HH (version 3.1-23)

NormalAndTplot: Specify plots to illustrate Normal and t Hypothesis Tests or Confidence Intervals.

Description

Specify plots to illustrate Normal and t Hypothesis Tests or Confidence Intervals.

Usage

NormalAndTplot(mean0, ...)
## S3 method for class 'default':
NormalAndTplot(mean0=0,
             mean1=NA,
             xbar=NA,
             df=Inf, n=1,
             sd=1,
             xlim=c(-3, 3)*sd/sqrt(n) + range(c(mean0, mean1, xbar), na.rm=TRUE),
             ylim, alpha.right=.05, alpha.left=0,
             float=TRUE, ntcolors="original",
             digits=4, digits.axis=digits, digits.float=digits,
             distribution.name=c("normal","z","t","binomial"),
             type=c("hypothesis", "confidence"),
             zaxis=FALSE, z1axis=FALSE,
             cex.z=.5, cex.prob=.6, cex.top.axis=1,
             main=NA, xlab, ylab,
             prob.labels=(type=="hypothesis"),
             xhalf.multiplier=1,
             yhalf.multiplier=1,
             cex.main=1,
             key.axis.padding=4.5,
             number.vars=1,
             sub=NULL,
             NTmethod="default",
             power=FALSE,
             beta=FALSE,
              ...)
## S3 method for class 'htest':
NormalAndTplot(mean0, type="hypothesis", xlim=NULL, mean1=NA, ...,
             xbar, sd, df, n, alpha.left, alpha.right, ## ignored
             distribution.name, sub ## these input arguments will be ignored
             )

Arguments

mean0
Null hypothesis $\mu_0$. When graphing a confidence interval, mean0 will be used for xbar should xbar itself have the value NA.
mean1
Alternative hypothesis $\mu_1$.
xbar
Observed $\bar{x}$.
sd
Standard deviation in the data scale $\sigma$ for normal-, or $s$ for $t$-distribution.
df
Degrees of freedom for $t$-distribution.
n
Number of observations per group.
main, xlab, ylab, xlim, ylim, sub
Standard xyplot arguments. Default values are constructed if these arguments are missing. The input value main=NA forces a new constructed main instead of using the
...
Additional xyplot arguments.
number.vars
Number of variables. 1 for a one-sample test, 2 for two-sample tests and paired tests.
alpha.left, alpha.right
For type="hypothesis", the sum of these two numbers is the probability of the Type I Error $\alpha$. When both of these numbers are positive, there is a two-sided test. Note that it is not required that they be equal. If one of the numbers
float
Logical. If TRUE, then the probabilities $\alpha$, $\beta$, power, and $p$-values or the confidence value are displayed on the graph. If FALSE, these values are not displayed.
ntcolors
Vector of colors used in the graph. The default value is "original", which implies the ten colors c(col.alpha = "blue", col.notalpha = "lightblue", col.beta = "red", col.power = "pink", col.pvalue = "green", col.pvaluetransluce
digits.axis, digits.float, digits
digits.axis is the number of significant digits for the top axis. digits.float is the number of significant digits for the floating probability values on the graph. digits is a convenience argument to s
distribution.name
Name of distribution.
type
"hypothesis" for a Hypothesis Test graph, or "confidence" for a Confidence Interval graph.
zaxis, z1axis
Logical or list. Should the $z$-axis centered on $\mu_0$, or the $z_1$-axis centered on $\mu_1$, be displayed? The list version of the argument must have two components at and labels as specified in
cex.z, cex.prob, cex.top.axis, cex.main
cex.z is the cex value for the $z$ and $z_1$ axes on the plot. cex.prob is the cex value for the floating probabilities on the graph. cex.top.axis is the cex value
key.axis.padding
tuning constant to create additional room above the graph for a larger cex.main to fit.
prob.labels
logical. If TRUE label the floating probability values with their name, such as $\alpha$. If FALSE, then don't label them. The default is TRUE for type="hypothesis" and FALSE<
xhalf.multiplier, yhalf.multiplier
Numerical tuning constants to control the width and height of the floating probability values. Empirically, we need a smaller value for the shiny app then we need for direct writing onto a graphic device.
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.

"htest" objec

power, beta
Logical. If TRUE, then display that graph, else don't display it. Passed forward to powerplot.

Value

  • "trellis" object.

Details

The graphs produced by this single function cover most of the first semester introductory Statistics course. The htest method plots the results of the stats::t.test function.

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

See Also

NTplot

Examples

Run this code
NTplot(mean0=0, mean1=2,  xbar=1.8,  xlim=c(-3, 5))
   NTplot(mean0=0, mean1=2,  xbar=1.8,  xlim=c(-3, 5), distribution.name="t", df=4)
   NTplot(mean0=100, sd=12, mean1=113,  xbar=105,  xlim=c(92, 120), n=20)
   NTplot(mean0=100, sd=12, mean1=113,  xbar=105,  xlim=c(92, 120), n=20,
          zaxis=TRUE, z1axis=TRUE)
   NTplot(mean0=100, sd=12,  xbar=105,  xlim=c(92, 108), n=20, ntcolors="stoplight")
   NTplot(xbar=95, sd=10, xlim=c(65, 125), type="confidence",
          alpha.left=.025, alpha.right=.025)


x <- rnorm(12, mean=.78)
x.t <- t.test(x)
NTplot(x.t)
NTplot(x.t, type="confidence")
x.tg <- t.test(x, alternative="greater")
NTplot(x.tg)

y <- rnorm(12, mean=-.05)
xy.t <- t.test(x, y)
NTplot(xy.t)
NTplot(xy.t, type="confidence")

NTplot(shiny=TRUE)  ## with any other arguments for initialization of the shiny app.

printbook.colors <- c(  ## based on "original" colors
  col.alpha             = "blue",
  col.notalpha          = "lightcyan",  ## this value is nonstandard
  col.beta              = "red",
  col.power             = "pink",
  col.pvalue            = "green",
  col.pvaluetranslucent = HH:::ColorWithAlpha("springgreen"),  ## this value is nonstandard
  col.critical          = "gray50",
  col.border            = HH:::ColorWithAlpha("black"),
  col.text              = "black",
  col.conf              = "lightgreen"
)
NTplot(ntcolors=printbook.colors) ## different colors

## mean1 and xbar
  NTplot(mean0=0, mean1=2,  xbar=1.8,  xlim=c(-3, 5))
  NTplot(mean0=0, mean1=-2, xbar=-1.8, xlim=c(-5, 3),
         alpha.left=.05,  alpha.right=0)
  NTplot(mean0=0, mean1=2,  xbar=2.1,  xlim=c(-3, 5),
         alpha.left=.025, alpha.right=.025)
  NTplot(mean0=0, mean1=-2, xbar=-2.1, xlim=c(-5, 3),
         alpha.left=.025, alpha.right=.025)

## mean1
  NTplot(mean0=0, mean1=2,  xbar=NA, xlim=c(-3, 5))
  NTplot(mean0=0, mean1=-2, xbar=NA, xlim=c(-5, 3),
         alpha.left=.05,  alpha.right=0)
  NTplot(mean0=0, mean1=2,  xbar=NA, xlim=c(-3, 5),
         alpha.left=.025, alpha.right=.025)
  NTplot(mean0=0, mean1=-2, xbar=NA, xlim=c(-5, 3),
         alpha.left=.025, alpha.right=.025)

## xbar
  NTplot(mean0=0, mean1=NA, xbar=1.8,  xlim=c(-3, 5))
  NTplot(mean0=0, mean1=NA, xbar=-1.8, xlim=c(-5, 3),
         alpha.left=.05,  alpha.right=0)
  NTplot(mean0=0, mean1=NA, xbar=2.1,  xlim=c(-3, 5),
         alpha.left=.025, alpha.right=.025)
  NTplot(mean0=0, mean1=NA, xbar=-2.1, xlim=c(-5, 3),
         alpha.left=.025, alpha.right=.025)

## t distribution
## mean1 and xbar
  NTplot(mean0=0, mean1=2,  xbar=1.8,  xlim=c(-3, 5),
         distribution.name="t", df=4)
  NTplot(mean0=0, mean1=-2, xbar=-1.8, xlim=c(-5, 3),
         alpha.left=.05,  alpha.right=0, distribution.name="t", df=4)
  NTplot(mean0=0, mean1=2,  xbar=2.1,  xlim=c(-3, 5),
         alpha.left=.025, alpha.right=.025, distribution.name="t", df=4)
  NTplot(mean0=0, mean1=-2, xbar=-2.1, xlim=c(-5, 3),
         alpha.left=.025, alpha.right=.025, distribution.name="t", df=4)

## mean1
  NTplot(mean0=0, mean1=2,  xbar=NA, xlim=c(-3, 5),
         distribution.name="t", df=4)
  NTplot(mean0=0, mean1=-2, xbar=NA, xlim=c(-5, 3),
         alpha.left=.05,  alpha.right=0, distribution.name="t", df=4)
  NTplot(mean0=0, mean1=2,  xbar=NA, xlim=c(-3, 5),
         alpha.left=.025, alpha.right=.025, distribution.name="t", df=4)
  NTplot(mean0=0, mean1=-2, xbar=NA, xlim=c(-5, 3),
         alpha.left=.025, alpha.right=.025, distribution.name="t", df=4)

## xbar
  NTplot(mean0=0, mean1=NA, xbar=1.8,  xlim=c(-3, 5),
         distribution.name="t", df=4)
  NTplot(mean0=0, mean1=NA, xbar=-1.8, xlim=c(-5, 3),
         alpha.left=.05,  alpha.right=0, distribution.name="t", df=4)
  NTplot(mean0=0, mean1=NA, xbar=2.1,  xlim=c(-3, 5),
         alpha.left=.025, alpha.right=.025, distribution.name="t", df=4)
  NTplot(mean0=0, mean1=NA, xbar=-2.1, xlim=c(-5, 3),
         alpha.left=.025, alpha.right=.025, distribution.name="t", df=4)

## confidence intervals

  NTplot(mean0=0, xlim=c(-3, 4), type="confidence")
  NTplot(xbar=01, xlim=c(-3, 4), type="confidence")
  NTplot(mean0=0, xlim=c(-4, 3), type="confidence",
         alpha.left=.05,  alpha.right=0)
  NTplot(mean0=0, xlim=c(-3, 3), type="confidence",
         alpha.left=.025, alpha.right=.025)
  NTplot(mean0=95, sd=10, xlim=c(65, 125), type="confidence",
         alpha.left=.025, alpha.right=.025)
  NTplot(mean0=95, sd=10, xlim=c(65, 125), type="confidence",
         alpha.left=.025, alpha.right=.025,
         distribution="t", df=10)

Run the code above in your browser using DataCamp Workspace