HH (version 3.1-32)

normalApproxBinomial: Plots to illustrate Normal Approximation to the Binomial---hypothesis tests or confidence intervals.

Description

Plots to illustrate Normal Approximation to the Binomial---hypothesis tests or confidence intervals.

Usage

normalApproxBinomial(p0= if (number.vars==1) .5 else 0, p1=NA, p2=NA, p.hat=if (number.vars==1) .75 else 0, n=1, xlim=if (number.vars==1) c(0,1) else c(-1,1), ylim=c(0, 5), type=c("hypothesis","confidence"), alpha.left=if (type=="hypothesis") 0 else .025, alpha.right=if (type=="hypothesis") .05 else .025, xlab=if (number.vars==1) "w = p = population proportion" else "w = p[1] - p[2] :: population proportions", ..., number.vars=if (!is.na(p1) && !is.na(p2)) 2 else 1)

Arguments

p0
Null hypothesis value of $p$.
p1
Alternate hypothesis value of $p$ for one-sample cases. Second sample value of $p$ for two-sample cases.
p2
Second sample value of $p$.
p.hat
Observed value of $p$.
n
Number of observations (for example, number of coins tossed).
xlim, ylim, xlab
Standard xyplot arguments...
type
"hypothesis" for a Hypothesis Test graph, or "confidence" for a Confidence Interval graph.
..., alpha.left, alpha.right
Additional arguments forwarded to NTplot.
number.vars
Number of variables. 1 for a one-sample test, 2 for two-sample tests and paired tests.

Value

"trellis" object.

Details

This is a wrapper function for the plots in NTplot.

Examples

Run this code
NTplot(distribution.name="binomial", n=20, ylim=c(0,4.2), p1=.8)
NTplot(distribution.name="binomial", n=20, type="confidence", ylim=c(0,4.2))
## Not run: 
# NTplot(distribution.name="binomial", n=20, zaxis=TRUE, z1axis=TRUE,
#        p1=.8678, ylim=c(0, 5.2))
# NTplot(p0=.4, p.hat=.65, p1=.7, distribution.name="binomial", n=15)
# NTplot(p.hat=.65, distribution.name="binomial", n=15, type="confidence")
# ## End(Not run)
## Not run:  ## these are interactive and won't work in R CMD check
# NTplot(distribution.name="binomial", n=20, ylim=c(0,4.2), p1=.8, shiny=TRUE)
# NTplot(p0=.4, p.hat=.65, p1=.7, distribution.name="binomial", n=15, shiny=TRUE)
# NTplot(p.hat=.65, distribution.name="binomial", n=15, type="confidence", shiny=TRUE)
# ## End(Not run)

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