On this R-data statistics page, you will find information about the carrots data set which pertains to Insect Damages on Carrots. The carrots data set is found in the robustbase R package. You can load the carrots data set in R by issuing the following command at the console data("carrots"). This will load the data into a variable called carrots. If R says the carrots data set is not found, you can try installing the package by issuing this command install.packages("robustbase") 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 carrots R data set. The size of this file is about 394 bytes.

Insect Damages on Carrots

Description

The damage carrots data set from Phelps (1982) was used by McCullagh and Nelder (1989) in order to illustrate diagnostic techniques because of the presence of an outlier. In a soil experiment trial with three blocks, eight levels of insecticide were applied and the carrots were tested for insect damage.

Usage

data(carrots)

Format

A data frame with 24 observations on the following 4 variables.

success

integer giving the number of carrots with insect damage.

total

integer giving the total number of carrots per experimental unit.

logdose

a numeric vector giving log(dose) values (eight different levels only).

block

factor with levels B1 to B3

Source

Phelps, K. (1982). Use of the complementary log-log function to describe doseresponse relationships in insecticide evaluation field trials.
In R. Gilchrist (Ed.), Lecture Notes in Statistics, No. 14. GLIM.82: Proceedings of the International Conference on Generalized Linear Models; Springer-Verlag.

References

McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.

Eva Cantoni and Elvezio Ronchetti (2001); JASA, and
Eva Cantoni (2004); JSS, see glmrob

Examples

data(carrots)
str(carrots)
plot(success/total ~ logdose, data = carrots, col = as.integer(block))
coplot(success/total ~ logdose | block, data = carrots)## Classical glm
Cfit0 <- glm(cbind(success, total-success) ~ logdose + block,
             data=carrots, family=binomial)
summary(Cfit0)## Robust Fit (see help(glmrob)) ....

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Dataset imported from https://www.r-project.org.