lme4 (version 1.1-38)

cbpp2: Contagious bovine pleuropneumonia (extended version)

Description

This dataset is an extension of cbpp, which describes the serological incidence of CBPP (Contagious bovine pleuropneumonia) in zebu cattle during a follow-up survey implemented in 15 commercial herds located in the Boji district of Ethiopia lesnoff2004withinlme4. There are two extra covariates: treatment and avg_size.

Format

A data frame with 56 observations on the following 6 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).

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.

Details

The description here is identical to the cbpp dataset: Contagious bovine pleuropneumonia (CBPP) is a major disease of cattle in Africa, caused by a mycoplasma. 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). Serological status was determined using a competitive enzyme-linked immuno-sorbent assay (cELISA).

References

lesnoff2004withinlme4

See Also

The shorter version, cbpp.

Examples

## Fitting the model 
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))