Create a summary table for a two-sample mean test
Usage
infer_2mean_test(
data,
formula,
digits = 3,
mu0 = 0,
alternative = c("notequal", "greater", "less"),
conf_lvl = 0.95,
caption = NULL
)Arguments
- data
A data frame (or tibble).
- formula
The variables to run the test on, in formula syntax,
var1 ~ var2.- digits
The number of digits to round table values to. Defaults to 3.
- mu0
The null hypothesis value. Defaults to 0.
- alternative
The alternative hypothesis. Defaults to "notequal" (two sided p-value). Other options include "greater" or "less". Use depends on your test.
- conf_lvl
The confidence level of the interval, entered as a value between 0 and 1. Defaults to 0.95.
- caption
An override to the table caption. A sensible default is provided.
Value
An object of class flextable. In an interactive environment, results are viewable immediately.
Examples
infer_2mean_test(mtcars, wt~vs)
Two Sample Independent Means Test Between wt and vs
Null Hypothesis Value (Difference in Means): 0
p-value Reported: Two Sided Variable
n
n
Missing
Group
Means
Standard
Error
t
df
p-value
0
18
0
3.689
0.286
3.764
29.981
0.000728
1
14
0
2.611
infer_2mean_test(mtcars, wt~vs, alternative = "greater")
Two Sample Independent Means Test Between wt and vs
Null Hypothesis Value (Difference in Means): 0
p-value Reported: One Sided (Greater Than) Variable
n
n
Missing
Group
Means
Standard
Error
t
df
p-value
0
18
0
3.689
0.286
3.764
29.981
0.000364
1
14
0
2.611
infer_2mean_test(mtcars, wt~vs, conf_lvl = .9)
Two Sample Independent Means Test Between wt and vs
Null Hypothesis Value (Difference in Means): 0
p-value Reported: Two Sided Variable
n
n
Missing
Group
Means
Standard
Error
t
df
p-value
0
18
0
3.689
0.286
3.764
29.981
0.000728
1
14
0
2.611