bernoulli {lax}

Inference for the Bernoulli distribution

Description

Functions involved in making inferences about the probability of success in a Bernoulli distribution.

Usage

fit_bernoulli(data)

## S3 method for class 'bernoulli'
logLikVec(object, pars = NULL, ...)

## S3 method for class 'bernoulli'
nobs(object, ...)

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

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

## S3 method for class 'bernoulli'
logLik(object, ...)

## S3 method for class 'bernoulli'
alogLik(x, cluster = NULL, use_vcov = TRUE, ...)

Arguments

data

A numeric vector of outcomes from Bernoulli trials: 0 for a failure, 1 for a success. Alternatively, a logical vector with FALSE for a failure and TRUE for a success.

pars

A numeric parameter vector of length 1 containing the value of the Bernoulli success probability.

...

Further arguments to be passed to the functions in the sandwich package meat (if cluster = NULL), or meatCL (if cluster is not NULL).

x, object

A fitted model object returned from fit_bernoulli().

cluster

A vector or factor indicating from which cluster each observation in data originates.

use_vcov

A logical scalar. Should we use the vcov S3 method for x (if this exists) to estimate the Hessian of the independence loglikelihood to be passed as the argument H to adjust_loglik? Otherwise, H is estimated inside adjust_loglik using optimHess.

Details

fit_bernoulli: fit a Bernoulli distribution

logLikVec.bernoulli: calculates contributions to a loglikelihood based on the Bernoulli distribution. The loglikelihood is calculated up to an additive constant.

nobs, coef, vcov and logLik methods are provided.

Value

fit_bernoulli returns an object of class "bernoulli", a list with components: logLik, mle, nobs, vcov, data, obs_data, where data are the input data and obs_data are the input data after any missing values have been removed, using na.omit.

logLikVec.bernoulli returns an object of class "logLikVec", a vector length length(data) containing the likelihood contributions from the individual observations in data.

See Also

Binomial. The Bernoulli distribution is the special case where size = 1.

Examples

# Set up data
x <- exdex::newlyn
u <- quantile(x, probs = 0.9)
exc <- x > u

# Fit a Bernoulli distribution
fit <- fit_bernoulli(exc)

# Calculate the loglikelihood at the MLE
res <- logLikVec(fit)

# The logLik method sums the individual loglikelihood contributions.
logLik(res)

# nobs, coef, vcov, logLik methods for objects returned from fit_bernoulli()
nobs(fit)
coef(fit)
vcov(fit)
logLik(fit)

# Adjusted loglikelihood
# Create 5 clusters each corresponding approximately to 1 year of data
cluster <- rep(1:5, each = 579)[-1]
afit <- alogLik(fit, cluster = cluster, cadjust = FALSE)
summary(afit)