glmb.influence.measures: Bayesian Regression Diagnostics

In glmbayes: Bayesian Generalized Linear Models (IID Samples)

Bayesian Regression Diagnostics

Description

These functions compute Cook's distance, dfbetas, dffits, covratio, and standardized/studentized residuals for fitted Bayesian GLM objects. They delegate to the corresponding stats functions applied to the fitted GLM component.

Usage

glmb.influence.measures(model, infl = influence(model))

## S3 method for class 'glmb'
rstandard(model, ..., infl = influence(model))

## S3 method for class 'glmb'
rstudent(model, ..., infl = influence(model))

glmb.dffits(model, infl = influence(model))

## S3 method for class 'glmb'
dfbetas(model, ..., infl = influence(model))

glmb.covratio(model, infl = influence(model))

## S3 method for class 'glmb'
cooks.distance(model, ..., infl = influence(model))

Arguments

model	an R object, typically returned by lm or glm.
infl	influence structure as returned by influence.glmb (the latter only for the glm method of rstudent and cooks.distance).
...	further arguments passed to or from other methods.

Value

a list with components as returned by the underlying stats functions.

Examples

set.seed(333)
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)

ps <- Prior_Setup(counts ~ outcome + treatment, family = poisson())
mu <- ps$mu
V0 <- ps$Sigma
glmb.D93 <- glmb(
  counts ~ outcome + treatment,
  family = poisson(),
  pfamily = dNormal(mu = mu, Sigma = V0)
)

## Start setup here [First output from earlier optim optimization]
betastar <- glmb.D93$coef.mode # Posterior mode from optim
x <- glmb.D93$x
y <- glmb.D93$y
# The fitted object does not currently store an offset, so use zeros here.
offset2 <- 0 * y # Should return this from lower level functions
weights2 <- glmb.D93$prior.weights

## Check influence measures for original model
fit <- glmb.wfit(x, y, weights2, offset2, family = poisson(), Bbar = mu, P = solve(V0), betastar)
influence.measures(fit)

print(fit)
print(glmb.D93$coef.mode)

### Now try a strong prior with poorly chosen intercept
mu1 <- 0 * mu
V1 <- 0.1 * V0
glmb2.D93 <- glmb(
  counts ~ outcome + treatment,
  family = poisson(),
  pfamily = dNormal(mu = mu1, Sigma = V1)
)

Bbar2 <- mu1 # Prior mean
betastar2 <- glmb2.D93$coef.mode # Posterior mode from optim
fit2 <- glmb.wfit(x, y, weights2, offset2, family = poisson(), Bbar2, P = solve(V1), betastar2)

influence.measures(fit2)

print(fit2)
print(glmb2.D93$coef.mode)



influence(glmb2.D93)
influence(glmb2.D93, do.coef = TRUE)
influence(glmb2.D93, do.coef = FALSE)

glmb.influence.measures(glmb2.D93, influence(glmb2.D93))


## Do not need method functions
dfbeta(glmb2.D93, influence(glmb2.D93))
dfbeta(glmb2.D93$fit, influence(glmb2.D93))
hatvalues(glmb2.D93, influence(glmb2.D93))
hatvalues(glmb2.D93$fit, influence(glmb2.D93))

# Implemented methods

rstandard(glmb2.D93, infl = influence(glmb2.D93))
rstandard(glmb2.D93)


# Methods requiring dedicated handling

## Needs a method function
dfbetas(glmb2.D93)
dfbetas(glmb2.D93, influence(glmb2.D93, do.coef = TRUE))
dfbetas(glmb2.D93$fit, influence(glmb2.D93))



# Needs a method function
cooks.distance(glmb2.D93)
cooks.distance(glmb2.D93$fit, influence(glmb2.D93))



# The rstandard method now works directly on glmb objects.


# Needs a method function
rstudent(glmb2.D93)
rstudent(glmb2.D93$fit, influence(glmb2.D93))


# Not a method, separate function
glmb.dffits(glmb2.D93)
dffits(glmb2.D93$fit, influence(glmb2.D93))

# Not a method - separate function
glmb.covratio(glmb2.D93)
covratio(glmb2.D93$fit, influence(glmb2.D93))



# Needs a method function but requires a different approach
# The influence function actually stores this measure
## This seems to require more work to create a method function

infl <- influence(glmb2.D93)
hat2 <- infl$hat
hat2

glmbayes documentation built on Aug. 5, 2026, 1:07 a.m.