t_test | R Documentation |
Provides a pipe-friendly framework to performs one and two sample t-tests. Read more: T-test in R.
t_test( data, formula, comparisons = NULL, ref.group = NULL, p.adjust.method = "holm", paired = FALSE, var.equal = FALSE, alternative = "two.sided", mu = 0, conf.level = 0.95, detailed = FALSE ) pairwise_t_test( data, formula, comparisons = NULL, ref.group = NULL, p.adjust.method = "holm", paired = FALSE, pool.sd = !paired, detailed = FALSE, ... )
data |
a data.frame containing the variables in the formula. |
formula |
a formula of the form |
comparisons |
A list of length-2 vectors specifying the groups of
interest to be compared. For example to compare groups "A" vs "B" and "B" vs
"C", the argument is as follow: |
ref.group |
a character string specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group). If |
p.adjust.method |
method to adjust p values for multiple comparisons. Used when pairwise comparisons are performed. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none". |
paired |
a logical indicating whether you want a paired test. |
var.equal |
a logical variable indicating whether to treat the
two variances as being equal. If |
alternative |
a character string specifying the alternative
hypothesis, must be one of |
mu |
a number specifying an optional parameter used to form the null hypothesis. |
conf.level |
confidence level of the interval. |
detailed |
logical value. Default is FALSE. If TRUE, a detailed result is shown. |
pool.sd |
logical value used in the function The If |
... |
other arguments to be passed to the function
|
- If a list of comparisons is specified, the result of the pairwise tests is filtered to keep only the comparisons of interest. The p-value is adjusted after filtering.
- For a grouped data, if pairwise test is performed, then the p-values are adjusted for each group level independently.
return a data frame with some the following columns:
.y.
: the y variable used in the test.
group1,group2
: the
compared groups in the pairwise tests.
n,n1,n2
: Sample counts.
statistic
: Test statistic used to compute the p-value.
df
: degrees of freedom.
p
: p-value.
p.adj
:
the adjusted p-value.
method
: the statistical test used to
compare groups.
p.signif, p.adj.signif
: the significance level
of p-values and adjusted p-values, respectively.
estimate
:
estimate of the effect size. It corresponds to the estimated mean or
difference in means depending on whether it was a one-sample test or a
two-sample test.
estimate1, estimate2
: show the mean values of
the two groups, respectively, for independent samples t-tests.
alternative
: a character string describing the alternative
hypothesis.
conf.low,conf.high
: Lower and upper bound on a
confidence interval.
The returned object has an attribute called args, which is a list holding the test arguments.
t_test()
: t test
pairwise_t_test()
: performs pairwise two sample t-test. Wrapper around the R
base function pairwise.t.test
.
# Load data #::::::::::::::::::::::::::::::::::::::: data("ToothGrowth") df <- ToothGrowth # One-sample test #::::::::::::::::::::::::::::::::::::::::: df %>% t_test(len ~ 1, mu = 0) # Two-samples unpaired test #::::::::::::::::::::::::::::::::::::::::: df %>% t_test(len ~ supp) # Two-samples paired test #::::::::::::::::::::::::::::::::::::::::: df %>% t_test (len ~ supp, paired = TRUE) # Compare supp levels after grouping the data by "dose" #:::::::::::::::::::::::::::::::::::::::: df %>% group_by(dose) %>% t_test(data =., len ~ supp) %>% adjust_pvalue(method = "bonferroni") %>% add_significance("p.adj") # pairwise comparisons #:::::::::::::::::::::::::::::::::::::::: # As dose contains more than two levels ==> # pairwise test is automatically performed. df %>% t_test(len ~ dose) # Comparison against reference group #:::::::::::::::::::::::::::::::::::::::: # each level is compared to the ref group df %>% t_test(len ~ dose, ref.group = "0.5") # Comparison against all #:::::::::::::::::::::::::::::::::::::::: df %>% t_test(len ~ dose, ref.group = "all")
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