StMoSim: Plots a QQ-Norm Plot with Several Gaussian Simulations

Description

With this package you can simulate several lines into the QQ-Norm Plot under the assumption of Gaussian distribution. If the realised observations lie inside of the simulations tracks there is the possibility that the observations stem from a Gaussian distribution. This can be very useful in residual analysis where you have to evaluate whether the model residuals fit the assumption of gaussian distributed terms or not.

Changelog

———–<CHANGELOG>———–

——-< v3.1.1 - 2018-11-19 >——-

provide more (plot) arguments to the user.

updated documentation - added more expamples.

added BugReports argument in DESCRIPTION.

implemented all recommendations from RcppParallel package.

——-< v3.1 - 2018-11-13 >——-

Minor bug fixes, due to CHECK changes on CRAN.

Moved documentation to roxygen2.

——-< v3.0 - 2014-10-16 >——-

Computation intense code moved to C++.

Moved to parallel computation, thanks to Rcpp/RcppParallel !

Minor bug fixes.

——-< v2.2 - 2012-02-24 >——-

Minor bug fixes, due to CHECK changes on CRAN.

——-< v2.1 - 2012-02-24 >——-

Minor bug fixes.

——-<v2.0 - 2011-03-31 >——-

Moved to S4 Classes.

——-<v1.1 - 2010-05-03 >——-

First Version on CRAN.

———–</CHANGELOG>———–

Author(s)

Matthias Salvisberg <matthias.salvisberg@gmail.com>

Quantile-Quantile plot with several Gaussian simulations.

Description

Plots a QQ plot of the variable x with nSim Gaussian simulations.

Usage

qqnormSim(x, nSim = 500, mOfVar = "mad",
  main = "Normal Q-Q Plot - SIM", xlab = "Theoretical Quantiles",
  ylab = "Sample Quantiles", qqnormCol = "black", qqnormPch = 1,
  qqlineCol = "#cdd2d015", qqlineLwd = 3)

## S4 method for signature 'lm'
qqnormSim(x, nSim = 500, mOfVar = "mad",
  main = "Normal Q-Q Plot - SIM", xlab = "Theoretical Quantiles",
  ylab = "Sample Quantiles", qqnormCol = "black", qqnormPch = 1,
  qqlineCol = "#cdd2d015", qqlineLwd = 3)

## S4 method for signature 'numeric'
qqnormSim(x, nSim = 500, mOfVar = "mad",
  main = "Normal Q-Q Plot - SIM", xlab = "Theoretical Quantiles",
  ylab = "Sample Quantiles", qqnormCol = "black", qqnormPch = 1,
  qqlineCol = "#cdd2d015", qqlineLwd = 3)

Arguments

Details

Two estimators are required for the simulation of the normal distribution. Since the normal distribution is a two-parameter family distribution. Default measure of location is the mean. Default measure of variation is the mad. This gives a robust estimation of the standard deviation even if there are outliers in the sample. Likewise this can be changed with the parameter mOfVar.

Value

invisible(NULL)

Author(s)

Matthias Salvisberg <matthias.salvisberg@gmail.com>

See Also

the basic graph corresponds to qqnorm

Examples

## Not run: 

######## qqnorm vs. qqnormSim ########

par(mfrow = c(1,2))
x<- rnorm(100)
qqnorm(x)
qqline(x)
qqnormSim(x)
par(mfrow = c(1,1))

######## basic functionality/arguments ########

# The observations should behave like a simulation, 
# because the observations are sampled from a Gaussian distribution.
qqnormSim(x = rnorm(100))

# If you don't feel comfortable with the mad as 
# measure of variation you can change it to the standard deviation.
qqnormSim(x = rnorm(100),
          mOfVar = "sd")

# On the first glance its obvious that this sample 
# doesn't originate from a Gaussian distribution due to the heavy tails.
qqnormSim(x = rt(100,df = 4))

Reduce the simulation tracks from 500 to 50. (500 is default).
Not recommended unless you have not enough computation power.
qqnormSim(x = rnorm(100), 
          nSim = 50)

######## graphical arguments ########

# set title and axes labels.
qqnormSim(x = rnorm(100), 
          main = "main title",
          xlab = "x-axis label",
          ylab = "y-axis label")
          
# I don't recommend fancy colors, unless you need it for your corporate identity.
qqnormSim(x = rnorm(100), 
          qqnormCol = "#ff0000",
          qqnormPch = 16,
          qqlineCol = "greenyellow",
          qqlineLwd = 1)


## End(Not run)