# R Dataset / Package lme4 / cbpp

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On this R-data statistics page, you will find information about the cbpp data set which pertains to Contagious bovine pleuropneumonia. The cbpp data set is found in the lme4 R package. You can load the cbpp data set in R by issuing the following command at the console data("cbpp"). This will load the data into a variable called cbpp. If R says the cbpp data set is not found, you can try installing the package by issuing this command install.packages("lme4") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the cbpp R data set. The size of this file is about 772 bytes. ## Contagious bovine pleuropneumonia## DescriptionContagious 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). ## FormatA 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` .
## DetailsSerological status was determined using a competitive enzyme-linked immuno-sorbent assay (cELISA). ## SourceLesnoff, 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. ## 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)) -- Dataset imported from https://www.r-project.org. |

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Attachment | Size |
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dataset-86341.csv | 772 bytes |