Proportion Test (2 Outcomes)

The Binomial test is used to test the Null hypothesis that the proportion of observations match some expected value. If the p-value is low, this suggests that the Null hypothesis is false, and that the true proportion must be some other value.

Example usage

dat <- data.frame(x=c(8, 15))

propTest2(dat, vars = x, areCounts = TRUE)

#  Binomial Test
#  ───────────────────────────────────────────────────────
#         Level    Count    Total    Proportion    p
#  ───────────────────────────────────────────────────────
#    x    1            8       23         0.348    0.210
#         2           15       23         0.652    0.210
#  ───────────────────────────────────────────────────────
#    Note. Hₐ is proportion ≠ 0.5


data the data as a data frame
vars a vector of strings naming the variables of interest in data
areCounts TRUE or FALSE (default), the variables are counts
testValue a number (default: 0.5), the value for the null hypothesis
hypothesis 'notequal' (default), 'greater' or 'less', the alternative hypothesis
ci TRUE or FALSE (default), provide confidence intervals
ciWidth a number between 50 and 99.9 (default: 95), the confidence interval width
bf TRUE or FALSE (default), provide Bayes factors
priorA a number (default: 1), the beta prior 'a' parameter
priorB a number (default: 1), the beta prior 'b' parameter
ciBayes TRUE or FALSE (default), provide Bayesian credible intervals
ciBayesWidth a number between 50 and 99.9 (default: 95), the credible interval width
postPlots TRUE or FALSE (default), provide posterior plots


A results object containing:

results$table a table
results$postPlots an array of arrays

Tables can be converted to data frames with asDF or For example:


Elements in arrays can be accessed with [[n]]. For example:

results$postPlots[[1]] # accesses the first element