Package {geer}

Type:	Package
Title:	Bias-Reduced and Penalized Generalized Estimating Equations
Version:	0.1.0
Description:	Fits marginal regression models for repeated or clustered responses using generalized estimating equations (GEE). Provides ordinary GEE estimators, bias-reduced and bias-corrected GEE estimators, and Jeffreys-type penalized GEE estimators for binary, count and continuous responses. Methods are described in Touloumis (2026a) <doi:10.48550/arXiv.2606.16043> and Touloumis (2026b) <doi:10.48550/arXiv.2606.16058>.
License:	GPL-3
URL:	https://github.com/AnestisTouloumis/geer
BugReports:	https://github.com/AnestisTouloumis/geer/issues
Depends:	R (≥ 4.0.0)
Imports:	brglm2, generics, Rcpp (≥ 1.0.9)
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	covr, emmeans (≥ 2.0.0), estimability, testthat (≥ 3.0.0), tibble
Encoding:	UTF-8
LazyData:	true
Config/testthat/edition:	3
Config/roxygen2/version:	8.0.0
NeedsCompilation:	yes
Packaged:	2026-06-18 12:42:46 UTC; Anestis
Author:	Anestis Touloumis [aut, cre]
Maintainer:	Anestis Touloumis <A.Touloumis@brighton.ac.uk>
Repository:	CRAN
Date/Publication:	2026-06-23 14:50:02 UTC

Bias-Reduced and Penalized Generalized Estimating Equations

Description

Fits marginal models for repeated or clustered responses using Generalized Estimating Equations (GEE). Supported estimation methods include the traditional GEE, bias-reducing GEE, bias-correcting GEE, and Jeffreys-prior penalized GEE. Continuous, binary and count responses are handled by geewa, while binary responses can also be handled by geewa_binary through an odds-ratio parameterization.

Author(s)

Anestis Touloumis A.Touloumis@brighton.ac.uk

References

Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika, 73, 13–22.

Touloumis, A. (2026) Jeffreys-Type Penalized GEE for Correlated Binary Data with an Odds-Ratio Parameterization. Preprint. https://arxiv.org/abs/2606.16058

Touloumis, A. (2026) Bias-Reduced GEE via Adjusted Estimating Equations, with Odds-Ratio Extensions. Preprint. https://arxiv.org/abs/2606.16043

See Also

Main functions:

• geewa for continuous, binary and count responses.

• geewa_binary for binary responses via an odds-ratio parameterization (preferred).

• geer_control for convergence and fitting options.

• geecriteria for model selection criteria.

• summary.geer, tidy.geer, and glance.geer for model summaries.

• anova.geer, add1.geer, drop1.geer, and step_p for model building.

• vcov.geer, confint.geer, predict.geer, fitted.geer, residuals.geer, and frechet_bounds_cor for inference and model diagnostics.

Add or Drop Single Terms to or from a geer Model

Description

Computes all single terms in the scope argument that can be added to or dropped from a fitted geer model, fits the corresponding models, and returns a table summarizing the resulting changes in fit.

Usage

## S3 method for class 'geer'
add1(
  object,
  scope,
  test = c("wald", "score", "working-wald", "working-score", "working-lrt"),
  cov_type = c("bias-corrected", "robust", "df-adjusted", "naive"),
  pmethod = c("rao-scott", "satterthwaite"),
  ...
)

## S3 method for class 'geer'
drop1(
  object,
  scope,
  test = c("wald", "score", "working-wald", "working-score", "working-lrt"),
  cov_type = c("bias-corrected", "robust", "df-adjusted", "naive"),
  pmethod = c("rao-scott", "satterthwaite"),
  ...
)

Arguments

Details

For add1.geer(), scope must specify the candidate terms to be added. If no eligible terms are supplied, an error is returned. In scope formulas, . denotes the set of terms already included in the model.

Model hierarchy is enforced: candidate additions and deletions must preserve marginality, so if a higher-order interaction is present, all of its lower-order component terms must also remain in the model.

Details of the hypothesis tests controlled by test are given in Rotnitzky and Jewell (1990). The option test = "working-lrt" is valid only when the model is fitted with an independence working association structure; otherwise an error is returned.

When test %in% c("wald", "score"), the pmethod argument is ignored and cov_type specifies the covariance estimator used to compute the test statistic. For modified working tests, cov_type determines the covariance matrix used to form the coefficients of the sum of independent chi-squared random variables, and pmethod specifies the approximation used to compute the p-value.

The output table also includes the Correlation Information Criterion (CIC) for each candidate model, which can be used to guide selection of the working association structure.

Value

An object of class "anova" summarizing the differences in fit between the models. For add1.geer() and drop1.geer(), the table contains one row for the current model and one row for each admissible single-term addition or deletion. Columns include Df (degrees of freedom of the test), CIC (Correlation Information Criterion), Chi (test statistic), and Pr(>Chi) (p-value).

References

Rotnitzky, A. and Jewell, N.P. (1990) Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data. Biometrika, 77, 485–497.

See Also

anova.geer, step_p, geecriteria, geewa, geewa_binary.

Examples

data("respiratory", package = "geer")
respiratory2 <- respiratory[respiratory$center == "C2", , drop = FALSE]

fitted_model <- geewa(
  formula = status ~ baseline + I(treatment == "active") + gender + visit + age,
  family = binomial(link = "probit"),
  data = respiratory2,
  id = id,
  repeated = visit,
  corstr = "ar1",
  method = "gee"
)
add1(
  fitted_model,
  scope = . ~ . + baseline:age + age:visit + I(treatment == "active"):age + age:gender,
  test = "score"
)

data("respiratory", package = "geer")
respiratory2 <- respiratory[respiratory$center == "C2", , drop = FALSE]

fitted_model <- geewa(
  formula = status ~ baseline + I(treatment == "active") + gender + visit + age,
  family = binomial(link = "probit"),
  data = respiratory2,
  id = id,
  repeated = visit,
  corstr = "ar1",
  method = "gee"
)
drop1(fitted_model, test = "score")

ANOVA Tables for geer Objects

Description

Computes hypothesis test tables for one or more fitted geer objects, analogous to analysis-of-variance (ANOVA) tables for GLM models.

Usage

## S3 method for class 'geer'
anova(
  object,
  ...,
  test = c("wald", "score", "working-wald", "working-score", "working-lrt"),
  cov_type = c("bias-corrected", "robust", "df-adjusted", "naive"),
  pmethod = c("rao-scott", "satterthwaite")
)

Arguments

Details

Details of the hypothesis tests controlled by test are given in Rotnitzky and Jewell (1990). The option test = "working-lrt" is valid only when the model is fitted with an independence working association structure; otherwise an error is returned.

When test %in% c("wald", "score"), the pmethod argument is ignored and cov_type specifies the covariance estimator used to compute the test statistic. For modified working tests, cov_type determines the covariance matrix used to form the coefficients of the sum of independent chi-squared random variables, and pmethod specifies the approximation used to compute the p-value.

When comparing two or more models, the data must be identical across all fits, and the models must be nested in the order supplied. In particular, each consecutive pair of models must be nested.

Value

An object of class c("anova", "data.frame"). With a single model, the table reports tests for terms added sequentially (first to last). With multiple models, the table reports sequential comparisons between each consecutive pair of nested models. Columns include Df (degrees of freedom of the test), Resid. Df (residual degrees of freedom), Chi (test statistic), and Pr(>Chi) (p-value). Unlike add1.geer and drop1.geer, no CIC column is included; use geecriteria for working association structure selection.

References

Rotnitzky, A. and Jewell, N.P. (1990) Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data. Biometrika, 77, 485–497.

See Also

add1.geer, drop1.geer for type II tests where each term is dropped one at a time while respecting model hierarchy; step_p for stepwise model selection; geecriteria for model comparison criteria.

Examples

data("cerebrovascular", package = "geer")

## Single-model ANOVA (sequential terms)
fit_full <- geewa_binary(
  formula = ecg ~ treatment + factor(period),
  link = "logit",
  data = cerebrovascular,
  id = id,
  orstr = "exchangeable"
)
anova(fit_full, test = "wald", cov_type = "robust")

## Two-model comparison (models must be nested)
fit_null <- geewa_binary(
  formula = ecg ~ 1,
  link = "logit",
  data = cerebrovascular,
  id = id,
  orstr = "exchangeable"
)
anova(fit_null, fit_full, test = "wald", cov_type = "robust")

Cerebrovascular Deficiency Trial

Description

Data from a randomized two-period crossover trial on cerebrovascular deficiency.

Usage

cerebrovascular

Format

A data frame with 134 rows and 4 columns:

id

integer subject identifier.

period

integer period identifier.

ecg

binary indicator of ECG status, 0 = abnormal and 1 = normal.

treatment

factor with levels active and placebo.

Details

Sixty-seven subjects were enrolled in a two-period crossover trial, yielding 134 observations (two per subject). Subjects were randomly assigned to receive either placebo followed by active treatment, or active treatment followed by placebo. ECG status was classified as normal or abnormal at each period.

References

Jones, B. and Kenward, M.G. (1989) Design and Analysis of Cross-over Trials. London: Chapman and Hall/CRC Press.

Examples

data("cerebrovascular", package = "geer")
str(cerebrovascular)

Shoulder Pain After Laparoscopic Cholecystectomy Trial

Description

Data from a randomized clinical trial on shoulder pain after laparoscopic cholecystectomy.

Usage

cholecystectomy

Format

A data frame with 246 rows and 6 columns:

id

integer subject identifier.

time

integer time identifier.

pain

binary indicator of shoulder pain status, 0 = high pain and 1 = low pain.

treatment

factor with levels active and placebo.

gender

factor with levels female and male.

age

numeric age in years.

Details

Forty-one subjects were enrolled in a randomized clinical trial of shoulder pain after laparoscopic cholecystectomy, yielding 246 observations (six per subject). Subjects were assigned to either the active group (with abdominal suction) or the placebo group (without abdominal suction). Shoulder pain was assessed on six occasions: morning and afternoon on each of the first three postoperative days.

Source

Lumley, T. (1996) Generalized estimating equations for ordinal data: a note on working correlation structures. Biometrics, 52, 354–361.

References

Jorgensen, J.O., Gillies, R.B., Hunt, D.R., Caplehorn, J.R.M. and Lumley, T. (1995) A simple and effective way to reduce postoperative pain after laparoscopic cholecystectomy. Australian and New Zealand Journal of Surgery, 65, 466–469.

Examples

data("cholecystectomy", package = "geer")
str(cholecystectomy)

Extract Model Coefficients from a geer Object

Description

Extracts the estimated regression coefficients from a fitted geer object. coefficients is an alias for coef.

Usage

## S3 method for class 'geer'
coef(object, ...)

Arguments

Value

A named numeric vector of estimated regression coefficients. The names correspond to the columns of the model matrix.

See Also

vcov.geer, confint.geer, summary.geer.

Examples

data("leprosy", package = "geer")
fit <- geewa(
  formula = bacilli ~ factor(period) + factor(period):treatment,
  family = poisson(link = "log"),
  data = leprosy,
  id = id
)
coef(fit)

data("cerebrovascular", package = "geer")
fit_bin <- geewa_binary(
  formula = ecg ~ treatment + factor(period),
  link = "logit",
  data = cerebrovascular,
  id = id
)
coef(fit_bin)

Confidence Intervals for Model Parameters from a geer Object

Description

Computes Wald-type confidence intervals for one or more regression parameters from a fitted geer object.

Usage

## S3 method for class 'geer'
confint(
  object,
  parm,
  level = 0.95,
  cov_type = c("bias-corrected", "robust", "df-adjusted", "naive"),
  ...
)

Arguments

Details

Confidence intervals are computed as \hat{\beta} \pm z_{1-\alpha/2} \, \mathrm{SE}(\hat{\beta}), where standard errors are obtained from vcov(object, cov_type = cov_type). The covariance estimator used is controlled by cov_type; see vcov.geer for details. The resulting intervals rely on the usual large-sample normal approximation.

Value

A matrix with columns giving lower and upper confidence limits for each parameter. The columns are labeled by the corresponding tail probabilities in percent, for example "2.5%" and "97.5%" when level = 0.95.

See Also

vcov.geer, coef.geer, tidy.geer.

Examples

data("cerebrovascular", package = "geer")
fit <- geewa_binary(
  formula = ecg ~ treatment + factor(period),
  link = "logit",
  data = cerebrovascular,
  id = id,
  orstr = "exchangeable"
)
confint(fit)
confint(fit, parm = "treatmentactive")
confint(fit, cov_type = "naive")

Postnatal Depression Oestrogen Patch Trial

Description

Data from a randomized clinical trial on the efficacy of oestrogen patches in treating postnatal depression.

Usage

depression

Format

A data frame with 366 rows and 5 columns:

id

integer subject identifier.

visit

integer follow-up month identifier.

score

numeric Edinburgh Postnatal Depression Scale (EPDS) score. Higher scores indicate greater depression.

treatment

factor with levels placebo and oestrogen.

baseline

numeric EPDS score at baseline.

Details

Sixty-one women with major depression were randomly assigned to either a placebo control group or an oestrogen patch group, yielding 366 observations (six monthly assessments per subject). Before treatment, depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS). EPDS scores were then collected monthly for six months.

Source

https://stats.oarc.ucla.edu/spss/library/spss-librarypanel-data-analysis-using-gee/

References

Gregoire, A.J.P., Kumar, R., Everitt, B., Henderson, A.F. and Studd, J.W.W. (1996) Transdermal oestrogen for treatment of severe postnatal depression. The Lancet, 347, 930–933.

Examples

data("depression", package = "geer")
str(depression)

emmeans Support for geer Objects

Description

Methods that allow fitted geer objects to work with the emmeans package.

These functions are used internally by emmeans and are not usually called directly by end users. Once both packages are installed, functions such as emmeans::emmeans() and emmeans::ref_grid() can be applied to fitted geer objects.

By default, emmeans calculations for geer objects use the bias-corrected covariance matrix. Alternative covariance estimators may be requested via vcov.method, with supported character values "bias-corrected", "robust", "df-adjusted", and "naive".

In line with the large-sample inference used elsewhere in geer, these methods use asymptotic inference with degrees of freedom equal to Inf.

Usage

recover_data.geer(object, data = NULL, ...)

emm_basis.geer(
  object,
  trms,
  xlev,
  grid,
  vcov.method = "bias-corrected",
  cov_type = NULL,
  vcov. = NULL,
  misc = NULL,
  ...
)

Arguments

Value

recover_data.geer() returns a recovered predictor data frame for use by emmeans. emm_basis.geer() returns the basis components required by emmeans to construct an emmGrid object.

See Also

vcov.geer, geewa, geewa_binary.

Examples

if (requireNamespace("emmeans", quietly = TRUE)) {
  data("cerebrovascular", package = "geer")

  fit <- geewa_binary(
    formula = ecg ~ treatment + factor(period),
    link = "logit",
    data = cerebrovascular,
    id = id,
    orstr = "exchangeable"
  )

  emmeans::emmeans(fit, ~ treatment)
  emmeans::emmeans(fit, ~ treatment, type = "response")
  emmeans::emmeans(fit, ~ treatment, vcov.method = "naive")
}

Progabide Epilepsy Trial

Description

Data from a randomized clinical trial on the efficacy of progabide in treating partial seizures.

Usage

epilepsy

Format

A data frame with 236 rows and 6 columns:

id

integer subject identifier.

visit

integer visit identifier corresponding to a two-week interval.

seizures

integer count of epileptic seizures.

treatment

factor with levels placebo and progabide.

lnbaseline

numeric logarithm of one-quarter of the number of seizures in the baseline 8-week interval.

lnage

numeric logarithm of age in years.

Details

Fifty-nine subjects with partial seizures were enrolled in a randomized clinical trial, yielding 236 observations (four visits per subject). Subjects were assigned to receive either progabide or placebo. Seizure counts were recorded during the 8 weeks before treatment (baseline). After treatment, seizure counts were assessed at four 2-week clinic visits.

Source

Thall, P.F. and Vail, S.C. (1990) Some covariance models for longitudinal count data with overdispersion. Biometrics, 46, 657–671.

References

Carey, V.J. and Wang, Y.G. (2011) Working covariance model selection for generalized estimating equations. Statistics in Medicine, 30, 3117–3124.

Examples

data("epilepsy", package = "geer")
str(epilepsy)

Extract Model Fitted Values from a geer Object

Description

Extracts the fitted mean values on the response scale from a fitted geer object. fitted.values is an alias for fitted.

Usage

## S3 method for class 'geer'
fitted(object, ...)

Arguments

Value

A numeric vector of fitted mean values on the response scale, of the same length as the number of observations used in fitting.

See Also

residuals.geer, predict.geer.

Examples

data("leprosy", package = "geer")
fit <- geewa(
  formula = bacilli ~ factor(period) + factor(period):treatment,
  family = poisson(link = "log"),
  data = leprosy,
  id = id
)
head(fitted(fit))

Frechet Bounds for a Working Correlation Matrix

Description

For a fitted geer model from geewa with a binomial family and a non-independence association structure, checks whether each off-diagonal entry of the working correlation matrix lies within the Frechet bounds implied by the fitted marginal probabilities. Results are summarized at the time-pair level.

Usage

frechet_bounds_cor(object)

Arguments

Details

For a pair of observations at times j and k within cluster i, with fitted marginal probabilities \pi_{ij} and \pi_{ik}, the Frechet bounds on their correlation are

\ell_{ijk} = \max\!\left( -\sqrt{\frac{\pi_{ij}\pi_{ik}}{(1-\pi_{ij})(1-\pi_{ik})}},\; -\sqrt{\frac{(1-\pi_{ij})(1-\pi_{ik})}{\pi_{ij}\pi_{ik}}} \right)

u_{ijk} = \min\!\left( \sqrt{\frac{\pi_{ij}(1-\pi_{ik})}{\pi_{ik}(1-\pi_{ij})}},\; \sqrt{\frac{\pi_{ik}(1-\pi_{ij})}{\pi_{ij}(1-\pi_{ik})}} \right)

The working correlation value cor for a time pair (j, k) is the same for all clusters and is read from the fitted working correlation matrix. The bounds \ell_{ijk} and u_{ijk} vary across clusters because they depend on the cluster-specific fitted probabilities. The tightest bounds across clusters, lower_max (maximum lower bound) and upper_min (minimum upper bound), are reported in the returned data frame. The column n_violated counts the number of clusters for which cor falls outside (\ell_{ijk},\, u_{ijk}).

Value

a data frame with one row per unique time pair (j, k) and columns:

alpha_name

label of the form alpha_j.k identifying the working correlation entry for this time pair.

alpha_value

working correlation value for the time pair.

lower_max

maximum Frechet lower bound across clusters, giving the tightest lower admissibility constraint.

upper_min

minimum Frechet upper bound across clusters, giving the tightest upper admissibility constraint.

n_violated

number of clusters for which alpha_value falls outside the cluster-specific Frechet bounds.

See Also

geewa.

Examples

data("cholecystectomy", package = "geer")

fit <- geewa(
  formula = pain ~ treatment + gender + age + I(time >4),
  family = binomial(link = "logit"),
  data = cholecystectomy,
  id = id,
  repeated = time,
  corstr = "unstructured",
  method = "gee"
)
frechet_bounds_cor(fit)

Model Selection Criteria for geer Objects

Description

Computes model selection criteria for one or more fitted geer objects, supporting both marginal mean model comparison and working association structure selection.

Usage

geecriteria(
  object,
  ...,
  cov_type = c("bias-corrected", "robust", "df-adjusted", "naive"),
  digits = 2
)

Arguments

Details

The reported criteria are:

QIC

Quasi Information Criterion for comparing marginal mean models. Smaller values are preferred.

CIC

Correlation Information Criterion, used here for selecting the working association structure. Smaller values are preferred.

RJC

Rotnitzky and Jewell Criterion. Smaller values are preferred.

QICu

A variant of QIC primarily intended for comparing marginal mean models with different covariate sets. Smaller values are preferred.

GESSC

Generalized Error Sum of Squares Criterion. Smaller values are preferred.

GPC

Gaussian Pseudolikelihood Criterion. Unlike the other criteria, larger values are preferred, because GPC is a pseudolikelihood measure rather than an information criterion.

Parameters

Number of regression parameters in the marginal mean model.

The cov_type argument affects only the covariance-based criteria QIC, CIC, and RJC; the remaining criteria are unaffected.

If the supplied models do not all have the same number of observations, a warning is issued.

Value

A data frame with one row per fitted model and columns QIC, CIC, RJC, QICu, GESSC, GPC, and Parameters, as described in the Details section. When more than one model is supplied, row names are set to the deparsed model expressions.

References

Carey, V.J. and Wang, Y.G. (2011) Working covariance model selection for generalized estimating equations. Statistics in Medicine, 30, 3117–3124.

Chaganty, N.R. and Shults, J. (1999) On eliminating the asymptotic bias in the quasi-least squares estimate of the correlation parameter. Journal of Statistical Planning and Inference, 76, 145–161.

Hin, L.Y. and Wang, Y.G. (2009) Working correlation structure identification in generalized estimating equations. Statistics in Medicine, 28, 642–658.

Pan, W. (2001) Akaike's information criterion in generalized estimating equations. Biometrics, 57, 120–125.

Rotnitzky, A. and Jewell, N.P. (1990) Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data. Biometrika, 77, 485–497.

See Also

glance.geer, anova.geer, geewa, geewa_binary.

Examples

## Single model
data("epilepsy", package = "geer")
fit <- geewa(
  formula = seizures ~ treatment + lnbaseline + lnage,
  family = poisson(link = "log"),
  data = epilepsy,
  id = id,
  corstr = "exchangeable"
)
geecriteria(fit)

## Compare working correlation structures
fit_ind <- update(fit, corstr = "independence")
fit_ar1 <- update(fit, corstr = "ar1")
geecriteria(fit_ind, fit, fit_ar1)

## Compare estimation methods
data("cerebrovascular", package = "geer")
fitted_gee <- geewa_binary(
  formula = ecg ~ factor(period) * treatment,
  link = "logit",
  data = cerebrovascular,
  id = id,
  orstr = "exchangeable",
  method = "gee"
)
fitted_brgee <- update(fitted_gee, method = "brgee-robust")
geecriteria(fitted_gee, fitted_brgee, cov_type = "robust")

Control Parameters for geer Model Fitting

Description

Creates a list of control parameters for iterative fitting in geewa and geewa_binary. This function is used internally, but may also be supplied directly through the control argument of those functions.

Usage

geer_control(
  tolerance = 1e-06,
  maxiter = 500,
  or_adding = 0.5,
  step_maxiter = 10,
  step_multiplier = 1,
  jeffreys_power = 0.5
)

Arguments

Details

The jeffreys_power argument is used only when method %in% c("pgee-jeffreys", "opgee-jeffreys", "hpgee-jeffreys") in geewa or geewa_binary.

Value

A named list with elements tolerance, maxiter, or_adding, step_maxiter, step_multiplier, and jeffreys_power, suitable for use as the control argument to geewa or geewa_binary.

References

Touloumis, A. (2026) Jeffreys-prior penalized GEE for correlated binary data with an odds-ratio parameterization. Preprint.

See Also

geewa, geewa_binary.

Examples

## Default control parameters
geer_control()

## Tighter convergence tolerance and fewer maximum iterations
geer_control(tolerance = 1e-8, maxiter = 200)

## Custom continuity correction for odds-ratio estimation
geer_control(or_adding = 1)

## Weaker Jeffreys-prior penalty for Jeffreys-type fits
geer_control(jeffreys_power = 0.1)

Fitting (Adjusted) Generalized Estimating Equations

Description

Fits a marginal model for repeated or clustered responses using Generalized Estimating Equations (GEE). Supported estimation methods include the traditional GEE, bias-reducing GEE, bias-correcting GEE, and Jeffreys-prior penalized GEE.

Usage

geewa(
  formula,
  family = gaussian(link = "identity"),
  data = parent.frame(),
  id,
  repeated,
  control = geer_control(...),
  corstr = "independence",
  Mv = 1,
  method = "gee",
  weights,
  beta_start = NULL,
  offset,
  control_glm = list(...),
  use_p = TRUE,
  alpha_vector = NULL,
  phi_fixed = FALSE,
  phi_value = 1,
  ...
)

Arguments

Details

method specifies the estimation approach. If method = "gee", the standard GEE are solved with no adjustment. If method is one of "brgee-naive", "brgee-robust" or "brgee-empirical", an adjustment vector is added to produce naive, robust or empirical bias-reduced estimators, respectively. If method is one of "bcgee-naive", "bcgee-robust" or "bcgee-empirical", the corresponding bias-corrected estimators are produced via a one-step correction applied to the converged GEE solution. If method = "pgee-jeffreys", the GEE are penalized using a Jeffreys-prior penalty run to full convergence. If method = "opgee-jeffreys", a single penalized scoring step is performed from the converged independence penalized solution (one-step approximation). If method = "hpgee-jeffreys", a single standard GEE scoring step is performed from the converged independence penalized solution (hybrid one-step approximation).

For the construction of the formula argument, see the documentation of glm and formula.

The data must be in long format (one row per observation). See reshape for details on reshaping between long and wide formats.

The quasi, quasibinomial and quasipoisson families are internally remapped to their standard parametric equivalents before fitting: quasibinomial to binomial, quasipoisson to poisson, and quasi to the family matching its variance function (gaussian, binomial, poisson, Gamma, or inverse.gaussian). The scale parameter phi is then estimated freely from the data unless phi_fixed = TRUE.

The default set for the id labels is \{1,\ldots,N\}, where N is the number of clusters. Otherwise, the function recodes the given labels of id onto this set.

The argument repeated can be safely omitted only if observations are already ordered within each cluster as intended. If repeated is not provided, it is created as 1, 2, ..., n_i within each cluster i, using the current row order (before internal sorting). If repeated is provided, its labels are recoded to 1, ..., T and must be unique within each cluster.

The variables id and repeated do not need to be pre-sorted. Instead the function sorts observations in ascending order of id and repeated.

A term of the form offset(expression) is allowed in the right-hand side of formula.

The length of id and of repeated or weights (when provided) must equal the number of observations.

Value

An object of class "geer", a list with components:

Note on returned components

For geewa, the alpha contains the estimated (or fixed) working correlation parameters. The association_structure component stores the value of corstr. Under corstr = "independence", alpha is set to 0.

For method in "bcgee-naive", "bcgee-robust", "bcgee-empirical", "opgee-jeffreys", and "hpgee-jeffreys", converged is always TRUE in the returned object.

References

Touloumis, A. (2026) Jeffreys-Type Penalized GEE for Correlated Binary Data with an Odds-Ratio Parameterization. Preprint. https://arxiv.org/abs/2606.16058

Touloumis, A. (2026) Bias-Reduced GEE via Adjusted Estimating Equations, with Odds-Ratio Extensions. Preprint. https://arxiv.org/abs/2606.16043

See Also

geewa_binary, geer_control, summary.geer, geecriteria.

Examples

data("epilepsy", package = "geer")
fitted_model_gee <- geewa(
  formula = seizures ~ treatment + lnbaseline + lnage,
  family = poisson(link = "log"),
  data = epilepsy,
  id = id,
  corstr = "exchangeable",
  method = "gee"
)
summary(fitted_model_gee, cov_type = "bias-corrected")

fitted_model_brgee_robust <- update(fitted_model_gee, method = "brgee-robust")
summary(fitted_model_brgee_robust, cov_type = "bias-corrected")

fitted_model_brgee_naive <- update(fitted_model_gee, method = "brgee-naive")
summary(fitted_model_brgee_naive, cov_type = "bias-corrected")

fitted_model_brgee_empirical <- update(fitted_model_gee, method = "brgee-empirical")
summary(fitted_model_brgee_empirical, cov_type = "bias-corrected")

fitted_model_bcgee_robust <- update(fitted_model_gee, method = "bcgee-robust")
summary(fitted_model_bcgee_robust, cov_type = "robust")


## Penalized GEE with custom control
fitted_model_pgee <- geewa(
  formula = seizures ~ treatment + lnbaseline + lnage,
  family = poisson(link = "log"),
  data = epilepsy,
  id = id,
  corstr = "exchangeable",
  method = "pgee-jeffreys",
  control = geer_control(jeffreys_power = 0.1)
)
summary(fitted_model_pgee, cov_type = "robust")

Fitting (Adjusted) Generalized Estimating Equations for Binary Responses

Description

Fits a marginal model for repeated or clustered binary responses using Generalized Estimating Equations (GEE). Supported estimation methods include the traditional GEE, bias-reducing GEE, bias-correcting GEE, and Jeffreys-prior penalized GEE.

Usage

geewa_binary(
  formula,
  link = "logit",
  data = parent.frame(),
  id,
  repeated,
  control = geer_control(...),
  orstr = "independence",
  method = "gee",
  weights,
  beta_start = NULL,
  offset,
  control_glm = list(...),
  alpha_vector = NULL,
  ...
)

Arguments

Details

method specifies the estimation approach. If method = "gee", the standard GEE are solved with no adjustment. If method is one of "brgee-naive", "brgee-robust" or "brgee-empirical", an adjustment vector is added to produce naive, robust or empirical bias-reducing estimators, respectively. If method is one of "bcgee-naive", "bcgee-robust" or "bcgee-empirical", the corresponding bias-corrected estimators are produced via a one-step correction applied to the converged GEE solution. If method = "pgee-jeffreys", the GEE are penalized using a Jeffreys-prior penalty run to full convergence. If method = "opgee-jeffreys", a single penalized scoring step is performed from the converged independence penalized solution (one-step approximation). If method = "hpgee-jeffreys", a single standard GEE scoring step is performed from the converged independence penalized solution (hybrid one-step approximation).

The marginal mean model always uses a binomial family with the specified link. Within-cluster association is modeled through marginalized pairwise odds ratios rather than a correlation structure. The scale parameter is fixed at phi = 1.

For the construction of the formula argument, see the documentation of glm and formula.

The data must be in long format (one row per observation). See reshape for details on reshaping between long and wide formats.

The default set for the id labels is \{1,\ldots,N\}, where N is the number of clusters. Otherwise, the function recodes the given labels of id onto this set.

The argument repeated can be safely omitted only if observations are already ordered within each cluster as intended. If repeated is not provided, it is created as 1, 2, ..., n_i within each cluster i, using the current row order (before internal sorting). If repeated is provided, its labels are recoded to 1, ..., T and must be unique within each cluster.

The variables id and repeated do not need to be pre-sorted. Instead the function sorts observations in ascending order of id and repeated.

A term of the form offset(expression) is allowed in the right-hand side of formula.

The length of id must equal the number of observations. When provided, repeated and weights must also have the same length.

Value

An object of class "geer", a list with components:

Note on returned components

For geewa_binary, the phi component is always 1 and alpha contains the estimated (or fixed) working odds-ratio parameters, not correlation parameters. The association_structure component stores the value of orstr. Under orstr = "independence", alpha is set to 1.

For method in "bcgee-naive", "bcgee-robust", "bcgee-empirical", "opgee-jeffreys", and "hpgee-jeffreys", converged is always TRUE in the returned object, because these methods produce their estimate via a single correction step applied to an already-converged fit.

References

Touloumis, A. (2026) Jeffreys-Type Penalized GEE for Correlated Binary Data with an Odds-Ratio Parameterization. Preprint. https://arxiv.org/abs/2606.16058

Touloumis, A. (2026) Bias-Reduced GEE via Adjusted Estimating Equations, with Odds-Ratio Extensions. Preprint. https://arxiv.org/abs/2606.16043

See Also

geewa, geer_control, summary.geer, geecriteria.

Examples

data("respiratory", package = "geer")
respiratory2 <- respiratory[respiratory$center == "C2", , drop = FALSE]
fitted_model <- geewa_binary(
  formula = status ~ baseline + treatment * gender + visit * age,
  link = "probit",
  data = respiratory2,
  id = id,
  repeated = visit,
  orstr = "independence",
  method = "pgee-jeffreys"
)
summary(fitted_model, cov_type = "bias-corrected")


data("cholecystectomy", package = "geer")
fitted_model_gee <- geewa_binary(
  formula = pain ~ treatment + gender + age,
  link = "logit",
  data = cholecystectomy,
  id = id,
  orstr = "independence",
  method = "gee"
)
summary(fitted_model_gee, cov_type = "bias-corrected")

fitted_model_brgee_robust <- update(fitted_model_gee, method = "brgee-robust")
summary(fitted_model_brgee_robust, cov_type = "bias-corrected")

fitted_model_brgee_naive <- update(fitted_model_gee, method = "brgee-naive")
summary(fitted_model_brgee_naive, cov_type = "bias-corrected")

fitted_model_brgee_empirical <- update(fitted_model_gee, method = "brgee-empirical")
summary(fitted_model_brgee_empirical, cov_type = "bias-corrected")

fitted_model_bcgee_robust <- update(fitted_model_gee, method = "bcgee-robust")
summary(fitted_model_bcgee_robust, cov_type = "robust")


## Penalized GEE with exchangeable odds-ratio structure
fitted_model_pgee <- geewa_binary(
  formula = pain ~ treatment + gender + age,
  link = "logit",
  data = cholecystectomy,
  id = id,
  orstr = "exchangeable",
  method = "pgee-jeffreys",
  control = geer_control(jeffreys_power = 1)
)
summary(fitted_model_pgee, cov_type = "robust")

Glance at a geer Object

Description

Produces a one-row summary for a fitted geer object, following broom conventions.

Usage

## S3 method for class 'geer'
glance(x, ...)

Arguments

Details

The returned one-row data frame contains the following columns:

family

name of the marginal response family.

link

name of the link function.

method

estimation method, for example "gee", "brgee-robust", "pgee-jeffreys", "opgee-jeffreys", or "hpgee-jeffreys".

wastr

stored working association structure. For geewa() fits this corresponds to corstr; for geewa_binary() fits it corresponds to orstr.

nobs

total number of observations n^{\star}.

nclusters

number of independent clusters N.

min.cluster.size

minimum cluster size.

max.cluster.size

maximum cluster size.

npar

number of marginal mean-model parameters p.

df.residual

residual degrees of freedom n^{\star} - p.

phi

estimated or fixed dispersion parameter. This equals 1 for the odds-ratio parameterization used by geewa_binary().

QIC

Quasi Information Criterion (Pan, 2001), computed from the robust sandwich covariance estimator. Smaller values are preferred.

QICu

Covariate-selection variant of QIC (Pan, 2001). Smaller values are preferred.

CIC

Correlation Information Criterion (Hin and Wang, 2009), used here to compare working association structures. Smaller values are preferred.

converged

logical; TRUE if the fitting algorithm converged.

niter

number of iterations used.

QIC and CIC are computed using the same formulas as geecriteria(object, cov_type = "robust"). QICu does not depend on the covariance estimator. If computation fails, the corresponding values are returned as NA_real_. For the full set of model selection criteria, including RJC, GESSC, and GPC, see geecriteria.

Value

A one-row data frame with columns as described in the Details section. When tibble is available, the result has class c("tbl_df", "tbl", "data.frame"). Otherwise, a plain data.frame is returned.

References

Pan W. (2001) Akaike's information criterion in generalized estimating equations. Biometrics, 57, 120–125.

Hin L.Y. and Wang Y.G. (2009) Working-correlation-structure identification in generalized estimating equations. Statistics in Medicine, 28, 642–658.

Robinson D., Hayes A. and Couch S. (2024) broom: Convert Statistical Objects into Tidy Tibbles. https://broom.tidymodels.org/.

See Also

tidy.geer, geecriteria, geewa, geewa_binary.

Examples

data("epilepsy", package = "geer")
fitmodel <- geewa(
  formula = seizures ~ treatment + lnbaseline + lnage,
  family = poisson(link = "log"),
  data = epilepsy,
  id = id,
  corstr = "exchangeable",
  method = "gee"
)
glance(fitmodel)

data("cerebrovascular", package = "geer")
fitbin <- geewa_binary(
  formula = ecg ~ treatment + factor(period),
  link = "logit",
  data = cerebrovascular,
  id = id,
  orstr = "exchangeable"
)
glance(fitbin)


fitind  <- update(fitmodel, corstr = "independence")
fitar1  <- update(fitmodel, corstr = "ar1")
fitunst <- update(fitmodel, corstr = "unstructured")
do.call(rbind, lapply(
  list(independence = fitind, exchangeable = fitmodel, ar1 = fitar1,
       unstructured = fitunst),
  glance
))[, c("wastr", "QIC", "CIC", "niter")]

Antibiotic Treatment for Leprosy Trial

Description

Data from a randomized clinical trial on the efficacy of antibiotic treatments for leprosy in the Philippines.

Usage

leprosy

Format

A data frame with 60 rows and 4 columns:

id

integer subject identifier.

period

factor period identifier with levels pre and post.

bacilli

integer count of leprosy bacilli at six body sites.

treatment

factor with levels A, B, and C, where C denotes placebo.

Details

Thirty subjects in the Philippines were enrolled in a randomized clinical trial, yielding 60 observations (two periods per subject). Subjects were assigned to receive treatment A, B, or C (placebo). Before treatment, the number of leprosy bacilli at six body sites was recorded. After treatment, bacilli counts were recorded again. The trial aimed to assess whether treatments A and B reduced bacilli abundance compared with placebo.

References

Snedecor, G.W. and Cochran, W.G. (1967) Statistical Methods. Ames, Iowa: Iowa State University Press.

Examples

data("leprosy", package = "geer")
str(leprosy)

Construct Design Matrices from a geer Object

Description

Constructs the design matrix for the marginal mean model underlying a fitted geer object.

Usage

## S3 method for class 'geer'
model.matrix(object, ...)

Arguments

Details

The design matrix is reconstructed from object$terms and object$data, with factor variables and interactions expanded using the contrasts recorded at fitting time.

By convention, if the response variable also appears on the right-hand side of the formula, it is dropped, although interactions involving that term are retained.

Value

A numeric design matrix for the marginal mean model represented by object.

The returned matrix has an "assign" attribute: an integer vector with one entry per column giving the index of the formula term that produced that column. A value of 0 corresponds to the intercept, if present. Positive values index terms in the order given by attr(terms(object), "term.labels").

If the model contains factor terms, the returned matrix may also have a "contrasts" attribute giving the contrasts used for those factors.

See Also

geewa, geewa_binary, model.matrix.

Examples

data("leprosy", package = "geer")
fit <- geewa(
  formula = bacilli ~ factor(period) + factor(period):treatment,
  family = poisson(link = "log"),
  data = leprosy,
  id = id
)
model.matrix(fit)

data("cerebrovascular", package = "geer")
fit <- geewa_binary(
  formula = ecg ~ treatment + factor(period),
  link = "logit",
  data = cerebrovascular,
  id = id
)
model.matrix(fit)

Predictions from a geer Object

Description

Generates predictions from a fitted geer object, optionally with approximate standard errors.

Usage

## S3 method for class 'geer'
predict(
  object,
  newdata = NULL,
  type = c("link", "response"),
  cov_type = c("bias-corrected", "robust", "df-adjusted", "naive"),
  se.fit = FALSE,
  ...
)

Arguments

Details

Predictions are obtained by computing the model matrix for newdata (or using the original fit when newdata is omitted) and multiplying by the estimated coefficients. If type = "response", the linear predictor is transformed via the inverse link function.

When se.fit = TRUE, approximate standard errors are computed by the delta method using the covariance matrix specified by cov_type. On the response scale, standard errors are additionally scaled by the absolute derivative of the inverse link function.

Value

If se.fit = FALSE, a numeric vector of predictions on the scale specified by type. If se.fit = TRUE, a list with components:

See Also

fitted.geer, residuals.geer, geewa, geewa_binary, predict.

Examples

data("cerebrovascular", package = "geer")
fit <- geewa_binary(
  formula = ecg ~ treatment + factor(period),
  link = "logit",
  data = cerebrovascular,
  id = id,
  orstr = "exchangeable"
)
head(predict(fit, type = "link"))
head(predict(fit, type = "response"))

nd <- cerebrovascular[1:5, , drop = FALSE]
predict(fit, newdata = nd, type = "response")

pred <- predict(fit, type = "response", se.fit = TRUE, cov_type = "robust")
head(pred$fit)
head(pred$se.fit)

Print a geer Object

Description

Prints the call, estimated regression coefficients, and basic fitting information for a fitted geer object.

Usage

## S3 method for class 'geer'
print(x, ...)

Arguments

Value

The input object x is returned invisibly.

See Also

summary.geer, coef.geer.

Examples

data("epilepsy", package = "geer")
fit <- geewa(
  formula = seizures ~ treatment + lnbaseline + lnage,
  family = poisson(link = "log"),
  data = epilepsy,
  id = id,
  corstr = "exchangeable"
)
print(fit)

Print a summary.geer Object

Description

Prints the contents of a summary.geer object, including the call, estimation method, coefficient table, dispersion parameter, and working association structure.

Usage

## S3 method for class 'summary.geer'
print(x, ...)

Arguments

Value

The input object x is returned invisibly.

See Also

summary.geer, print.geer.

Examples

data("epilepsy", package = "geer")
fit <- geewa(
  formula = seizures ~ treatment + lnbaseline + lnage,
  family = poisson(link = "log"),
  data = epilepsy,
  id = id,
  corstr = "exchangeable"
)
print(summary(fit))

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics

glance(), tidy()

Residuals from a geer Object

Description

Extracts residuals of different types from a fitted geer object.

Usage

## S3 method for class 'geer'
residuals(object, type = c("working", "pearson", "deviance"), ...)

Arguments

Details

Residuals are computed using the marginal variance and deviance functions of the family specified in the fitted model.

Value

A numeric vector of residuals of the requested type, of the same length as the number of observations used in fitting.

See Also

fitted.geer, predict.geer, geewa, geewa_binary, residuals.

Examples

data("cerebrovascular", package = "geer")
fit <- geewa_binary(
  formula = ecg ~ treatment + factor(period),
  link = "logit",
  data = cerebrovascular,
  id = id,
  orstr = "exchangeable"
)
head(residuals(fit, type = "working"))
head(residuals(fit, type = "pearson"))
head(residuals(fit, type = "deviance"))

Respiratory Illness Clinical Trial

Description

Data from a randomized clinical trial on respiratory illness.

Usage

respiratory

Format

A data frame with 444 rows and 8 columns:

id

integer subject identifier.

visit

integer follow-up visit identifier.

status

binary indicator of respiratory status, 0 = poor and 1 = good.

treatment

factor with levels active and placebo.

baseline

binary indicator of baseline respiratory status, 0 = poor and 1 = good.

age

numeric age in years recorded at baseline.

gender

factor with levels female and male.

center

factor with levels C1 and C2.

Details

One hundred eleven subjects from two clinical centers were enrolled in a randomized clinical trial, yielding 444 observations (four follow-up visits per subject). Subjects were randomly assigned to treatment groups. Baseline examinations were conducted before treatment began. After treatment, subjects were examined at four scheduled follow-up visits.

References

Stokes, M.E., Davis, C.S. and Koch, G.G. (1995) Categorical Data Analysis using the SAS System. Cary, NC: SAS Institute, Inc.

Examples

data("respiratory", package = "geer")
str(respiratory)

Dental Plaque Mouth Rinse Trial

Description

Data from a randomized clinical trial on the efficacy of mouth rinses in reducing dental plaque.

Usage

rinse

Format

A data frame with 218 rows and 8 columns:

id

integer subject identifier.

time

integer follow-up month identifier.

score

numeric plaque score.

treatment

factor indicating rinse group with levels A, B, and placebo.

baseline

numeric plaque score at baseline.

gender

factor with levels female and male.

age

numeric age in years recorded at baseline.

smoke

factor with levels yes and no.

Details

One hundred nine adults aged 18–55 with pre-existing dental plaque but without advanced periodontal disease were enrolled in a randomized, double-blind clinical trial, yielding 218 observations (baseline plus up to two follow-up assessments per subject). Subjects were assigned to one of two novel mouth rinses (A or B) or to a control mouth rinse. Eligibility required at least 20 sound natural teeth and a mean plaque index of 2.0 or greater. Plaque was assessed at baseline, 3 months, and 6 months using the Turesky modification of the Quigley-Hein index. Four subjects had missing plaque scores. The trial aimed to evaluate the effectiveness of the three rinses in inhibiting dental plaque.

References

Hadgu, A. and Koch, G. (1999) Application of generalized estimating equations to a dental randomized clinical trial. Journal of Biopharmaceutical Statistics, 9, 161–178.

Examples

data("rinse", package = "geer")
str(rinse)

Stepwise Model Selection by Hypothesis Testing

Description

Performs stepwise model selection for a fitted geer object using repeated single-term additions (add1.geer) and deletions (drop1.geer), with candidate moves evaluated by hypothesis tests.

Usage

step_p(
  object,
  scope,
  direction = c("backward", "forward", "both"),
  p_enter = 0.15,
  p_remove = 0.15,
  test = c("wald", "score", "working-wald", "working-score", "working-lrt"),
  cov_type = c("bias-corrected", "robust", "df-adjusted", "naive"),
  pmethod = c("rao-scott", "satterthwaite"),
  steps = 1000
)

Arguments

Details

step_p() repeatedly calls add1.geer and drop1.geer.

The set of models searched is determined by the scope argument. The right-hand side of its lower component is always retained in the model, and the right-hand side of its upper component defines the largest model that can be considered. If scope is a single formula, it specifies the upper component and the lower model is empty.

Candidate additions and deletions respect model hierarchy: a move is allowed only if it preserves all lower-order component terms implied by any higher-order interactions in the model.

If scope is omitted, the search is restricted to the terms in the initial model.

With direction = "both", backward elimination is attempted first at each step. A forward addition is considered only if no eligible backward deletion is found. The algorithm stops when no candidate move satisfies the relevant p-value threshold, when the maximum number of steps is reached, or when an immediate add-remove cycle involving the same term would occur.

Details of the hypothesis tests controlled by test are given in Rotnitzky and Jewell (1990). The option test = "working-lrt" is valid only when the model is fitted with an independence working association structure. Otherwise, an error is returned.

When test %in% c("wald", "score"), the pmethod argument is ignored and cov_type specifies the covariance estimator used to compute the test statistic. For modified working tests, cov_type determines the covariance matrix used to form the coefficients of the sum of independent chi-squared random variables, and pmethod specifies the approximation used to compute the p-value.

Value

A fitted model object of class "geer" corresponding to the final selected model. The returned object also includes an anova component of class c("anova", "data.frame") summarizing the stepwise sequence. This table has one row for the initial model and one row for each accepted step, with columns Step (the term added or removed), Df (degrees of freedom of the test), Chi (test statistic), Pr(>Chi) (p-value), and CIC (Correlation Information Criterion).

References

Rotnitzky, A. and Jewell, N.P. (1990) Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data. Biometrika, 77, 485–497.

See Also

add1.geer, drop1.geer, anova.geer, geecriteria, geewa, geewa_binary.

Examples

data("respiratory", package = "geer")
respiratory2 <- respiratory[respiratory$center == "C2", , drop = FALSE]

full_fit <- geewa_binary(
  formula = status ~ (baseline + treatment + gender + visit + age)^2,
  link = "probit",
  data = respiratory2,
  id = id,
  repeated = visit,
  orstr = "independence",
  method = "pgee-jeffreys"
)

## Backward elimination within the initial model terms
step_p(
  full_fit,
  direction = "backward",
  test = "wald",
  cov_type = "bias-corrected",
  p_remove = 0.10
)

## Bidirectional selection with an explicit scope
step_p(
  full_fit,
  scope = list(
    lower = ~ baseline + treatment,
    upper = ~ (baseline + treatment + gender + visit + age)^2
  ),
  direction = "both",
  test = "score",
  cov_type = "robust",
  p_enter = 0.10,
  p_remove = 0.15,
  steps = 50
)

Summarize a geer Object

Description

Produces a coefficient table and basic model summary information for a fitted geer object.

Usage

## S3 method for class 'geer'
summary(
  object,
  cov_type = c("bias-corrected", "robust", "df-adjusted", "naive"),
  ...
)

Arguments

Value

An object of class "summary.geer", a list with components:

See Also

print.summary.geer, tidy.geer, glance.geer, vcov.geer.

Examples

data("epilepsy", package = "geer")
fit <- geewa(
  formula = seizures ~ treatment + lnbaseline + lnage,
  family = poisson(link = "log"),
  data = epilepsy,
  id = id,
  corstr = "exchangeable"
)
summary(fit)
summary(fit, cov_type = "bias-corrected")

data("cerebrovascular", package = "geer")
fit2 <- geewa_binary(
  formula = ecg ~ treatment + factor(period),
  link = "logit",
  data = cerebrovascular,
  id = id,
  orstr = "exchangeable"
)
summary(fit2)

Tidy a geer Object

Description

Summarizes a fitted geer object at the coefficient level, returning one row per regression term in a tidy data frame.

Usage

## S3 method for class 'geer'
tidy(
  x,
  conf.int = FALSE,
  conf.level = 0.95,
  exponentiate = FALSE,
  cov_type = c("bias-corrected", "robust", "df-adjusted", "naive"),
  ...
)

Arguments

Details

The returned data frame contains the following columns:

term

name of the regression coefficient.

estimate

point estimate, or the exponentiated point estimate when exponentiate = TRUE.

std.error

standard error derived from vcov(x, cov_type = cov_type).

statistic

Wald z-statistic, computed as the coefficient estimate divided by its standard error. NA is reported when the standard error is not positive.

p.value

two-sided p-value from the standard normal distribution.

conf.low, conf.high

lower and upper Wald confidence limits. These columns are included only when conf.int = TRUE.

When exponentiate = TRUE, only estimate, conf.low, and conf.high are exponentiated. The columns std.error and statistic remain on the original scale.

Value

A data frame with one row per regression coefficient and columns as described in the Details section. When tibble is available, the result has class c("tbl_df", "tbl", "data.frame"). Otherwise, a plain data.frame is returned.

References

Robinson D., Hayes A. and Couch S. (2024) broom: Convert Statistical Objects into Tidy Tibbles. https://broom.tidymodels.org/.

See Also

glance.geer, vcov.geer, confint.geer, geewa, geewa_binary.

Examples

data("epilepsy", package = "geer")
fitmodel <- geewa(
  formula = seizures ~ treatment + lnbaseline + lnage,
  family = poisson(link = "log"),
  data = epilepsy,
  id = id,
  corstr = "exchangeable",
  method = "gee"
)
tidy(fitmodel)
tidy(fitmodel, conf.int = TRUE)
tidy(fitmodel, conf.int = TRUE, exponentiate = TRUE)
tidy(fitmodel, cov_type = "robust")

data("cerebrovascular", package = "geer")
fitbin <- geewa_binary(
  formula = ecg ~ treatment + factor(period),
  link = "logit",
  data = cerebrovascular,
  id = id,
  orstr = "exchangeable"
)
tidy(fitbin, conf.int = TRUE, conf.level = 0.90, exponentiate = TRUE)

Extract Variance-Covariance Matrix from a geer Object

Description

Extracts the variance-covariance matrix of the estimated regression parameters from a fitted geer object.

Usage

## S3 method for class 'geer'
vcov(
  object,
  cov_type = c("bias-corrected", "robust", "df-adjusted", "naive"),
  ...
)

Arguments

Details

The form of the covariance estimator is controlled by cov_type:

"bias-corrected"

the bias-corrected covariance estimator (Morel et al., 2003).

"robust"

the sandwich (robust) covariance estimator (Liang and Zeger, 1986).

"df-adjusted"

the degrees-of-freedom adjusted covariance estimator (MacKinnon, 1985).

"naive"

the model-based covariance estimator (Liang and Zeger, 1986).

Value

A square numeric matrix of estimated covariances between regression coefficients. Rows and columns are named according to the coefficient names returned by coef.geer.

References

Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika, 73, 13–22.

MacKinnon, J.G. (1985) Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics, 29, 305–325.

Morel, J.G., Bokossa, M.C. and Neerchal, N.K. (2003) Small sample correction for the variance of GEE estimators. Biometrical Journal, 45, 395–409.

See Also

coef.geer, confint.geer, summary.geer, tidy.geer.

Examples

data("cerebrovascular", package = "geer")
fit <- geewa_binary(
  formula = ecg ~ treatment + factor(period),
  link = "logit",
  data = cerebrovascular,
  id = id,
  orstr = "exchangeable"
)
vcov(fit)
vcov(fit, cov_type = "robust")