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,
...
)
a data.frame containing the variables in the formula.
a formula of the form x ~ group
where x
is a
numeric variable giving the data values and group
is a factor with
one or multiple levels giving the corresponding groups. For example,
formula = TP53 ~ cancer_group
.
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: comparisons = list(c("A", "B"), c("B",
"C"))
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 ref.group = "all"
, pairwise two sample tests are performed for
comparing each grouping variable levels against all (i.e. basemean).
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".
a logical indicating whether you want a paired test.
a logical variable indicating whether to treat the
two variances as being equal. If TRUE
then the pooled
variance is used to estimate the variance otherwise the Welch
(or Satterthwaite) approximation to the degrees of freedom is used.
a character string specifying the alternative
hypothesis, must be one of "two.sided"
(default),
"greater"
or "less"
. You can specify just the initial
letter.
a number specifying an optional parameter used to form the null hypothesis.
confidence level of the interval.
logical value. Default is FALSE. If TRUE, a detailed result is shown.
logical value used in the function pairwise_t_test()
.
Switch to allow/disallow the use of a pooled SD.
The pool.sd = TRUE
(default) calculates a common SD for all groups
and uses that for all comparisons (this can be useful if some groups are
small). This method does not actually call t.test, so extra arguments are
ignored. Pooling does not generalize to paired tests so pool.sd and paired
cannot both be TRUE.
If pool.sd = FALSE
the standard two sample t-test is applied to all
possible pairs of groups. This method calls the t.test()
, so extra
arguments, such as var.equal
are accepted.
other arguments to be passed to the function
t.test
.
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.
- 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.
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)
#> # A tibble: 1 × 7
#> .y. group1 group2 n statistic df p
#> * <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 len 1 null model 60 19.1 59 6.94e-27
# Two-samples unpaired test
#:::::::::::::::::::::::::::::::::::::::::
df %>% t_test(len ~ supp)
#> # A tibble: 1 × 8
#> .y. group1 group2 n1 n2 statistic df p
#> * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
#> 1 len OJ VC 30 30 1.92 55.3 0.0606
# Two-samples paired test
#:::::::::::::::::::::::::::::::::::::::::
df %>% t_test (len ~ supp, paired = TRUE)
#> # A tibble: 1 × 8
#> .y. group1 group2 n1 n2 statistic df p
#> * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
#> 1 len OJ VC 30 30 3.30 29 0.00255
# 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")
#> # A tibble: 3 × 11
#> dose .y. group1 group2 n1 n2 statistic df p p.adj p.adj.…¹
#> <dbl> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 0.5 len OJ VC 10 10 3.17 15.0 0.00636 0.0191 *
#> 2 1 len OJ VC 10 10 4.03 15.4 0.00104 0.00312 **
#> 3 2 len OJ VC 10 10 -0.0461 14.0 0.964 1 ns
#> # … with abbreviated variable name ¹p.adj.signif
# pairwise comparisons
#::::::::::::::::::::::::::::::::::::::::
# As dose contains more than two levels ==>
# pairwise test is automatically performed.
df %>% t_test(len ~ dose)
#> # A tibble: 3 × 10
#> .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
#> * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 len 0.5 1 20 20 -6.48 38.0 1.27e- 7 2.54e- 7 ****
#> 2 len 0.5 2 20 20 -11.8 36.9 4.4 e-14 1.32e-13 ****
#> 3 len 1 2 20 20 -4.90 37.1 1.91e- 5 1.91e- 5 ****
# Comparison against reference group
#::::::::::::::::::::::::::::::::::::::::
# each level is compared to the ref group
df %>% t_test(len ~ dose, ref.group = "0.5")
#> # A tibble: 2 × 10
#> .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
#> * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 len 0.5 1 20 20 -6.48 38.0 1.27e- 7 1.27e- 7 ****
#> 2 len 0.5 2 20 20 -11.8 36.9 4.4 e-14 8.8 e-14 ****
# Comparison against all
#::::::::::::::::::::::::::::::::::::::::
df %>% t_test(len ~ dose, ref.group = "all")
#> # A tibble: 3 × 10
#> .y. group1 group2 n1 n2 statistic df p p.adj p.adj…¹
#> * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 len all 0.5 60 20 5.82 56.4 0.00000029 0.00000087 ****
#> 2 len all 1 60 20 -0.660 57.5 0.512 0.512 ns
#> 3 len all 2 60 20 -5.61 66.5 0.000000425 0.00000087 ****
#> # … with abbreviated variable name ¹p.adj.signif