p_function {parameters}R Documentation

p-value or consonance function

Description

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.


Conditional versus unconditional interpretations of P-values

"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).

Value

A data frame with p-values and compatibility intervals.

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

Examples


model <- lm(Sepal.Length ~ Species, data = iris)
p_function(model)

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)
[Package parameters version 0.22.1 Index]