tigerstats (version 0.1.6)

binomtestGC: Exact Procedures for a Single Proportion

Description

Wrapper for binom.test in package stats. Employs the binomial distribution in inferential procedures for a single proportion.

Usage

binomtestGC(x,n=numeric(),p=NULL,data=parent.frame(),alternative="two.sided",
                         success="yes",conf.level=0.95,graph=FALSE,verbose=TRUE)

Arguments

x

Either a formula or a numeric vector. If formula, it must be of the form ~x indicating the single variable under study. When summary data are provided, x is a numeric vector of success counts.

n

When not empty, this is a numeric vector giving the size of the sample.

p

Specifies Null Hypothesis value for population proportion. If not set, no test is performed.

data

Data frame that supplies the variable x. If not found in data, the variable is searched for in the parent environment.

alternative

"two.sided" requests computation of a two-sided P-value; other possible values are "less" and "greater".

success

When x is a formula, this argument indicates which value of variable x is being counted as a success. When working with formula-data input the value of this parameter MUST be set, even when the variable has only two values.

conf.level

Number between 0 and 1 indicating the confidence-level of the interval supplied.

graph

If TRUE, plot graph of P-value. Ignored if no test is performed.

verbose

Determines whether to return lots of information or only the basics

Value

• Output to console. Future versions may return an object, and include a print method.

Examples

#Confidence interval only:
binomtestGC(~sex,data=m111survey,success="female")

#Confidence interval and two-sided test, Null Hypothesis p = 0.5:
binomtestGC(~sex,data=m111survey,success="female",p=0.5)

#For confidence level other than 95\%, use conf.level argument.
#For 90\% interval:
binomtestGC(~sex,data=m111survey,success="female",conf.level=0.90)

#For one-sided test, set alternative argument as desired:
binomtestGC(~sex,data=m111survey,p=0.50,
    success="female",alternative="greater")

#Summary data:
#In one sample, 40 successes in 100 trials.  Testing whether p = 0.45.
binomtestGC(40,100,p=0.45)