ci.default: Confidence Intervals (CI)

Description Usage Arguments Value Confidence intervals and approximation of degrees of freedom Note Examples

View source: R/2_ci.R

Description

Compute confidence intervals (CI) for frequentist models.

Usage

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## Default S3 method:
ci(x, ci = 0.95, dof = NULL, method = NULL, robust = FALSE, ...)

## S3 method for class 'glmmTMB'
ci(
  x,
  ci = 0.95,
  dof = NULL,
  method = "wald",
  robust = FALSE,
  component = "all",
  verbose = TRUE,
  ...
)

## S3 method for class 'merMod'
ci(x, ci = 0.95, dof = NULL, method = "wald", robust = FALSE, ...)

Arguments

x

A statistical model.

ci

Confidence Interval (CI) level. Default to 0.95 (95%).

dof

Number of degrees of freedom to be used when calculating confidence intervals. If NULL (default), the degrees of freedom are retrieved by calling degrees_of_freedom() with approximation method defined in method. If not NULL, use this argument to override the default degrees of freedom used to compute confidence intervals.

method

Method for computing degrees of freedom for confidence intervals (CI) and the related p-values. Allowed are following options (which vary depending on the model class): "residual", "normal", "likelihood", "satterthwaite", "kenward", "wald", "profile", "boot", "uniroot", "ml1", "betwithin", "hdi", "quantile", "ci", "eti", "si", "bci", or "bcai". See section Confidence intervals and approximation of degrees of freedom in model_parameters() for further details.

robust

Logical, if TRUE, computes confidence intervals based on robust standard errors. See standard_error_robust().

...

Arguments passed down to standard_error_robust() when confidence intervals or p-values based on robust standard errors should be computed.

component

Model component for which parameters should be shown. See the documentation for your object's class in model_parameters() for further details.

verbose

Toggle warnings and messages.

Value

A data frame containing the CI bounds.

Confidence intervals and approximation of degrees of freedom

There are different ways of approximating the degrees of freedom depending on different assumptions about the nature of the model and its sampling distribution. The ci_method modulates the method for computing degrees of freedom (df) that are used to calculate confidence intervals (CI) and the related p-values. Following options are allowed, depending on the model class:

Classical methods:

Classical inference is generally based on the Wald method. The Wald approach to inference computes a test statistic by dividing the parameter estimate by its standard error (Coefficient / SE), then comparing this statistic against a t- or normal distribution. This approach can be used to compute CIs and p-values.

"wald"

"normal"

"residual"

Methods for mixed models:

Compared to fixed effects (or single-level) models, determining appropriate df for Wald-based inference in mixed models is more difficult. See the R GLMM FAQ for a discussion.

Several approximate methods for computing df are available, but you should also consider instead using profile likelihood ("profile") or bootstrap ("boot") CIs and p-values instead.

"satterthwaite"

"kenward"

"ml1"

"betwithin"

Likelihood-based methods:

Likelihood-based inference is based on comparing the likelihood for the maximum-likelihood estimate to the the likelihood for models with one or more parameter values changed (e.g., set to zero or a range of alternative values). Likelihood ratios for the maximum-likelihood and alternative models are compared to a χ-squared distribution to compute CIs and p-values.

"profile"

"uniroot"

Methods for bootstrapped or Bayesian models:

Bootstrap-based inference is based on resampling and refitting the model to the resampled datasets. The distribution of parameter estimates across resampled datasets is used to approximate the parameter's sampling distribution. Depending on the type of model, several different methods for bootstrapping and constructing CIs and p-values from the bootstrap distribution are available.

For Bayesian models, inference is based on drawing samples from the model posterior distribution.

"quantile"

"eti"

"hdi"

"bci"

"bcai"

"si"

"boot"

For all iteration-based methods other than "boot" ("hdi", "quantile", "ci", "eti", "si", "bci", "bcai"), p-values are based on the probability of direction (bayestestR::p_direction()), which is converted into a p-value using bayestestR::pd_to_p().

Note

ci_robust() resp. ci(robust = TRUE) rely on the sandwich or clubSandwich package (the latter if vcov_estimation = "CR" for cluster-robust standard errors) and will thus only work for those models supported by those packages.

Examples

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library(parameters)
if (require("glmmTMB")) {
  model <- glmmTMB(
    count ~ spp + mined + (1 | site),
    ziformula = ~mined,
    family = poisson(),
    data = Salamanders
  )

  ci(model)
  ci(model, component = "zi")
}

parameters documentation built on Oct. 19, 2021, 1:07 a.m.