Performs one and two sample t-tests.
The mosaic t.test provides wrapper functions around the function
of the same name in stats.
These wrappers provide an extended interface that allows for a more systematic
use of the formula interface.
t_test(x, ...)
t.test(x, ...)
# S3 method for formula
t_test(formula, data, ..., groups = NULL)
# S3 method for default
t_test(
x,
y = NULL,
alternative = c("two.sided", "less", "greater"),
mu = 0,
paired = FALSE,
var.equal = FALSE,
conf.level = 0.95,
...
)a (non-empty) numeric vector of data values.
further arguments to be passed to or from methods.
a formula of the form lhs ~ rhs where lhs
is a numeric variable giving the data values and rhs either
1 for a one-sample or paired test or a factor
with two levels giving the corresponding groups. If lhs is of
class "Pair" and rhs is 1, a paired test is done
an optional matrix or data frame (or similar: see
model.frame) containing the variables in the
formula formula. By default the variables are taken from
environment(formula).
When x is a formula, groups can be used to compare groups:
x = ~ var, groups = g is equivalent to x = var ~ g.
See the examples.
an optional (non-empty) numeric vector of data values.
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 indicating the true value of the mean (or difference in means if you are performing a two sample test).
a logical indicating whether you want a paired t-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.
confidence level of the interval.
an object of class htest
This is a wrapper around stats::t.test() from the stats package
to extend the functionality of the formula interface. In particular, one can
now use the formula interface for a 1-sample t-test. Before, the formula interface
was only permitted for a 2-sample test. The type of formula that can be used
for the 2-sample test has also be broadened. See the examples.
t.test(HELPrct$age)
#>
#> One Sample t-test
#>
#> data: HELPrct$age
#> t = 98.419, df = 452, p-value < 2.2e-16
#> alternative hypothesis: true mean is not equal to 0
#> 95 percent confidence interval:
#> 34.94150 36.36534
#> sample estimates:
#> mean of x
#> 35.65342
#>
# We can now do this with a formula
t.test(~ age, data = HELPrct)
#>
#> One Sample t-test
#>
#> data: age
#> t = 98.419, df = 452, p-value < 2.2e-16
#> alternative hypothesis: true mean is not equal to 0
#> 95 percent confidence interval:
#> 34.94150 36.36534
#> sample estimates:
#> mean of x
#> 35.65342
#>
# data = can be omitted, but it is better to use it
t.test(~ age, HELPrct)
#>
#> One Sample t-test
#>
#> data: age
#> t = 98.419, df = 452, p-value < 2.2e-16
#> alternative hypothesis: true mean is not equal to 0
#> 95 percent confidence interval:
#> 34.94150 36.36534
#> sample estimates:
#> mean of x
#> 35.65342
#>
# the original 2-sample formula
t.test(age ~ sex, data = HELPrct)
#>
#> Welch Two Sample t-test
#>
#> data: age by sex
#> t = 0.92976, df = 179.74, p-value = 0.3537
#> alternative hypothesis: true difference in means between group female and group male is not equal to 0
#> 95 percent confidence interval:
#> -0.8800365 2.4482932
#> sample estimates:
#> mean in group female mean in group male
#> 36.25234 35.46821
#>
# alternative 2-sample formulas
t.test(~ age | sex, data = HELPrct)
#>
#> Welch Two Sample t-test
#>
#> data: age by sex
#> t = 0.92976, df = 179.74, p-value = 0.3537
#> alternative hypothesis: true difference in means between group female and group male is not equal to 0
#> 95 percent confidence interval:
#> -0.8800365 2.4482932
#> sample estimates:
#> mean in group female mean in group male
#> 36.25234 35.46821
#>
t.test(~ age, groups = sex, data = HELPrct)
#>
#> Welch Two Sample t-test
#>
#> data: age by sex
#> t = 0.92976, df = 179.74, p-value = 0.3537
#> alternative hypothesis: true difference in means between group female and group male is not equal to 0
#> 95 percent confidence interval:
#> -0.8800365 2.4482932
#> sample estimates:
#> mean in group female mean in group male
#> 36.25234 35.46821
#>
# 2-sample t from vectors
with(HELPrct, t.test(age[sex == "male"], age[sex == "female"]))
#>
#> Welch Two Sample t-test
#>
#> data: age[sex == "male"] and age[sex == "female"]
#> t = -0.92976, df = 179.74, p-value = 0.3537
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> -2.4482932 0.8800365
#> sample estimates:
#> mean of x mean of y
#> 35.46821 36.25234
#>
# just the means
mean(age ~ sex, data = HELPrct)
#> female male
#> 36.25234 35.46821