T-test

Source: R/t_test.R

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,
  id = NULL,
  error.as.na = FALSE
)

pairwise_t_test(
  data,
  formula,
  comparisons = NULL,
  ref.group = NULL,
  p.adjust.method = "holm",
  paired = FALSE,
  pool.sd = !paired,
  detailed = FALSE,
  ...
)

Arguments

data

a data.frame containing the variables in the formula.

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.

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: comparisons = list(c("A", "B"), c("B", "C"))

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 ref.group = "all", pairwise two sample tests are performed for comparing each grouping variable levels against all (i.e. basemean).

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 TRUE then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

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.

id

(optional) character string specifying the column that contains the sample/subject identifier, used only for a paired test (paired = TRUE). When supplied, observations of the two compared groups are matched by id (instead of by row order), and only subjects present in both groups are used. For more than two groups, the matching is done independently for each pairwise comparison, so different comparisons can be based on different numbers of pairs (per-comparison pairwise deletion). This makes paired tests work when some observations are missing or the groups have unequal sizes. The default (id = NULL) keeps the previous behaviour (groups paired in row order).

error.as.na

logical. If TRUE, a comparison that cannot be computed (for example a group with fewer than two observations, or data that are essentially constant) returns an NA result row with a warning instead of stopping with an error; the other comparisons (or groups, for a grouped analysis) are still computed. Default is FALSE (the comparison errors as before).

pool.sd

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.

Value

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. For a two-sample test the difference is taken as estimate1 - estimate2, i.e. mean(group1) - mean(group2) (following t.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.

Details

- 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.

Functions

  • t_test(): t test

  • pairwise_t_test(): performs pairwise two sample t-test. Wrapper around the R base function pairwise.t.test.

Note

On the sign of statistic and estimate when a ref.group is specified: the reference group is taken as group1 and the other group as group2, and the difference is computed as estimate = mean(group1) - mean(group2) = mean(ref.group) - mean(other) (the t.test convention). A positive statistic/estimate therefore means the value is higher in the reference group. To orient results so that a positive sign means "higher in the non-reference group", flip the sign yourself, e.g. mutate(statistic = -statistic, estimate = -estimate).

See also

rstatix-programming for building the formula from variable names held in strings (e.g. reformulate()).

Examples

# 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
#>   <dbl> <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>   <dbl>   <dbl>
#> 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      
#> # ℹ 1 more variable: p.adj.signif <chr>

# 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 4.40e-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 4.40e-14 8.80e-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.signif
#> * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>     <dbl>   <dbl> <chr>       
#> 1 len   all    0.5       60    20     5.82   56.4   2.90e-7 8.69e-7 ****        
#> 2 len   all    1         60    20    -0.660  57.5   5.12e-1 5.12e-1 ns          
#> 3 len   all    2         60    20    -5.61   66.5   4.25e-7 8.69e-7 ****