この節の作者: Ravi Selker, Jonathon Love, Damian Dropmann
One Sample T-Test¶
(ttestOneS
)¶
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,
ci = FALSE,
ciWidth = 95,
effectSize = FALSE,
ciES = FALSE,
ciWidthES = 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 |
bf |
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 |
qq |
TRUE or FALSE (default), provide a Q-Q plot of residuals |
meanDiff |
TRUE or FALSE (default), provide means and standard deviations |
ci |
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 |
effectSize |
TRUE or FALSE (default), provide Cohen's d 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. |
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).
Output¶
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)
Examples¶
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
# ------------------------------------------------------
#