Computes all results for test on proportion using either stats::binom.test(), or
a normal approximation without continuity correction.
Either named parameters can be given or an arglist with the following parameters:
x number of successes
n sample size (default: sd(x))
pi0 true value of the proportion (default: 0.5)
alternative a string specifying the alternative hypothesis (default: "two.sided"), otherwise "greater" or "less" can be used
alpha significance level (default: 0.05)
binom2norm can the binomial distribution be approximated by a normal distribution? (default: NA = use binom2norm function)
proptest_num(..., arglist = NULL)prop_binomtest_num(..., arglist = NULL)
nbinomtest(..., arglist = NULL)
A list with the input parameters and the following:
X distribution of the random sampling function
Statistic distribution of the test statistics
statistic test value
critical critical value(s)
criticalx critical value(s) in x range
acceptance0 acceptance interval for H0
acceptance0x acceptance interval for H0 in x range
accept1 is H1 accepted?
p.value p value for test (note: the p-value may not be reliable see Notes!)
alphaexact exact significance level
stderr standard error of the proportion used as denominator
named input parameters
list: named input parameters, if given ... will be ignored
The results of proptest_num may differ from stats::binom.test(). proptest_num is designed to return results
when you compute a binomial test by hand. For example, for computing the test statistic the approximation \(t_n \approx N(0; 1)\)
is used if \(n>n.tapprox\). The p.value is computed by stats::binom.test and may not be reliable, for Details see Note!
n <- 100
x <- sum(runif(n)<0.4)
proptest_num(x=x, n=n)
Run the code above in your browser using DataLab