ttestOneS: One Sample T-Test

Description

The Student's One-sample t-test is used to test the null hypothesis that the true mean is equal to a particular value (typically zero). A low p-value suggests that the null hypothesis is not true, and therefore the true mean must be different from the test value.

Usage

ttestOneS(data, vars, students = TRUE, bf = FALSE, bfPrior = 0.707,
  wilcoxon = FALSE, testValue = 0, hypothesis = "dt", norm = FALSE,
  qq = FALSE, meanDiff = FALSE, effectSize = FALSE, ci = FALSE,
  ciWidth = 95, desc = FALSE, plots = FALSE, miss = "perAnalysis",
  mann = FALSE)

Arguments

data

the data as a data frame

vars

a vector of strings naming the variables of interest in data

students

TRUE (default) or FALSE, perform Student's t-tests

TRUE or FALSE (default), provide Bayes factors

bfPrior

a number between 0.5 and 2.0 (default 0.707), the prior width to use in calculating Bayes factors

wilcoxon

TRUE or FALSE (default), perform Wilcoxon signed rank tests

testValue

a number specifying the value of the null hypothesis

hypothesis

'dt' (default), 'gt' or 'lt', the alternative hypothesis; different to testValue, greater than testValue, and less than testValue respectively

norm

TRUE or FALSE (default), perform Shapiro-wilk tests of normality

TRUE or FALSE (default), provide a Q-Q plot of residuals

meanDiff

TRUE or FALSE (default), provide means and standard deviations

effectSize

TRUE or FALSE (default), provide Cohen's d effect sizes

TRUE or FALSE (default), provide confidence intervals for the mean difference

ciWidth

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

desc

TRUE or FALSE (default), provide descriptive statistics

plots

TRUE or FALSE (default), provide descriptive plots

miss

'perAnalysis' or 'listwise', how to handle missing values; 'perAnalysis' excludes missing values for individual dependent variables, 'listwise' excludes a row from all analyses if one of its entries is missing.

mann

deprecated

Value

A results object containing:

`results$ttest`					a table containing the t-test results
`results$normality`					a table containing the normality test results
`results$descriptives`					a table containing the descriptives
`results$plots`					an image of the descriptive plots
`results$qq`					an array of Q-Q plots

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

results$ttest$asDF

as.data.frame(results$ttest)

Details

The Student's One-sample t-test assumes that the data are from a normal distribution -- in the case that one is unwilling to assume this, the non-parametric Wilcoxon signed-rank can be used in it's place (However, note that the Wilcoxon signed-rank has a slightly different null hypothesis; that the *median* is equal to the test value).

Examples

Run this code

# NOT RUN {
data('ToothGrowth')

ttestOneS(ToothGrowth, vars = vars(len, dose))

#
#  ONE SAMPLE T-TEST
#
#  One Sample T-Test
#  ------------------------------------------------------
#                           statistic    df      p
#  ------------------------------------------------------
#    len     Student's t         19.1    59.0    < .001
#    dose    Student's t         14.4    59.0    < .001
#  ------------------------------------------------------
#

# }

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