この節の作者: Ravi Selker, Jonathon Love, Damian Dropmann
Independent Samples T-Test (ttestIS
)¶
Description¶
The Student's Independent samples t-test (sometimes called a two-samples t-test) is used to test the null hypothesis that two groups have the same mean. A low p-value suggests that the null hypothesis is not true, and therefore the group means are different.
Usage¶
ttestIS(
data,
vars,
group,
students = TRUE,
bf = FALSE,
bfPrior = 0.707,
welchs = FALSE,
mann = FALSE,
hypothesis = "different",
norm = FALSE,
qq = FALSE,
eqv = FALSE,
meanDiff = FALSE,
ci = FALSE,
ciWidth = 95,
effectSize = FALSE,
ciES = FALSE,
ciWidthES = 95,
desc = FALSE,
plots = FALSE,
miss = "perAnalysis",
formula
)
Arguments¶
data |
the data as a data frame |
vars |
the dependent variables (not necessary when using a formula, see the examples) |
group |
the grouping variable with two levels (not necessary when using a formula, see the examples) |
students |
TRUE (default) or FALSE , perform Student's t-tests |
bf |
TRUE 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 |
welchs |
TRUE or FALSE (default), perform Welch's t-tests |
mann |
TRUE or FALSE (default), perform Mann-Whitney U 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 |
norm |
TRUE or FALSE (default), perform Shapiro-Wilk tests of normality |
qq |
TRUE or FALSE (default), provide Q-Q plots of residuals |
eqv |
TRUE or FALSE (default), perform Levene's tests for homogeneity of variances |
meanDiff |
TRUE or FALSE (default), provide means and standard errors |
ci |
TRUE or FALSE (default), provide confidence intervals |
ciWidth |
a number between 50 and 99.9 (default: 95), the width of confidence intervals |
effectSize |
TRUE or FALSE (default), provide effect sizes |
ciES |
TRUE or FALSE (default), provide confidence intervals for the effect-sizes |
ciWidthES |
a number between 50 and 99.9 (default: 95), the width of confidence intervals for the effect sizes |
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. |
formula |
(optional) the formula to use, see the examples |
Details¶
The Student's independent t-test assumes that the data from each group are from a normal distribution, and that the variances of these groups are equal. If unwilling to assume the groups have equal variances, the Welch's t-test can be used in it's place. If one is additionally unwilling to assume the data from each group are from a normal distribution, the non-parametric Mann-Whitney U test can be used instead (however, note that the Mann-Whitney U test has a slightly different null hypothesis; that the distributions of each group are equal).
Output¶
A results object containing:
results$ttest |
a table containing the t-test results |
results$assum$norm |
a table containing the normality tests |
results$assum$eqv |
a table containing the homogeneity of variances tests |
results$desc |
a table containing the group descriptives |
results$plots |
an array of groups of plots |
Tables can be converted to data frames with asDF
or
as.data.frame()
. For example:
results$ttest$asDF
as.data.frame(results$ttest)
Examples¶
data('ToothGrowth')
ttestIS(formula = len ~ supp, data = ToothGrowth)
#
# INDEPENDENT SAMPLES T-TEST
#
# Independent Samples T-Test
# ----------------------------------------------------
# statistic df p
# ----------------------------------------------------
# len Student's t 1.92 58.0 0.060
# ----------------------------------------------------
#