- nonnormvar1(method = "Polynomial", means = 0, vars = 1,
                      skews = stcum[3], skurts = stcum[4],
                      fifths = stcum[5], sixths = stcum[6], seed = seed)

# Plot cdf with cumulative probability calculated up to delta = 5
plot_sim_cdf(sim_y = Logvar1$continuous_variable,
             title = "Invalid Logistic Empirical CDF",
             calc_cprob = TRUE, delta = 5)

# Simulate with the sixth cumulant correction
# (valid power method pdf)
Logvar2 <- nonnormvar1(method = "Polynomial", means = 0, vars = 1,
                      skews = stcum[3], skurts = stcum[4],
                      fifths = stcum[5], sixths = stcum[6],
                      Six = seq(1.5, 2, 0.05), seed = seed)

# Plot cdf with cumulative probability calculated up to delta = 5
plot_sim_cdf(sim_y = Logvar2$continuous_variable,
             title = "Valid Logistic Empirical CDF",
             calc_cprob = TRUE, delta = 5)

# Simulate one binary and one ordinal variable (4 categories) with
# correlation 0.3
Ordvars = rcorrvar(k_cat = 2, marginal = list(0.4, c(0.2, 0.5, 0.7)),
                   rho = matrix(c(1, 0.3, 0.3, 1), 2, 2), seed = seed)

# Plot cdf of 2nd variable
plot_sim_cdf(Ordvars$ordinal_variables[, 2])


## End(Not run)