Package 'bclogit' reference manual

Title:	Conditional Logistic Regression
Description:	Performs inference for Bayesian conditional logistic regression with informative priors built from the concordant pair data. We include many options to build the priors. And we include many options during the inference step for estimation, testing and confidence set creation. For details, see Kapelner and Tennenbaum (2026) "Improved Conditional Logistic Regression using Information in Concordant Pairs with Software" <doi:10.48550/arXiv.2602.08212>.
Authors:	Adam Kapelner [aut, cre] , Jacob Tennenbaum [aut]
Maintainer:	Adam Kapelner <[email protected]>
License:	GPL-3
Version:	1.1.1
Built:	2026-05-24 05:59:30 UTC
Source:	https://github.com/tennenbaumj/bclogit_package_and_paper_repo

Bayesian Conditional Logistic Regression with Concordant Pairs

Description

bclogit

Details

Fits a conditional logistic regression model to incidence data. Allows for use of the concordant pairs in the fitting.

Author(s)

	Jacob Tennenbaum \email{[email protected]}

	Adam Kapelner \email{[email protected]}

References

Jacob Tennenbaum and Adam Kapelner (2026). "Improved Conditional Logistic Regression using Information in Concordant Pairs with Software." arXiv preprint arXiv:2602.08212.

Initialize a new bclogit model

Description

This function fits a Bayesian conditional logistic regression model, incorporating information from concordant pairs to improve estimation.

Usage

## S3 method for class 'formula'
bclogit(
  formula,
  data,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  concordant_method = "GLM",
  prior_type = "Naive",
  chains = 4,
  return_raw_stan_output = FALSE,
  prior_variance_treatment = 100,
  stan_refresh = 0,
  ...
)

bclogit(
  formula,
  data,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  concordant_method = "GLM",
  prior_type = "Naive",
  chains = 4,
  return_raw_stan_output = FALSE,
  prior_variance_treatment = 100,
  stan_refresh = 0,
  ...
)

## Default S3 method:
bclogit(
  formula = NULL,
  data = NULL,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  concordant_method = "GLM",
  prior_type = "Naive",
  chains = 4,
  return_raw_stan_output = FALSE,
  prior_variance_treatment = 100,
  stan_refresh = 0,
  ...,
  y = NULL,
  X = NULL,
  treatment_name = NULL,
  call = NULL
)
## S3 method for class 'formula'
bclogit(
  formula,
  data,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  concordant_method = "GLM",
  prior_type = "Naive",
  chains = 4,
  return_raw_stan_output = FALSE,
  prior_variance_treatment = 100,
  stan_refresh = 0,
  ...
)

bclogit(
  formula,
  data,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  concordant_method = "GLM",
  prior_type = "Naive",
  chains = 4,
  return_raw_stan_output = FALSE,
  prior_variance_treatment = 100,
  stan_refresh = 0,
  ...
)

## Default S3 method:
bclogit(
  formula = NULL,
  data = NULL,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  concordant_method = "GLM",
  prior_type = "Naive",
  chains = 4,
  return_raw_stan_output = FALSE,
  prior_variance_treatment = 100,
  stan_refresh = 0,
  ...,
  y = NULL,
  X = NULL,
  treatment_name = NULL,
  call = NULL
)

Arguments

formula

For the formula method, a symbolic description of the model to be fitted.

data

A data.frame, data.table, or model.matrix containing the variables (optional for formula method).

treatment

Optional vector specifying the treatment variable (required for default method, or can be specified in formula method).

strata

Vector specifying the strata (matched pairs).

subset

An optional vector specifying a subset of observations.

na.action

A function which indicates what should happen when the data contain NAs.

concordant_method

The method to use for fitting the concordant pairs and reservoir. Options are "GLM", "GEE", and "GLMM".

prior_type

The type of prior to use for the discordant pairs. Options are "Naive", "G prior", "PMP", and "Hybrid".

chains

Number of chains for Stan sampling. Default is 4.

return_raw_stan_output

Logical; if TRUE, the raw Stan posterior samples (iterations x chains x parameters) are stored in the returned object. Default FALSE.

prior_variance_treatment

Prior variance for the treatment coefficient in the covariance matrix Sigma_con. Default is 100.

stan_refresh

How often Stan reports sampling progress (in iterations). Default is 0 (silent). Set to a positive integer (e.g., 1 or 100) to see progress.

...

Additional arguments passed to rstan::sampling (e.g., iter, warmup, thin, seed, control).

y

For the default method, a binary (0,1) vector containing the response of each subject.

X

A data.frame, data.table, or model.matrix containing the variables.

treatment_name

Optional string name for the treatment variable.

call

Optional call object to store in the result.

Value

An object of class "bclogit".

A list of class bclogit containing:

coefficients

Estimated coefficients (posterior means).

var

Variance-covariance matrix of coefficients.

model

The fitted Stan model object.

posterior_samples

Raw posterior samples as a 3D array (iterations x chains x parameters) from rstan::extract(model, permuted = FALSE). Only populated when return_raw_stan_output = TRUE; NULL otherwise.

concordant_model

The fitted model object for the concordant pairs/reservoir (GLM/GEE/GLMM).

matched_data

The processed matched pairs data from the premodeling step.

prior_info

A list with elements mu (prior mean vector), Sigma (prior covariance matrix), and fallbacks (a character vector naming every robustness fallback that was triggered during prior construction, or character(0) if none were needed). Possible fallback names are: "insufficient_concordant_pairs" (too few concordant pairs; diffuse prior used), "na_in_prior_mean" (NAs in prior mean replaced with 0), "na_in_prior_covariance" (NAs in prior covariance replaced with diag(100)), "glm_fallback" (non-positive-definite GEE/GLMM covariance; re-fitted via GLM), "diffuse_prior" (non-positive-definite covariance replaced with diag(100)).

call

The function call.

terms

The model terms.

num_discordant

Number of discordant pairs used.

num_concordant

Number of concordant pairs/reservoir entries used.

A list of class "bclogit" containing:

coefficients

Estimated coefficients (posterior means).

var

Variance-covariance matrix of the coefficients (posterior covariance).

model

The fitted Stan model object for the discordant pairs.

posterior_samples

Raw posterior samples as a 3D array (iterations x chains x parameters) from rstan::extract(model, permuted = FALSE). Only populated when return_raw_stan_output = TRUE; NULL otherwise.

concordant_model

The fitted model object for the concordant pairs (GLM, GEE, or GLMM).

matched_data

The processed matched pairs data from the C++ pre-modeling step.

prior_info

A list with elements mu (prior mean vector), Sigma (prior covariance matrix), and fallbacks (character vector of robustness fallbacks triggered; character(0) if none).

call

The function call.

terms

The model terms.

xlevels

Factor level information (always NULL for this method).

n

Total number of observations.

num_discordant

Number of discordant pairs used for fitting.

num_concordant

Number of concordant pairs used for the prior.

X_model_matrix_col_names

Column names of the covariate model matrix.

treatment_name

Name of the treatment variable.

Methods (by class)

bclogit(formula): Formula method
bclogit(default): Default method for matrix/data input.

Numerical Stability in GEE Pre-modeling

When concordant_method = "GEE", the function applies a layered set of safeguards to ensure the prior covariance Sigma_con passed to Stan is valid (positive definite, symmetric, and free of NA values).

Naive (model-based) covariance: GEE's default covariance is the sandwich (robust) estimator, which can produce degenerate (e.g. negative) variances when the number of independent clusters is small. The code instead uses geese$vbeta.naiv, the model-based covariance that assumes the working correlation structure is exactly correct. It is less efficient asymptotically but always well-structured for small samples.
Cholesky positive-definiteness check: After extracting the covariance, a Cholesky decomposition is attempted via tryCatch(chol(Sigma_con), ...). This is the canonical test for positive definiteness: it succeeds if and only if the matrix is PD. A failed decomposition sets an is_pd = FALSE flag without crashing the call.
GLM fallback for non-PD GEE or GLMM covariance: If the GEE naive covariance or the GLMM fixed-effect vcov is still not positive definite (GLMM failures are common with boundary variance estimates or convergence issues), a standard glm(..., family = binomial) is re-fitted on the same concordant data and its Fisher-information-based vcov is used instead. That matrix is always PD for a non-degenerate design matrix, providing a graceful degradation from GEE/GLMM to GLM.
Symmetrization: Sigma_con <- (Sigma_con + t(Sigma_con)) / 2 is applied after the GLM fallback re-extraction and again unconditionally before the matrix is passed to Stan. Name-indexed covariance matching can introduce tiny floating-point asymmetries; averaging with the transpose enforces exact symmetry as required by Stan's Cholesky-based samplers.
NA sanitization: Aliased model terms (collinear columns in the concordant data) cause coef() and vcov() to return NA. These are caught before Stan sees them: NA entries in the prior mean vector default to 0 (a neutral prior), and any NA in the covariance matrix triggers replacement of the entire matrix with a wide diagonal (diag(100, p)), an uninformative but valid prior.
Diffuse prior fallback: Multiple failure paths (too few concordant pairs, persistent non-PD matrix, NA-contaminated covariance) fall back to diag(100, p), which is positive definite by construction and encodes a broad, independent prior that lets the discordant-pair likelihood dominate.
Treatment prior reset: After extracting the concordant-model covariance, the entire first row and column (corresponding to the treatment coefficient) are zeroed and the diagonal entry is set to prior_variance_treatment (default 100). This decouples the treatment prior from the nuisance-covariate prior: concordant pairs inform covariate shrinkage but are not allowed to shrink the treatment coefficient, which remains independently diffuse. Zeroing the off-diagonals also eliminates any spurious prior correlation between treatment and covariates induced by the concordant-model fit.

Examples


# Example usage
data("fhs")
fit <- bclogit(PREVHYP ~ TOTCHOL + CIGPDAY + BMI + HEARTRTE, 
  data = fhs, treatment = PERIOD, strata = RANDID)
summary(fit)

# Example usage
data("fhs")
fit <- bclogit(PREVHYP ~ TOTCHOL + CIGPDAY + BMI + HEARTRTE, 
  data = fhs, treatment = PERIOD, strata = RANDID)
summary(fit)

Frequentist Conditional Logistic Regression

Description

Fits a conditional logistic regression model for matched pairs using the discordant-pair GLM trick. This is a fast frequentist alternative to bclogit.

Usage

## Default S3 method:
clogit(
  formula = NULL,
  data = NULL,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  do_inference_on_var = "all",
  ...,
  y = NULL,
  X = NULL,
  treatment_name = NULL,
  call = NULL
)

## S3 method for class 'formula'
clogit(
  formula,
  data,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  do_inference_on_var = "all",
  ...
)

clogit(
  formula,
  data,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  do_inference_on_var = "all",
  ...
)
## Default S3 method:
clogit(
  formula = NULL,
  data = NULL,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  do_inference_on_var = "all",
  ...,
  y = NULL,
  X = NULL,
  treatment_name = NULL,
  call = NULL
)

## S3 method for class 'formula'
clogit(
  formula,
  data,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  do_inference_on_var = "all",
  ...
)

clogit(
  formula,
  data,
  treatment = NULL,
  strata = NULL,
  subset = NULL,
  na.action = NULL,
  do_inference_on_var = "all",
  ...
)

Arguments

formula

For the formula method, a symbolic description of the model.

data

A data frame containing the variables (for formula method).

treatment

Vector specifying the treatment variable.

strata

Vector specifying the strata (matched pairs).

subset

An optional vector specifying a subset of observations to be used in the fitting process.

na.action

A function which indicates what should happen when the data contain NAs.

do_inference_on_var

Which variable(s) to compute standard errors for. "all" (default) computes SEs for all coefficients. An integer j computes the SE only for the jth coefficient (1 = treatment, then covariates in order). "none" skips inference entirely.

...

Additional arguments passed to fastLogisticRegressionWrap::fast_logistic_regression (e.g., convergence tolerances).

y

For the default method, a binary (0,1) response vector.

X

A data.frame, data.table, or model.matrix containing the variables.

treatment_name

Optional string name for the treatment variable.

call

Optional call object to store in the result.

Value

A list of class "clogit_bclogit" containing:

coefficients

Estimated coefficients (posterior means).

var

Variance-covariance matrix of the coefficients (diagonal, built from standard errors). NULL when do_inference_on_var is not "all".

flr_model

The fitted fast logistic regression model object returned by fastLogisticRegressionWrap::fast_logistic_regression.

call

The function call.

terms

The model terms.

n

Total number of observations.

num_discordant

Number of discordant pairs used for fitting.

num_concordant

Number of concordant pairs.

X_model_matrix_col_names

Column names of the covariate model matrix.

treatment_name

Name of the treatment variable.

se

Standard errors of the coefficients.

z

Z-statistics for each coefficient.

pval

Approximate p-values for each coefficient.

do_inference_on_var

The value of the do_inference_on_var argument.

An object of class "clogit_bclogit".

Methods (by class)

clogit(default): Default method for matrix/data input.
clogit(formula): Formula method

Examples

data("fhs")
fit <- clogit(PREVHYP ~ TOTCHOL + CIGPDAY + BMI + HEARTRTE, 
  data = fhs, treatment = PERIOD, strata = RANDID)
summary(fit)
n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5),
  x1 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(1:(n / 2), each = 2)
)
fit <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata)
# Inference on treatment only (faster):
fit2 <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata,
               do_inference_on_var = 1)
n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5),
  x1 = rnorm(n),
  x2 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(1:(n / 2), each = 2)
)
fit <- clogit(y ~ x1 + x2, data = dat, treatment = treatment, strata = strata)
summary(fit)
coef(fit)
vcov(fit)
data("fhs")
fit <- clogit(PREVHYP ~ TOTCHOL + CIGPDAY + BMI + HEARTRTE, 
  data = fhs, treatment = PERIOD, strata = RANDID)
summary(fit)
n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5),
  x1 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(1:(n / 2), each = 2)
)
fit <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata)
# Inference on treatment only (faster):
fit2 <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata,
               do_inference_on_var = 1)
n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5),
  x1 = rnorm(n),
  x2 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(1:(n / 2), each = 2)
)
fit <- clogit(y ~ x1 + x2, data = dat, treatment = treatment, strata = strata)
summary(fit)
coef(fit)
vcov(fit)

Extract coefficients from a bclogit model

Description

Extract coefficients from a bclogit model

Usage

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

Arguments

object

A bclogit object.

...

Additional arguments.

Value

Named numeric vector of posterior mean coefficients.

Examples


data("fhs")
fit <- bclogit(PREVHYP ~ TOTCHOL + BMI, data = fhs,
               treatment = PERIOD, strata = RANDID)
coef(fit)

data("fhs")
fit <- bclogit(PREVHYP ~ TOTCHOL + BMI, data = fhs,
               treatment = PERIOD, strata = RANDID)
coef(fit)

Extract coefficients from a clogit_bclogit model

Description

Extract coefficients from a clogit_bclogit model

Usage

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

Arguments

object

A clogit_bclogit object.

...

Additional arguments.

Value

Numeric vector of coefficients.

Examples

n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5), x1 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(1:(n / 2), each = 2)
)
fit <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata)
coef(fit)
n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5), x1 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(1:(n / 2), each = 2)
)
fit <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata)
coef(fit)

Credible Intervals for bclogit Parameters

Description

Computes Bayesian credible intervals for the model parameters.

Usage

## S3 method for class 'bclogit'
confint(object, parm, level = 0.95, type = c("HPD_one", "CR", "HPD_many"), ...)
## S3 method for class 'bclogit'
confint(object, parm, level = 0.95, type = c("HPD_one", "CR", "HPD_many"), ...)

Arguments

object

A bclogit object.

parm

A specification of which parameters to be given credible intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.

level

The confidence level required (default 0.95).

type

Type of interval to compute: "HPD_one" (default unimodal HPD interval via coda), "CR" (equal-tailed credible region), "HPD_many" (multimodal HPD interval via ggdist).

...

Additional arguments.

Value

A matrix with columns lower and upper. For "HPD_many", a parameter may appear on multiple rows when the interval is disjoint. The matrix has a Probability attribute.

Framingham Heart Study Dataset

Description

A subset of the Framingham Heart Study data.

Usage

fhs
fhs

Format

A data frame with 5944 rows and 39 variables:

RANDID: Unique identification number for each participant. (This is the strata for the matched pairs).
PERIOD: Examination Cycle where 0 = baseline, 1 = endpoint (the treatment variable)
SEX: Participant sex (1 = Male, 2 = Female).
TOTCHOL: Total serum cholesterol (mg/dL).
AGE: Age at exam (years).
SYSBP: Systolic blood pressure (mmHg).
DIABP: Diastolic blood pressure (mmHg).
CURSMOKE: Current smoking status (0 = No, 1 = Yes).
CIGPDAY: Number of cigarettes smoked per day.
BMI: Body Mass Index (kg/m^2).
DIABETES: Diabetes status (0 = No, 1 = Yes).
BPMEDS: Use of Anti-hypertensive medication (0 = No, 1 = Yes).
HEARTRTE: Heart rate (beats/minute).
GLUCOSE: Fast blood glucose (mg/dL).
educ: Education level.
PREVCHD: Prevalence of Coronary Heart Disease.
PREVAP: Prevalence of Angina Pectoris.
PREVMI: Prevalence of Myocardial Infarction.
PREVSTRK: Prevalence of Stroke.
PREVHYP: Prevalence of Hypertension.
TIME: Number of days since baseline exam.
HDLC: High Density Lipoprotein Cholesterol.
LDLC: Low Density Lipoprotein Cholesterol.
DEATH: Death status.
ANGINA: Angina Pectoris status.
HOSPMI: Hospitalized Myocardial Infarction.
MI_FCHD: Myocardial Infarction or Fatal Coronary Heart Disease.
ANYCHD: Any Coronary Heart Disease event.
STROKE: Stroke status.
CVD: Cardiovascular Disease status.
HYPERTEN: Hypertension status.
TIMEAP: Time to Angina Pectoris.
TIMEMI: Time to Myocardial Infarction.
TIMEMIFC: Time to Myocardial Infarction or Fatal CHD.
TIMECHD: Time to Any CHD.
TIMESTRK: Time to Stroke.
TIMECVD: Time to Cardiovascular Disease.
TIMEDTH: Time to Death.
TIMEHYP: Time to Hypertension.

Details

This dataset was constructed by running the following code:

pacman::p_load(riskCommunicator, data.table)
data("framingham")
D = data.table(framingham)
D = D[!is.na(CIGPDAY)] #we drop missing data in covariates
D = D[!is.na(BMI)]
D = D[!is.na(HEARTRTE)]
D = D[!is.na(TOTCHOL)]
Dba = D[PERIOD %in% c(1,3)] #we drop intermediate periods so we have matched pairs
Dba[PERIOD == 1, PERIOD := 0]
Dba[PERIOD == 3, PERIOD := 1]
Dba[, num_periods_per_id := .N, by = RANDID]
Dba = Dba[num_periods_per_id == 2] #we drop intermediate periods so we have matched pairs
Dba[, num_periods_per_id := NULL]

Source

https://biolincc.nhlbi.nih.gov/teaching/

Extract model formula

Description

Extract model formula

Usage

## S3 method for class 'bclogit'
formula(x, ...)
## S3 method for class 'bclogit'
formula(x, ...)

Arguments

x

A bclogit object.

...

Additional arguments.

Value

The formula used in the model, or NULL when the model was fitted via the default (matrix) interface.

Examples


data("fhs")
fit <- bclogit(PREVHYP ~ TOTCHOL + BMI, data = fhs,
               treatment = PERIOD, strata = RANDID)
formula(fit)

data("fhs")
fit <- bclogit(PREVHYP ~ TOTCHOL + BMI, data = fhs,
               treatment = PERIOD, strata = RANDID)
formula(fit)

Print summary of a bclogit model

Description

Print summary of a bclogit model

Usage

## S3 method for class 'summary.bclogit'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'summary.bclogit'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x

A summary.bclogit object.

digits

Number of significant digits to print.

...

Additional arguments.

Value

Invisibly returns x.

Examples


data("fhs")
fit <- bclogit(PREVHYP ~ TOTCHOL + BMI, data = fhs,
               treatment = PERIOD, strata = RANDID)
print(summary(fit))

data("fhs")
fit <- bclogit(PREVHYP ~ TOTCHOL + BMI, data = fhs,
               treatment = PERIOD, strata = RANDID)
print(summary(fit))

Print summary of a clogit_bclogit model

Description

Print summary of a clogit_bclogit model

Usage

## S3 method for class 'summary.clogit_bclogit'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'summary.clogit_bclogit'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x

A summary.clogit_bclogit object.

digits

Number of significant digits to print.

...

Additional arguments.

Value

Invisibly returns x.

Examples

n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5), x1 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(1:(n / 2), each = 2)
)
fit <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata)
print(summary(fit))
n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5), x1 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(1:(n / 2), each = 2)
)
fit <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata)
print(summary(fit))

Summarize a bclogit model

Description

Summarize a bclogit model

Usage

## S3 method for class 'bclogit'
summary(object, level = 0.95, inference_method = "HPD_one", ...)
## S3 method for class 'bclogit'
summary(object, level = 0.95, inference_method = "HPD_one", ...)

Arguments

object

A bclogit object.

level

Confidence level for credible intervals (default 0.95).

inference_method

Method used for both the displayed confidence set bounds and the p-value (computed via bisection over alpha). Options are: "HPD_one" (default) uses unimodal HPD intervals (C++) with 20 bisection iterations, "HPD_many" uses ggdist::hdi which supports disjoint (multimodal) HPD regions, with 50 bisection iterations (requires the ggdist package). Confidence set bounds are shown when the HPD is a single interval; if disjoint, they are set to NA (use confint.bclogit with type = "HPD_many" to see all intervals), "CR" uses equal-tailed credible intervals (quantile-based, C++) with 20 bisection iterations.

...

Additional arguments (not used).

Value

A list of class "summary.bclogit" containing:

call

The original function call.

coefficients

A matrix with one row per parameter and columns for the posterior mean estimate, posterior median estimate, standard error, lower and upper credible interval bounds, optionally Rhat and n_eff convergence diagnostics (when available from Stan), and Pr(tx!=0) (the Bayesian p-value).

num_discordant

Number of discordant pairs used for fitting.

num_concordant

Number of concordant pairs used for the prior.

level

The credible interval level used.

inference_method

The inference method used for interval and p-value computation.

prior_info

A list with elements mu and Sigma describing the prior derived from the concordant pairs model.

treatment_name

Name of the treatment variable.

Summarize a clogit_bclogit model

Description

Summarize a clogit_bclogit model

Usage

## S3 method for class 'clogit_bclogit'
summary(object, ...)
## S3 method for class 'clogit_bclogit'
summary(object, ...)

Arguments

object

A clogit_bclogit object.

...

Additional arguments (not used).

Value

A list of class "summary.clogit_bclogit" containing:

call

The original function call.

coefficients

A matrix with one row per parameter and columns Estimate, Std. Error, z value, and Pr(>|z|).

num_discordant

Number of discordant pairs used for fitting.

num_concordant

Number of concordant pairs.

n

Total number of observations.

treatment_name

Name of the treatment variable.

do_inference_on_var

The value of the do_inference_on_var argument.

Examples

n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5),
  x1 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(seq_len(n / 2), each = 2)
)
fit <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata)
summary(fit)
n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5),
  x1 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(seq_len(n / 2), each = 2)
)
fit <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata)
summary(fit)

Extract variance-covariance matrix from a bclogit model

Description

Extract variance-covariance matrix from a bclogit model

Usage

## S3 method for class 'bclogit'
vcov(object, ...)
## S3 method for class 'bclogit'
vcov(object, ...)

Arguments

object

A bclogit object.

...

Additional arguments.

Value

A square matrix of the posterior covariance of the coefficients, derived from the MCMC samples.

Examples


data("fhs")
fit <- bclogit(PREVHYP ~ TOTCHOL + BMI, data = fhs,
               treatment = PERIOD, strata = RANDID)
vcov(fit)

data("fhs")
fit <- bclogit(PREVHYP ~ TOTCHOL + BMI, data = fhs,
               treatment = PERIOD, strata = RANDID)
vcov(fit)

Extract variance-covariance matrix from a clogit_bclogit model

Description

Extract variance-covariance matrix from a clogit_bclogit model

Usage

## S3 method for class 'clogit_bclogit'
vcov(object, ...)
## S3 method for class 'clogit_bclogit'
vcov(object, ...)

Arguments

object

A clogit_bclogit object.

...

Additional arguments.

Value

A diagonal matrix of the estimated coefficient variances (squared standard errors), or NULL when do_inference_on_var is not "all".

Examples

n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5),
  x1 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(seq_len(n / 2), each = 2)
)
fit <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata)
vcov(fit)
n <- 200
dat <- data.frame(
  y = rbinom(n, 1, 0.5),
  x1 = rnorm(n),
  treatment = rep(c(0, 1), n / 2),
  strata = rep(seq_len(n / 2), each = 2)
)
fit <- clogit(y ~ x1, data = dat, treatment = treatment, strata = strata)
vcov(fit)

Package 'bclogit'

Help Index

Bayesian Conditional Logistic Regression with Concordant Pairs

Description

Details

Author(s)

References

See Also

Initialize a new bclogit model

Description

Usage

Arguments

Value

Methods (by class)

Numerical Stability in GEE Pre-modeling

See Also

Examples

Frequentist Conditional Logistic Regression

Description

Usage

Arguments

Value

Methods (by class)

See Also

Examples

Extract coefficients from a bclogit model

Description

Usage

Arguments

Value

See Also

Examples

Extract coefficients from a clogit_bclogit model

Description

Usage

Arguments

Value

Examples

Credible Intervals for bclogit Parameters

Description

Usage

Arguments

Value

See Also

Framingham Heart Study Dataset

Description

Usage

Format

Details

Source

Extract model formula

Description

Usage

Arguments

Value

See Also

Examples

Print summary of a bclogit model

Description

Usage

Arguments

Value

See Also

Examples

Print summary of a clogit_bclogit model

Description

Usage

Arguments

Value

Examples

Summarize a bclogit model

Description

Usage

Arguments

Value

See Also

Summarize a clogit_bclogit model

Description

Usage

Arguments