Version: 2.9.2
Date: 2025-01-11
Title: Extensions of Package 'distr' for Teaching Stochastics/Statistics in Secondary School
Description: Provides flexible examples of LLN and CLT for teaching purposes in secondary school.
Depends: R(≥ 3.4), methods, distr(≥ 2.2), distrEx(≥ 2.2)
Suggests: tcltk
Imports: startupmsg(≥ 1.0.0), grDevices, graphics, stats
ByteCompile: yes
License: LGPL-3
Encoding: UTF-8
URL: http://distr.r-forge.r-project.org/
Packaged: 2025-01-11 22:07:16 UTC; ruckdesc
LastChangedDate: {$LastChangedDate: 2024-01-30 20:10:15 +0100 (Di, 30 Jan 2024) $}
LastChangedRevision: {$LastChangedRevision: 1430 $}
VCS/SVNRevision: 1493
NeedsCompilation: no
Author: Eleonora Feist [ctb] (contributed as student in the initial phase --2008), Matthias Kohl [aut, cph], Peter Ruckdeschel [cre, cph], Anja Hueller [ctb] (contributed as student in the initial phase --2008)
Maintainer: Peter Ruckdeschel <peter.ruckdeschel@uni-oldenburg.de>
Repository: CRAN
Date/Publication: 2025-01-13 19:10:09 UTC

distrTeach – Teaching Extensions of Package distr

Description

distrTeach provides some illustrations based on package distr for teaching Stochastics / Statistics in secondary school; so far the following has been implemented

as well as a Tcl/Tk based demo forillustrateCLT

Details

Package: distrTeach
Version: 2.9.2
Date: 2025-01-11
Depends: R(>= 3.4), methods, distr(>= 2.2), distrEx(>= 2.2)
Suggests: tcltk
Imports: startupmsg(>= 1.0.0), grDevices, graphics, stats
LazyLoad: yes
License: LGPL-3
URL: http://distr.r-forge.r-project.org/
VCS/SVNRevision: 1493

Classes

Teaching Classes

Methods

illustration:
illustrateLLT           function for the generation of LLN - visualizations
illustrateCLT           function for the generation of CLT - visualizations
plotCLT                 Generic function for the plotting of CLT-approximations

Demos

Demos are available — see demo(package="distrTeach").

Start-up-Banner

You may suppress the start-up banner/message completely by setting options("StartupBanner"="off") somewhere before loading this package by library or require in your R-code / R-session. If option "StartupBanner" is not defined (default) or setting options("StartupBanner"=NULL) or options("StartupBanner"="complete") the complete start-up banner is displayed. For any other value of option "StartupBanner" (i.e., not in c(NULL,"off","complete")) only the version information is displayed. The same can be achieved by wrapping the library or require call into either suppressStartupMessages() or onlytypeStartupMessages(.,atypes="version").

As for general packageStartupMessage's, you may also suppress all the start-up banner by wrapping the library or require call into suppressPackageStartupMessages() from startupmsg-version 0.5 on.

Package versions

Note: The first two numbers of package versions do not necessarily reflect package-individual development, but rather are chosen for the distrXXX family as a whole in order to ease updating "depends" information.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de and
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de,
Eleonara Feist eleonoragerber@gmx.de, and,
Anja Hueller
Maintainer: Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de

References

P. Ruckdeschel, M. Kohl, T. Stabla, F. Camphausen (2006): S4 Classes for Distributions, R News, 6(2), 2-6. https://CRAN.R-project.org/doc/Rnews/Rnews_2006-2.pdf a vignette for packages distr, distrSim, distrTEst,

and distrTeach is included into the mere documentation package distrDoc and may be called by require("distrDoc");vignette("distr") a homepage to this package is available under
https://distr.r-forge.r-project.org/ and the pages ... M. Kohl (2005): Numerical Contributions to the Asymptotic Theory of Robustness. PhD Thesis. Bayreuth. Available as https://www.stamats.de/wp-content/uploads/2018/04/ThesisMKohl.pdf

See Also

distr-package distrEx-package

Functions for Illustrating the CLT

Description

Functions for generating a sequence of plots of the density and cdf of the consecutive standardized and centered sums of iid r.v. distributed according to a prescribed discrete or absolutely continuous distribution compared to the standard normal — uses the generic function plotCLT.

Usage

illustrateCLT(Distr, len, sleep = 0)
illustrateCLT.tcl(Distr, k, Distrname)

Arguments

Distr

object of class "AbscontDistribution", "LatticeDistribution" or "DiscreteDistribution": distribution of the summands

len

integer: up to which number of summands plots are generated

k

integer: number of summands for which a plot is to be generated

Distrname

character: name of the summand distribution to be used as title in the plot

sleep

numeric: pause in seconds between subsequent plots

Details

illustrateCLT generates a sequence of plots, while illustrateCLT.tcl may be used with Tcl/Tk-widgets as in demo illustCLT_tcl.R.

Value

void

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de

References

Kohl, M., Ruckdeschel, P., (2014): General purpose convolution algorithm for distributions in S4-Classes by means of FFT. J. Statist. Softw. 59(4): 1-25.

See Also

plotCLT

Examples

distroptions("DefaultNrFFTGridPointsExponent" = 13)
illustrateCLT(Distr = Unif(), len = 10)
distroptions("DefaultNrFFTGridPointsExponent" = 12)
illustrateCLT(Distr = Pois(lambda = 2), len = 10)
distroptions("DefaultNrFFTGridPointsExponent" = 13)
illustrateCLT(Distr = Pois(lambda = 2)+Unif(), len = 10)
illustrateCLT.tcl(Distr = Unif(), k = 4, "Unif()")

Functions for Illustrating the LLN

Description

Functions for generating a sequence of plots of randomly generated replicates of \bar X_n= \frac{1}{n} \sum_{i=1}^n X_i for sums of iid r.v. distributed according to a prescribed discrete or absolutely continuous distribution. A line for the expectation and CLT based (pointwise) 95%-confidence bands are also plotted and the empirical coverage of this band by the replicated plotted so far is indicated.

Usage

illustrateLLN(Distr = Norm(),n = c(1,3,5,10,25,50,100,500,1000,10000), 
            m = 50, step = 1, sleep = 0, withConf = TRUE, 
            withCover = (length(n)<=12), withEline = TRUE, withLegend = TRUE,
            CLTorCheb = "CLT",  coverage = 0.95, ...,  col.Eline = "blue", 
            lwd.Eline = par("lwd"), lty.Eline = par("lty"), col.Conf = "red", 
            lwd.Conf = par("lwd"), lty.Conf = 2, cex.Cover = 0.7, 
            cex.legend = 0.8)

Arguments

Distr

object of class "UnivariateDistribution": distribution of the summands

n

vector of integers: sample sizes to be considered

m

integer: (total) number of replicates to be plotted subsequently

step

integer: number of replicates to be drawn at once

sleep

numeric: pause in seconds between subsequent plots

withEline

logical: shall a line for the limiting expectation (in case of class Cauchy instead: median) be drawn?

withConf

logical: shall (CLT-based) confidence bands be plotted?

withCover

logical: shall empirical coverage of (CLT-based) confidence bands be printed?

withLegend

logical: shall a legend be included?

CLTorCheb

character: type of confidence interval —"CLT" or "Chebyshev"; partial matching is used; if this fails "CLT" is used.

coverage

numerical: nominal coverage of the confidence bands —to be in (0,1)

col.Eline

character or integer code; color for confidence bands

lwd.Eline

integer code (see par); line width of the confidence bands

lty.Eline

integer code (see par); line type of the confidence bands

col.Conf

character or integer code; color for confidence bands

lwd.Conf

integer code (see par); line width of the confidence bands

lty.Conf

integer code (see par); line type of the confidence bands

cex.Cover

magnification w.r.t. the current setting of cex to be used for empirical coverages; as in par

cex.legend

magnification w.r.t. the current setting of cex to be used for the legend as in par

...

further arguments to be passed to matplot, matlines, abline

Details

illustrateLLN generates a sequence of plots. Any parameters of plot.default may be passed on to this particular plot method.

There are default main titles as well as xlab and ylab annotations.

In all title arguments, the following patterns are substituted:

"%C"

class of argument x

"%P"

parameters of x in form of a comma-separated list of <value>'s coerced to character

"%Q"

parameters of x in form of a comma-separated list of <value>'s coerced to character and in parenthesis — unless empty; then ""

"%N"

parameters of x in form of a comma-separated list <name> = <value> coerced to character

"%A"

deparsed argument x

"%D"

time/date-string when the plot was generated

"%X"

the expression \bar X_n=\sum_{i=1}^n X_i/n

If not explicitly set, col.Eline, col.Conf are set to col if this arg is given and else to their default values as given above. Similarly for cex, lwd and lty.

Value

void

Author(s)

Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de

Examples

illustrateLLN(Distr = Unif())
illustrateLLN(Distr = Pois(lambda = 2))
illustrateLLN(Distr = Pois(lambda = 2)+Unif())
illustrateLLN(Td(3), m = 50, col.Eline = "green", lwd = 2, cex = 0.6, main = 
 "My LLN %C%Q", sub = "generated %D")
illustrateLLN(Td(3), m = 50, CLTorCheb = "Chebyshev") 
illustrateLLN(Td(3), m = 50, CLTorCheb = "Chebyshev", coverage = 0.75)

Generic Plot Function for Illustrating the CLT

Description

Generic 'plot' function for generating the plots of 'illustrateCLT'.

Usage

plotCLT(Tn, ...)
## S4 method for signature 'AbscontDistribution'
plotCLT(Tn, k, summands="")
## S4 method for signature 'DiscreteDistribution'
plotCLT(Tn, k, summands="")
## S4 method for signature 'UnivariateDistribution'
plotCLT(Tn, k, summands="")

Arguments

Tn

object of class "AbscontDistribution" or class "DiscreteDistribution": distribution of the summands

k

integer: number off summands to be plotted as graphics title

summands

character: name of the summands

...

addtional arguments for methods not yet implemented

Value

void

Author(s)

Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
Matthias Kohl Matthias.Kohl@stamats.de

See Also

illustrateCLT

Examples

illustrateCLT(Distr = Unif(), len = 20)