Compute p-values and compatibility (confidence) intervals for
statistical models, at different levels. This function is also called
consonance function. It allows to see which estimates are compatible with
the model at various compatibility levels. Use `plot()`

to generate plots
of the *p* resp. *consonance* function and compatibility intervals at
different levels.

## Usage

```
p_function(
model,
ci_levels = c(0.25, 0.5, 0.75, emph = 0.95),
exponentiate = FALSE,
effects = "fixed",
component = "all",
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
consonance_function(
model,
ci_levels = c(0.25, 0.5, 0.75, emph = 0.95),
exponentiate = FALSE,
effects = "fixed",
component = "all",
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
confidence_curve(
model,
ci_levels = c(0.25, 0.5, 0.75, emph = 0.95),
exponentiate = FALSE,
effects = "fixed",
component = "all",
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
```

## Arguments

- model
Statistical Model.

- ci_levels
Vector of scalars, indicating the different levels at which compatibility intervals should be printed or plotted. In plots, these levels are highlighted by vertical lines. It is possible to increase thickness for one or more of these lines by providing a names vector, where the to be highlighted values should be named

`"emph"`

, e.g`ci_levels = c(0.25, 0.5, emph = 0.95)`

.- exponentiate
Logical, indicating whether or not to exponentiate the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. It is also recommended to use

`exponentiate = TRUE`

for models with log-transformed response values.**Note:**Delta-method standard errors are also computed (by multiplying the standard errors by the transformed coefficients). This is to mimic behaviour of other software packages, such as Stata, but these standard errors poorly estimate uncertainty for the transformed coefficient. The transformed confidence interval more clearly captures this uncertainty. For`compare_parameters()`

,`exponentiate = "nongaussian"`

will only exponentiate coefficients from non-Gaussian families.- effects
Should parameters for fixed effects (

`"fixed"`

), random effects (`"random"`

), or both (`"all"`

) be returned? Only applies to mixed models. May be abbreviated. If the calculation of random effects parameters takes too long, you may use`effects = "fixed"`

.- component
Should all parameters, parameters for the conditional model, for the zero-inflation part of the model, or the dispersion model be returned? Applies to models with zero-inflation and/or dispersion component.

`component`

may be one of`"conditional"`

,`"zi"`

,`"zero-inflated"`

,`"dispersion"`

or`"all"`

(default). May be abbreviated.- keep
Character containing a regular expression pattern that describes the parameters that should be included (for

`keep`

) or excluded (for`drop`

) in the returned data frame.`keep`

may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If`keep`

is a character vector, every parameter name in the*"Parameter"*column that matches the regular expression in`keep`

will be selected from the returned data frame (and vice versa, all parameter names matching`drop`

will be excluded). Furthermore, if`keep`

has more than one element, these will be merged with an`OR`

operator into a regular expression pattern like this:`"(one|two|three)"`

. If`keep`

is a named list of regular expression patterns, the names of the list-element should equal the column name where selection should be applied. This is useful for model objects where`model_parameters()`

returns multiple columns with parameter components, like in`model_parameters.lavaan()`

. Note that the regular expression pattern should match the parameter names as they are stored in the returned data frame, which can be different from how they are printed. Inspect the`$Parameter`

column of the parameters table to get the exact parameter names.- drop
See

`keep`

.- verbose
Toggle warnings and messages.

- ...
Arguments passed to or from other methods. Non-documented arguments are

`digits`

,`p_digits`

,`ci_digits`

and`footer_digits`

to set the number of digits for the output. If`s_value = TRUE`

, the p-value will be replaced by the S-value in the output (cf.*Rafi and Greenland 2020*).`pd`

adds an additional column with the*probability of direction*(see`bayestestR::p_direction()`

for details).`groups`

can be used to group coefficients. It will be passed to the print-method, or can directly be used in`print()`

, see documentation in`print.parameters_model()`

. Furthermore, see 'Examples' in`model_parameters.default()`

. For developers, whose interest mainly is to get a "tidy" data frame of model summaries, it is recommended to set`pretty_names = FALSE`

to speed up computation of the summary table.

## Details

### Compatibility intervals and continuous *p*-values for different estimate values

`p_function()`

only returns the compatibility interval estimates, not the
related *p*-values. The reason for this is because the *p*-value for a
given estimate value is just `1 - CI_level`

. The values indicating the lower
and upper limits of the intervals are the related estimates associated with
the *p*-value. E.g., if a parameter `x`

has a 75% compatibility interval
of `(0.81, 1.05)`

, then the *p*-value for the estimate value of `0.81`

would be `1 - 0.75`

, which is `0.25`

. This relationship is more intuitive and
better to understand when looking at the plots (using `plot()`

).

### Conditional versus unconditional interpretation of *p*-values and intervals

`p_function()`

, and in particular its `plot()`

method, aims at re-interpreting
*p*-values and confidence intervals (better named: *compatibility* intervals)
in *unconditional* terms. Instead of referring to the long-term property and
repeated trials when interpreting interval estimates (so-called "aleatory
probability", *Schweder 2018*), and assuming that all underlying assumptions
are correct and met, `p_function()`

interprets *p*-values in a Fisherian way
as "*continuous* measure of evidence against the very test hypothesis *and*
entire model (all assumptions) used to compute it"
(*P-Values Are Tough and S-Values Can Help*, lesslikely.com/statistics/s-values;
see also *Amrhein and Greenland 2022*).

This interpretation as a continuous measure of evidence against the test
hypothesis and the entire model used to compute it can be seen in the
figure below (taken from *P-Values Are Tough and S-Values Can Help*,
lesslikely.com/statistics/s-values). The "conditional" interpretation of
*p*-values and interval estimates (A) implicitly assumes certain assumptions
to be true, thus the interpretation is "conditioned" on these assumptions
(i.e. assumptions are taken as given). The unconditional interpretation (B),
however, questions all these assumptions.

"Emphasizing unconditional interpretations helps avoid overconfident and
misleading inferences in light of uncertainties about the assumptions used
to arrive at the statistical results." (*Greenland et al. 2022*).

**Note:** The term "conditional" as used by Rafi and Greenland probably has
a slightly different meaning than normally. "Conditional" in this notion
means that all model assumptions are taken as given - it should not be
confused with terms like "conditional probability". See also *Greenland et al. 2022*
for a detailed elaboration on this issue.

In other words, the term compatibility interval emphasizes "the dependence
of the *p*-value on the assumptions as well as on the data, recognizing that
*p*<0.05 can arise from assumption violations even if the effect under
study is null" (*Gelman/Greenland 2019*).

### Probabilistic interpretation of compatibility intervals

Schweder (2018) resp. Schweder and Hjort (2016) (and others) argue that
confidence curves (as produced by `p_function()`

) have a valid probabilistic
interpretation. They distinguish between *aleatory probability*, which
describes the aleatory stochastic element of a distribution *ex ante*, i.e.
before the data are obtained. This is the classical interpretation of
confidence intervals following the Neyman-Pearson school of statistics.
However, there is also an *ex post* probability, called *epistemic* probability,
for confidence curves. The shift in terminology from *confidence* intervals
to *compatibility* intervals may help emphasizing this interpretation.

In this sense, the probabilistic interpretation of *p*-values and
compatibility intervals is "conditional" - on the data *and* model assumptions
(which is in line with the "unconditional" interpretation in the sense of
Rafi and Greenland).

Ascribing a probabilistic interpretation to one realized confidence interval
is possible without repeated sampling of the specific experiment. Important
is the assumption that a *sampling distribution* is a good description of the
variability of the parameter (*Vos and Holbert 2022*). At the core, the
interpretation of a confidence interval is "I assume that this sampling
distribution is a good description of the uncertainty of the parameter. If
that's a good assumption, then the values in this interval are the most
plausible or compatible with the data". The source of confidence in
probability statements is the assumption that the selected sampling
distribution is appropriate.

"The realized confidence distribution is clearly an epistemic probability
distribution" (*Schweder 2018*). In Bayesian words, compatibility intervals
(or confidence distributons, or consonance curves) are "posteriors without
priors" (*Schweder, Hjort, 2003*). In this regard, interpretation of *p*-values
might be guided using `bayestestR::p_to_pd()`

.

### Compatibility intervals - is their interpretation conditional or not?

The fact that the term "conditional" is used in different meanings, is
confusing and unfortunate. Thus, we would summarize the probabilistic
interpretation of compatibility intervals as follows: The intervals are built
from the data *and* our modeling assumptions. The accuracy of the intervals
depends on our model assumptions. If a value is outside the interval, that
might be because (1) that parameter value isn't supported by the data, or
(2) the modeling assumptions are a poor fit for the situation. When we make
bad assumptions, the compatibility interval might be too wide or (more
commonly and seriously) too narrow, making us think we know more about the
parameter than is warranted.

When we say "there is a 95% chance the true value is in the interval", that is
a statement of *epistemic probability* (i.e. description of uncertainty related
to our knowledge or belief). When we talk about repeated samples or sampling
distributions, that is referring to *aleatoric* (physical properties) probability.
Frequentist inference is built on defining estimators with known *aleatoric*
probability properties, from which we can draw *epistemic* probabilistic
statements of uncertainty (*Schweder and Hjort 2016*).

## Note

Curently, `p_function()`

computes intervals based on Wald t- or z-statistic.
For certain models (like mixed models), profiled intervals may be more
accurate, however, this is currently not supported.

## References

Amrhein V, Greenland S. Discuss practical importance of results based on interval estimates and p-value functions, not only on point estimates and null p-values. Journal of Information Technology 2022;37:316–20. doi:10.1177/02683962221105904

Fraser DAS. The P-value function and statistical inference. The American Statistician. 2019;73(sup1):135-147. doi:10.1080/00031305.2018.1556735

Gelman A, Greenland S. Are confidence intervals better termed "uncertainty intervals"? BMJ (2019)l5381. doi:10.1136/bmj.l5381

Greenland S, Rafi Z, Matthews R, Higgs M. To Aid Scientific Inference, Emphasize Unconditional Compatibility Descriptions of Statistics. (2022) https://arxiv.org/abs/1909.08583v7 (Accessed November 10, 2022)

Rafi Z, Greenland S. Semantic and cognitive tools to aid statistical science: Replace confidence and significance by compatibility and surprise. BMC Medical Research Methodology. 2020;20(1):244. doi:10.1186/s12874-020-01105-9

Schweder T. Confidence is epistemic probability for empirical science. Journal of Statistical Planning and Inference (2018) 195:116–125. doi:10.1016/j.jspi.2017.09.016

Schweder T, Hjort NL. Confidence and Likelihood. Scandinavian Journal of Statistics. 2002;29(2):309-332. doi:10.1111/1467-9469.00285

Schweder T, Hjort NL. Frequentist analogues of priors and posteriors. In Stigum, B. (ed.), Econometrics and the Philosophy of Economics: Theory Data Confrontation in Economics, pp. 285-217. Princeton University Press, Princeton, NJ, 2003

Schweder T, Hjort NL. Confidence, Likelihood, Probability: Statistical inference with confidence distributions. Cambridge University Press, 2016.

Vos P, Holbert D. Frequentist statistical inference without repeated sampling. Synthese 200, 89 (2022). doi:10.1007/s11229-022-03560-x

## Examples

```
model <- lm(Sepal.Length ~ Species, data = iris)
p_function(model)
#> Consonance Function
#>
#> Parameter | 25% CI | 50% CI | 75% CI | 95% CI
#> --------------------------------------------------------------------------------
#> (Intercept) | [4.98, 5.03] | [4.96, 5.06] | [4.92, 5.09] | [4.86, 5.15]
#> Species [versicolor] | [0.90, 0.96] | [0.86, 1.00] | [0.81, 1.05] | [0.73, 1.13]
#> Species [virginica] | [1.55, 1.61] | [1.51, 1.65] | [1.46, 1.70] | [1.38, 1.79]
model <- lm(mpg ~ wt + as.factor(gear) + am, data = mtcars)
result <- p_function(model)
# single panels
plot(result, n_columns = 2)
# integrated plot, the default
plot(result)
```