Contagious bovine pleuropneumonia

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).

Format

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.

Source

Lesnoff, M., Laval, G., Bonnet, P., Abdicho, S., Workalemahu, A., Kifle, D., Peyraud, A., Lancelot, R., Thiaucourt, F. (2004) Within-herd spread of contagious bovine pleuropneumonia in Ethiopian highlands. Preventive Veterinary Medicine 64, 27–40.

Details

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

Examples

## response as a matrix
(m1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
             family = binomial, data = cbpp))
#> Generalized linear mixed model fit by maximum likelihood (Laplace
#>   Approximation) [glmerMod]
#>  Family: binomial  ( logit )
#> Formula: cbind(incidence, size - incidence) ~ period + (1 | herd)
#>    Data: cbpp
#>      AIC      BIC   logLik deviance df.resid 
#> 194.0531 204.1799 -92.0266 184.0531       51 
#> Random effects:
#>  Groups Name        Std.Dev.
#>  herd   (Intercept) 0.6421  
#> Number of obs: 56, groups:  herd, 15
#> Fixed Effects:
#> (Intercept)      period2      period3      period4  
#>     -1.3983      -0.9919      -1.1282      -1.5797  
## 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))
#> Generalized linear mixed model fit by maximum likelihood (Laplace
#>   Approximation) [glmerMod]
#>  Family: binomial  ( logit )
#> Formula: cbind(incidence, size - incidence) ~ period + (1 | herd) + (1 |  
#>     obs)
#>    Data: cbpp
#>      AIC      BIC   logLik deviance df.resid 
#> 186.6383 198.7904 -87.3192 174.6383       50 
#> Random effects:
#>  Groups Name        Std.Dev.
#>  obs    (Intercept) 0.8911  
#>  herd   (Intercept) 0.1840  
#> Number of obs: 56, groups:  obs, 56; herd, 15
#> Fixed Effects:
#> (Intercept)      period2      period3      period4  
#>      -1.500       -1.226       -1.329       -1.866