# NTplot

From HH v3.1-25
0th

Percentile

##### Specify plots to illustrate Normal and t Hypothesis Tests or Confidence Intervals, including normal approximation to the binomial.

Specify plots to illustrate Normal and t Hypothesis Tests or Confidence Intervals, including normal approximation to the binomial.

##### Usage
NTplot(mean0, ...)
## S3 method for class 'default':
NTplot(mean0=0, ..., shiny=FALSE,
distribution.name = c("normal","z","t","binomial"))
## S3 method for class 'htest':
NTplot(mean0, ..., shiny=FALSE, NTmethod="htest")
## S3 method for class 'power.htest':
NTplot(mean0, ..., shiny=FALSE, xbar=NA, ## these input values are used
mean1, n, df, sd, distribution.name, sub, ## these input values ignored
alpha.left, alpha.right, number.vars) ## these input values ignored
## NTplot(NTplot(htest.object), n=20) ## allows override of arguments
## S3 method for class 'NormalAndTplot':
NTplot(mean0, ..., shiny=FALSE)
##### Arguments
mean0
For the default method, mean0 is either missing or a numeric argument for the mean under the null hypothesis. For the htest method, mean0 is an htest object from the
xbar
See NormalAndTplot.
...
Other arguments, selected from the options for the default method NormalAndTplot.
shiny
Logical. If TRUE, a shiny app is started to provide an interactive graphics device in a web-browser. If FALSE, a plot is drawn on the current graphics device.
htest
logical. TRUE for "htest" objects.
mean1, n, df, sd, sub, alpha.left, alpha.right, number.vars
These variables are ignored here. They are captured so they won't interfere with similarly named variables that are generated in the power.htest method.
distribution.name
Ignored by htest and power.htest methods. Otherwise passed on to the next method.
NTmethod
Character string used when shiny=TRUE. It is normally calculated by the methods. NTmethod tells shiny how to use or ignore the df and n sliders. See the extended discussion in
##### Details

The graphs produced by this single function cover most of the first semester introductory Statistics course. All options of the t.test, power.t.test, and z.test are accepted and displayed.

NTplot is built on xyplot. Most of the arguments detailed in xyplot documentation work to control the appearance of the plot.

The shiny app (called when the argument shiny=TRUE) provides animated sliders for the means, standard deviation, xlimits, significance levels, df, and n. The df and n are rounded to integers for the sliders (relevant for htest and power.htest objects).

When you have a graph on the shiny window that you wish to keep, click on the "Display Options" tab, and then on the "Display Call" radio button. The main shiny window will show an R command which will reproduce the current plot. Pick it up with the mouse and drop it into an R console window.

To get out of the shiny window and return to an interactive R console, move the cursor back to the console window and interrupt the shiny call, usually by entering Ctrl-C or ESC.

##### Value

• "trellis" object. The object can be plotted or fed back into the NTplot function with argument shiny=TRUE to allow interactive graphical investigation of the hypothesis test or confidence interval. The attributes of the object\ NTobj <- NTplot()\ attr(NTobj, "scales") and attr(NTobj, "prob") make the data values and probability values accessible for further R computations. The "call" attribute cat(attr(NT.object, "call"), "") displays a statement that can be copied back into R to reproduce the graph. The cat() is needed to unescape embedded quotes. The "call.list" attribute attr(NT.object, "call.list") is a list that can be used with do.call to reproduce the graph. do.call(NTplot, attr(NT.object, "call.list")). This is usually not needed by the user because the simpler statement NTplot(NT.object) does it for you. NormalAndTplot, print.NormalAndTplot. [object Object] This function is built on lattice and latticeExtra. It supersedes the similar function normal.and.t.dist built on base graphics that is used in many displays in the book by Erich Neuwirth and me: R through Excel, Springer (2009). http://www.springer.com/978-1-4419-0051-7. Many details, particularly the alternate color scheme and the concept of floating probability labels, grew out of discussions that Erich and I have had since the book was published. It incorporates ideas that Jay Kerns and I developed at the 2011 UseR! conference. This version incorporates some ideas suggested by Moritz Heene. hplot shiny

x1 <- rnorm(12) x2 <- rnorm(12, mean=.5)

NT.object <- NTplot(mean0=0, mean1=1) NT.object attr(NT.object, "scales") attr(NT.object, "prob") cat(attr(NT.object, "call"), "") ## the cat() is needed to unescape embedded quotes.

NTplot(t.test(x1, x2)) NTplot(power.t.test(power = .90, delta = 1, alternative = "one.sided"))

## 22 distinct calls are shown in demo(NTplot, ask=FALSE)

## these are interactive and do not work in static checking of the code NTplot(mean0=0, mean1=1, shiny=TRUE) NTplot(t.test(x1, x2), shiny=TRUE, mean1=1) NTplot(power.t.test(power = .90, delta = 1, alternative = "one.sided"), shiny=TRUE) NTplot(NT.object, shiny=TRUE)

##### Aliases
• NTplot
• NTplot.default
• NTplot.htest
• NTplot.power.htest
• NTplot.NormalAndTplot
Documentation reproduced from package HH, version 3.1-25, License: GPL (>= 2)

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