Methods for TOSTt objects — TOSTt-methods • TOSTER

Methods defined for objects returned from the t_TOST and boot_t_TOST functions.

# S3 method for TOSTt
print(x, digits = 4, ...)

# S3 method for TOSTt
plot(
  x,
  type = c("simple", "cd", "c", "tnull"),
  estimates = c("raw", "SMD"),
  ci_lines,
  ci_shades,
  ...
)

describe(x, ...)

# S3 method for TOSTt
describe(x, digits = 3, ...)

Arguments

x: object of class TOSTt.
digits: Number of digits to print for p-values
...: further arguments passed through, see description of return value for details..
type: Type of plot to produce. Default is a consonance density plot "cd". Consonance plots (type = "cd") and null distribution plots (type = "tnull") can also be produced. Note: null distribution plots only available for estimates = "raw".
estimates: indicator of what estimates to plot; options include "raw" or "SMD". Default is is both: c("raw","SMD").
ci_lines: Confidence interval lines for plots. Default is 1-alpha*2 (e.g., alpha = 0.05 is 90%)
ci_shades: Confidence interval shades when plot type is "cd".

Value

print: Prints short summary of the tests.
plot: Returns a plot of the effects.
describe: Verbose description of results.

Examples

# example code
# Print

res1 = t_TOST(mpg ~ am, data = mtcars, eqb = 3)

res1
#> 
#> Welch Two Sample t-test
#> 
#> The equivalence test was non-significant, t(18.33) = -2.2, p = 0.98
#> The null hypothesis test was significant, t(18.33) = -3.77, p < 0.01
#> NHST: reject null significance hypothesis that the effect is equal to zero 
#> TOST: don't reject null equivalence hypothesis
#> 
#> TOST Results 
#>                 t    df p.value
#> t-test     -3.767 18.33   0.001
#> TOST Lower -2.207 18.33    0.98
#> TOST Upper -5.327 18.33 < 0.001
#> 
#> Effect Sizes 
#>                Estimate     SE                C.I. Conf. Level
#> Raw              -7.245 1.9232 [-10.5766, -3.9133]         0.9
#> Hedges's g(av)   -1.360 0.4419   [-2.032, -0.6603]         0.9
#> Note: SMD confidence intervals are an approximation. See vignette("SMD_calcs").
# Print with more digits
print(res1, digits = 6)
#> 
#> Welch Two Sample t-test
#> 
#> The equivalence test was non-significant, t(18.33) = -2.2, p = 0.98
#> The null hypothesis test was significant, t(18.33) = -3.77, p < 0.01
#> NHST: reject null significance hypothesis that the effect is equal to zero 
#> TOST: don't reject null equivalence hypothesis
#> 
#> TOST Results 
#>                   t      df p.value
#> t-test     -3.76712 18.3323   0.001
#> TOST Lower -2.20722 18.3323    0.98
#> TOST Upper -5.32702 18.3323 < 0.001
#> 
#> Effect Sizes 
#>                Estimate       SE                    C.I. Conf. Level
#> Raw            -7.24494 1.923202 [-10.576623, -3.913256]         0.9
#> Hedges's g(av) -1.35989 0.441871   [-2.031979, -0.66034]         0.9
#> Note: SMD confidence intervals are an approximation. See vignette("SMD_calcs").

# Plot with density plot - only raw values (SLOW)
#plot(res1, type = "cd", estimates = "raw")
# Plot with consonance - only raw values (SLOW)
#plot(res1, type = "c", estimates = "raw")
# Plot null distribution - only raw values
#plot(res1, type = "tnull", estimates = "raw")

# Get description of the results
describe(res1)
#> [1] "Using the Welch Two Sample t-test, a null hypothesis significance test (NHST), and a equivalence test, via two one-sided tests (TOST), were performed with an alpha-level of 0.05. These tested the null hypotheses that true mean difference is equal to 0 (NHST), and true mean difference is more extreme than -3 and 3 (TOST). The equivalence test was not significant (p = 0.98). The NHST was significant, t(18.332) = -3.77, p = 0.001 (mean difference = -7.24 90% C.I.[-10.6, -3.91]; Hedges's g(av) = -1.36 90% C.I.[-2.03, -0.66]). At the desired error rate, it can be stated that the true mean difference is not equal to 0 (i.e., no equivalence)."