cbpp: Contagious bovine pleuropneumonia

In lme4: Linear Mixed-Effects Models using 'Eigen' and S4

Contagious bovine pleuropneumonia

Description

Contagious bovine pleuropneumonia (CBPP) is a major disease of cattle in Africa, caused by a mycoplasma. This dataset describes the serological incidence of CBPP in zebu cattle during a follow-up survey implemented in 15 commercial herds located in the Boji district of Ethiopia. The goal of the survey was to study the within-herd spread of CBPP in newly infected herds. Blood samples were quarterly collected from all animals of these herds to determine their CBPP status. These data were used to compute the serological incidence of CBPP (new cases occurring during a given time period). Some data are missing (lost to follow-up).

There are two datasets, although their precise origins are unclear. The dataset used in the classical lme4 examples, cbpp, matches the file maintained by the package authors. The extended dataset, cbpp2, corresponds to Table 1 of \insertCitelesnoff2004withinlme4, with the exception of herd 6, which is a known typographical error in the original paper.

Format

cbpp is a data frame with 56 observations on the following 4 variables.

herd

A factor identifying the herd (1 to 15).

incidence

The number of new serological cases for a given herd and time period.

size

A numeric vector describing herd size at the beginning of a given time period.

period

A factor with levels 1 to 4.

The extended version, cbpp2, has the additional variables:

herd

A factor identifying the herd (1 to 15).

treatment

A factor referring to the control measure used to manage CBPP.

• Complete = complete isolation or antibiotic treatment,

• Partial/null = partial/null isolation and no antibiotic treatment,

• Unknown = strategy remained.

avg_size

The average number of animals housed in a della (a temporary paddock used for holding cattle on the farm).

Details

Serological status was determined using a competitive enzyme-linked immuno-sorbent assay (cELISA).

Source

\insertCite

lesnoff2004withinlme4

References

\insertRef

lesnoff2004withinlme4

Examples

## response as a matrix
(m1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
             family = binomial, data = cbpp))
## response as a vector of probabilities and usage of argument "weights"
m1p <- glmer(incidence / size ~ period + (1 | herd), weights = size,
             family = binomial, data = cbpp)
## Confirm that these are equivalent:
stopifnot(all.equal(fixef(m1), fixef(m1p), tolerance = 1e-5),
          all.equal(ranef(m1), ranef(m1p), tolerance = 1e-5))


## GLMM with individual-level variability (accounting for overdispersion)
cbpp$obs <- 1:nrow(cbpp)
(m2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd) +  (1|obs),
              family = binomial, data = cbpp))
              
## Fitting the model for cbpp2
gm1 <- glmer(incidence/size ~ period + treatment + avg_size + (1 | herd),
             family = binomial,
             data = cbpp2, weights = size,
             control = glmerControl(optimizer="bobyqa"))
## Adding an observation-level random effect
cbpp2 <- transform(cbpp2,obs=factor(seq(nrow(cbpp2))))
## Herd and observation-level REs (below causes singular fit issues)
gm2 <- update(gm1,.~.+(1|obs)) 
## observation-level REs only (no singular fit issue)
gm3 <- update(gm1,.~.-(1|herd)+(1|obs)) 

lme4 documentation built on March 6, 2026, 1:07 a.m.