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)
#
# PROPORTION TEST (2 OUTCOMES)
#
# 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
#
Arguments
| 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 |
Returns
A results object containing:
| results$table | a table |
| results$postPlots | an array of arrays |
Tables can be converted to data frames with asDF or as.data.frame(). For example:
results$table$asDF
as.data.frame(results$table)
Elements in arrays can be accessed with [[n]]. For example:
results$postPlots[[1]] # accesses the first element