jmv

Paired Samples T-Test

Provides a range of descriptive statistics

Example usage

data('ToothGrowth')

# todo: find a better example

ttestPS(ToothGrowth,
    pairs = list(
        list(i1 = 'len', i2 = 'dose')))

#
# Paired Samples T-Test
#
# Paired Samples T-Test
# ─────────────────────────────────────────────────────────────
#                                 statistic    df      p
# ─────────────────────────────────────────────────────────────
#   len    dose    Student's t         19.1    59.0    < .001
# ─────────────────────────────────────────────────────────────
#
 

Arguments

data — the data as a data frame

pairs — a list of lists specifying the pairs of measurement in data

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

bfTRUE or FALSE (default), provide Bayes factors

bfPrior — a number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors

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

hypothesis'different' (default), 'oneGreater' or 'twoGreater', the alternative hypothesis; group 1 different to group 2, group 1 greater than group 2, and group 2 greater than group 1 respectively

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

meanDiffTRUE or FALSE (default), provide means and standard errors

effectSizeTRUE or FALSE (default), provide effect sizes

ciTRUE or FALSE (default), provide confidence intervals

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

descTRUE or FALSE (default), provide descriptive statistics

plotsTRUE 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.