propTest2 | R Documentation |
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.
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)
data |
the data as a data frame |
vars |
a vector of strings naming the variables of interest in
|
areCounts |
|
testValue |
a number (default: 0.5), the value for the null hypothesis |
hypothesis |
|
ci |
|
ciWidth |
a number between 50 and 99.9 (default: 95), the confidence interval width |
bf |
|
priorA |
a number (default: 1), the beta prior 'a' parameter |
priorB |
a number (default: 1), the beta prior 'b' parameter |
ciBayes |
|
ciBayesWidth |
a number between 50 and 99.9 (default: 95), the credible interval width |
postPlots |
|
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)
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
#
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