Section author: Ravi Selker, Jonathon Love, Damian Dropmann

Proportion Test (2 Outcomes; propTest2)

Description

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.

Usage

propTest2(
  data,
  vars,
  areCounts = FALSE,
  testValue = 0.5,
  hypothesis = "notequal",
  ci = FALSE,
  ciWidth = 95,
  bf = FALSE,
  priorA = 1,
  priorB = 1,
  ciBayes = FALSE,
  ciBayesWidth = 95,
  postPlots = FALSE
)

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

Output

A results object containing:

results$table	a table of the proportions and test results
results$postPlots	an array of the posterior plots

Tables can be converted to data frames with asDF or as.data.frame(). For example:

results$table$asDF

as.data.frame(results$table)

Examples

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
#