propTest2: Proportion Test (2 Outcomes)

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

TRUE or FALSE (default), provide confidence intervals

ciWidth

a number between 50 and 99.9 (default: 95), the confidence interval width

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

Value

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

Run this code

# NOT RUN {
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
#
# }

Run the code above in your browser using DataCamp Workspace