plot_sim_pdf_ext {SimMultiCorrData}

Plot Simulated Probability Density Function and Target PDF of External Data for Continuous or Count Variables

Description

This plots the pdf of simulated continuous or count data and overlays the target pdf computed from the given external data vector. The external data is a required input. The simulated data is centered and scaled to have the same mean and variance as the external data set. If the user wants to only plot simulated data, plot_sim_theory should be used instead (with overlay = FALSE). It returns a ggplot2-package object so the user can modify as necessary. The graph parameters (i.e. title, power_color, target_color, target_lty) are ggplot2-package parameters. It works for valid or invalid power method pdfs.

Usage

plot_sim_pdf_ext(sim_y, title = "Simulated Probability Density Function",
  ylower = NULL, yupper = NULL, power_color = "dark blue", ext_y = NULL,
  target_color = "dark green", target_lty = 2, legend.position = c(0.975,
  0.9), legend.justification = c(1, 1), legend.text.size = 10,
  title.text.size = 15, axis.text.size = 10, axis.title.size = 13)

Arguments

sim_y

a vector of simulated data

title

the title for the graph (default = "Simulated Probability Density 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)

power_color

the histogram color for the simulated variable (default = "dark blue")

ext_y

a vector of external data (required)

target_color

the histogram color for the target pdf (default = "dark green")

target_lty

the line type for the target pdf (default = 2, dashed line)

legend.position

the position of the legend

legend.justification

the justification of the legend

legend.text.size

the size of the legend labels

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

Value

A ggplot2-package object.

References

Please see the references for plot_cdf.

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

See Also

ggplot2-package, geom_density

Examples

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

seed = 1234

# Simulate "external" data set
set.seed(seed)
ext_y <- rlogis(10000)

# 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],
                      n = 10000, seed = seed)

# Plot pdfs of simulated variable (invalid) and external data
plot_sim_pdf_ext(sim_y = Logvar1$continuous_variable,
                 title = "Invalid Logistic Simulated PDF", ext_y = ext_y)

# 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), n = 10000, seed = 1234)

# Plot pdfs of simulated variable (valid) and external data
plot_sim_pdf_ext(sim_y = Logvar2$continuous_variable,
                 title = "Valid Logistic Simulated PDF", ext_y = ext_y)

# Simulate 2 Poisson distributions (means = 10, 15) and correlation 0.3
# using Method 1
Pvars <- rcorrvar(k_pois = 2, lam = c(10, 15),
                  rho = matrix(c(1, 0.3, 0.3, 1), 2, 2), seed = seed)

# Simulate "external" data set
set.seed(seed)
ext_y <- rpois(10000, 10)

# Plot pdfs of 1st simulated variable and external data
plot_sim_pdf_ext(sim_y = Pvars$Poisson_variable[, 1], ext_y = ext_y)


## End(Not run)