Perform a **Test for Practical Equivalence** for Bayesian and frequentist models.

## Usage

```
equivalence_test(x, ...)
# Default S3 method
equivalence_test(x, ...)
# S3 method for class 'data.frame'
equivalence_test(
x,
range = "default",
ci = 0.95,
rvar_col = NULL,
verbose = TRUE,
...
)
# S3 method for class 'stanreg'
equivalence_test(
x,
range = "default",
ci = 0.95,
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma",
"distributional", "auxiliary"),
parameters = NULL,
verbose = TRUE,
...
)
# S3 method for class 'brmsfit'
equivalence_test(
x,
range = "default",
ci = 0.95,
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
verbose = TRUE,
...
)
```

## Arguments

- x
Vector representing a posterior distribution. Can also be a

`stanreg`

or`brmsfit`

model.- ...
Currently not used.

- range
ROPE's lower and higher bounds. Should be

`"default"`

or depending on the number of outcome variables a vector or a list. For models with one response,`range`

can be:a vector of length two (e.g.,

`c(-0.1, 0.1)`

),a list of numeric vector of the same length as numbers of parameters (see 'Examples').

a list of

*named*numeric vectors, where names correspond to parameter names. In this case, all parameters that have no matching name in`range`

will be set to`"default"`

.

In multivariate models,

`range`

should be a list with a numeric vectors for each response variable. Vector names should correspond to the name of the response variables. If`"default"`

and input is a vector, the range is set to`c(-0.1, 0.1)`

. If`"default"`

and input is a Bayesian model,`rope_range()`

is used.- ci
The Credible Interval (CI) probability, corresponding to the proportion of HDI, to use for the percentage in ROPE.

- rvar_col
A single character - the name of an

`rvar`

column in the data frame to be processed. See example in`p_direction()`

.- verbose
Toggle off warnings.

- effects
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.

- component
Should results for all parameters, parameters for the conditional model or the zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models.

- parameters
Regular expression pattern that describes the parameters that should be returned. Meta-parameters (like

`lp__`

or`prior_`

) are filtered by default, so only parameters that typically appear in the`summary()`

are returned. Use`parameters`

to select specific parameters for the output.

## Value

A data frame with following columns:

`Parameter`

The model parameter(s), if`x`

is a model-object. If`x`

is a vector, this column is missing.`CI`

The probability of the HDI.`ROPE_low`

,`ROPE_high`

The limits of the ROPE. These values are identical for all parameters.`ROPE_Percentage`

The proportion of the HDI that lies inside the ROPE.`ROPE_Equivalence`

The "test result", as character. Either "rejected", "accepted" or "undecided".`HDI_low`

,`HDI_high`

The lower and upper HDI limits for the parameters.

## Details

Documentation is accessible for:

For Bayesian models, the **Test for Practical Equivalence** is based on the
*"HDI+ROPE decision rule"* (Kruschke, 2014, 2018) to check whether
parameter values should be accepted or rejected against an explicitly
formulated "null hypothesis" (i.e., a ROPE). In other words, it checks the
percentage of the `89%`

HDI that is the null region (the ROPE). If
this percentage is sufficiently low, the null hypothesis is rejected. If this
percentage is sufficiently high, the null hypothesis is accepted.

Using the ROPE and the HDI, Kruschke (2018)
suggests using the percentage of the `95%`

(or `89%`

, considered more stable)
HDI that falls within the ROPE as a decision rule. If the HDI
is completely outside the ROPE, the "null hypothesis" for this parameter is
"rejected". If the ROPE completely covers the HDI, i.e., all most credible
values of a parameter are inside the region of practical equivalence, the
null hypothesis is accepted. Else, it’s undecided whether to accept or
reject the null hypothesis. If the full ROPE is used (i.e., `100%`

of the
HDI), then the null hypothesis is rejected or accepted if the percentage
of the posterior within the ROPE is smaller than to `2.5%`

or greater than
`97.5%`

. Desirable results are low proportions inside the ROPE (the closer
to zero the better).

Some attention is required for finding suitable values for the ROPE limits
(argument `range`

). See 'Details' in `rope_range()`

for further
information.
**Multicollinearity: Non-independent covariates**

When parameters show strong correlations, i.e. when covariates are not
independent, the joint parameter distributions may shift towards or
away from the ROPE. In such cases, the test for practical equivalence may
have inappropriate results. Collinearity invalidates ROPE and hypothesis
testing based on univariate marginals, as the probabilities are conditional
on independence. Most problematic are the results of the "undecided"
parameters, which may either move further towards "rejection" or away
from it (Kruschke 2014, 340f).
`equivalence_test()`

performs a simple check for pairwise correlations
between parameters, but as there can be collinearity between more than two variables,
a first step to check the assumptions of this hypothesis testing is to look
at different pair plots. An even more sophisticated check is the projection
predictive variable selection (Piironen and Vehtari 2017).

## Note

There is a `print()`

-method with a `digits`

-argument to control
the amount of digits in the output, and there is a
`plot()`

-method
to visualize the results from the equivalence-test (for models only).

## References

Kruschke, J. K. (2018). Rejecting or accepting parameter values in Bayesian estimation. Advances in Methods and Practices in Psychological Science, 1(2), 270-280. doi:10.1177/2515245918771304

Kruschke, J. K. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press

Piironen, J., & Vehtari, A. (2017). Comparison of Bayesian predictive methods for model selection. Statistics and Computing, 27(3), 711–735. doi:10.1007/s11222-016-9649-y

## Examples

```
library(bayestestR)
equivalence_test(x = rnorm(1000, 0, 0.01), range = c(-0.1, 0.1))
#> # Test for Practical Equivalence
#>
#> ROPE: [-0.10 0.10]
#>
#> H0 | inside ROPE | 95% HDI
#> --------------------------------------
#> Accepted | 100.00 % | [-0.02, 0.02]
#>
#>
equivalence_test(x = rnorm(1000, 0, 1), range = c(-0.1, 0.1))
#> # Test for Practical Equivalence
#>
#> ROPE: [-0.10 0.10]
#>
#> H0 | inside ROPE | 95% HDI
#> ---------------------------------------
#> Undecided | 8.11 % | [-2.00, 1.97]
#>
#>
equivalence_test(x = rnorm(1000, 1, 0.01), range = c(-0.1, 0.1))
#> # Test for Practical Equivalence
#>
#> ROPE: [-0.10 0.10]
#>
#> H0 | inside ROPE | 95% HDI
#> -------------------------------------
#> Rejected | 0.00 % | [0.98, 1.02]
#>
#>
equivalence_test(x = rnorm(1000, 1, 1), ci = c(.50, .99))
#> # Test for Practical Equivalence
#>
#> ROPE: [-0.10 0.10]
#>
#> H0 | inside ROPE | 50% HDI
#> -------------------------------------
#> Rejected | 0.00 % | [0.31, 1.64]
#>
#>
#> H0 | inside ROPE | 99% HDI
#> ---------------------------------------
#> Undecided | 5.05 % | [-1.58, 3.65]
#>
#>
# print more digits
test <- equivalence_test(x = rnorm(1000, 1, 1), ci = c(.50, .99))
print(test, digits = 4)
#> # Test for Practical Equivalence
#>
#> ROPE: [-0.1000 0.1000]
#>
#> H0 | inside ROPE | 50% HDI
#> -----------------------------------------
#> Rejected | 0.0000 % | [0.3115, 1.7148]
#>
#>
#> H0 | inside ROPE | 99% HDI
#> -------------------------------------------
#> Undecided | 4.9495 % | [-1.7070, 3.7015]
#>
#>
# \donttest{
model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.052 seconds (Warm-up)
#> Chain 1: 0.051 seconds (Sampling)
#> Chain 1: 0.103 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 9e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.054 seconds (Warm-up)
#> Chain 2: 0.047 seconds (Sampling)
#> Chain 2: 0.101 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 9e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.051 seconds (Warm-up)
#> Chain 3: 0.05 seconds (Sampling)
#> Chain 3: 0.101 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 9e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.054 seconds (Warm-up)
#> Chain 4: 0.046 seconds (Sampling)
#> Chain 4: 0.1 seconds (Total)
#> Chain 4:
equivalence_test(model)
#> Possible multicollinearity between cyl and wt (r = 0.78). This might
#> lead to inappropriate results. See 'Details' in '?equivalence_test'.
#> # Test for Practical Equivalence
#>
#> ROPE: [-0.60 0.60]
#>
#> Parameter | H0 | inside ROPE | 95% HDI
#> -----------------------------------------------------
#> (Intercept) | Rejected | 0.00 % | [36.21, 43.06]
#> wt | Rejected | 0.00 % | [-4.74, -1.62]
#> cyl | Rejected | 0.00 % | [-2.36, -0.70]
#>
#>
# multiple ROPE ranges - asymmetric, symmetric, default
equivalence_test(model, range = list(c(10, 40), c(-5, -4), "default"))
#> Possible multicollinearity between cyl and wt (r = 0.78). This might
#> lead to inappropriate results. See 'Details' in '?equivalence_test'.
#> # Test for Practical Equivalence
#>
#> Parameter | H0 | inside ROPE | 95% HDI | ROPE
#> -----------------------------------------------------------------------
#> (Intercept) | Undecided | 58.39 % | [36.21, 43.06] | [10.00, 40.00]
#> wt | Undecided | 12.05 % | [-4.74, -1.62] | [-5.00, -4.00]
#> cyl | Rejected | 0.00 % | [-2.36, -0.70] | [-0.10, 0.10]
#>
#>
# named ROPE ranges
equivalence_test(model, range = list(wt = c(-5, -4), `(Intercept)` = c(10, 40)))
#> Possible multicollinearity between cyl and wt (r = 0.78). This might
#> lead to inappropriate results. See 'Details' in '?equivalence_test'.
#> # Test for Practical Equivalence
#>
#> Parameter | H0 | inside ROPE | 95% HDI | ROPE
#> -----------------------------------------------------------------------
#> (Intercept) | Undecided | 58.39 % | [36.21, 43.06] | [10.00, 40.00]
#> wt | Undecided | 12.05 % | [-4.74, -1.62] | [-5.00, -4.00]
#> cyl | Rejected | 0.00 % | [-2.36, -0.70] | [-0.10, 0.10]
#>
#>
# plot result
test <- equivalence_test(model)
#> Possible multicollinearity between cyl and wt (r = 0.78). This might
#> lead to inappropriate results. See 'Details' in '?equivalence_test'.
plot(test)
#> Picking joint bandwidth of 0.0895
equivalence_test(emmeans::emtrends(model, ~1, "wt", data = mtcars))
#> # Test for Practical Equivalence
#>
#> ROPE: [-0.10 0.10]
#>
#> X1 | H0 | inside ROPE | 95% HDI
#> -------------------------------------------------
#> overall | Rejected | 0.00 % | [-4.74, -1.62]
#>
#>
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 7e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.023 seconds (Warm-up)
#> Chain 1: 0.022 seconds (Sampling)
#> Chain 1: 0.045 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 4e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.025 seconds (Warm-up)
#> Chain 2: 0.023 seconds (Sampling)
#> Chain 2: 0.048 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 4e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.022 seconds (Warm-up)
#> Chain 3: 0.015 seconds (Sampling)
#> Chain 3: 0.037 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 3e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.024 seconds (Warm-up)
#> Chain 4: 0.018 seconds (Sampling)
#> Chain 4: 0.042 seconds (Total)
#> Chain 4:
equivalence_test(model)
#> Possible multicollinearity between b_cyl and b_wt (r = 0.78). This might
#> lead to inappropriate results. See 'Details' in '?equivalence_test'.
#> # Test for Practical Equivalence
#>
#> ROPE: [-0.60 0.60]
#>
#> Parameter | H0 | inside ROPE | 95% HDI
#> ---------------------------------------------------
#> Intercept | Rejected | 0.00 % | [36.19, 43.16]
#> wt | Rejected | 0.00 % | [-4.71, -1.64]
#> cyl | Rejected | 0.00 % | [-2.36, -0.68]
#>
#>
bf <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
# equivalence_test(bf)
# }
```