R Dataset / Package DAAG / ACF1

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<iframe src="https://embed.picostat.com/r-dataset-package-daag-acf1.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-74650.csv 122 bytes
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the ACF1 data set which pertains to Aberrant Crypt Foci in Rat Colons. The ACF1 data set is found in the DAAG R package. You can load the ACF1 data set in R by issuing the following command at the console data("ACF1"). This will load the data into a variable called ACF1. If R says the ACF1 data set is not found, you can try installing the package by issuing this command install.packages("DAAG") 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 ACF1 R data set. The size of this file is about 122 bytes.

Documentation

Aberrant Crypt Foci in Rat Colons

Description

Numbers of aberrant crypt foci (ACF) in the section 1 of the colons of 22 rats subjected to a single dose of the carcinogen azoxymethane (AOM), sacrificed at 3 different times.

Usage

ACF1

Format

This data frame contains the following columns:

count

The number of ACF observed in section 1 of each rat colon

endtime

Time of sacrifice, in weeks following injection of AOM

Source

Ranjana P. Bird, Faculty of Human Ecology, University of Manitoba, Winnipeg, Canada.

References

E.A. McLellan, A. Medline and R.P. Bird. Dose response and proliferative characteristics of aberrant crypt foci: putative preneoplastic lesions in rat colon. Carcinogenesis, 12(11): 2093-2098, 1991.

Examples

sapply(split(ACF1$count,ACF1$endtime),var)
plot(count ~ endtime, data=ACF1, pch=16)
pause()
print("Poisson Regression - Example 8.3")
ACF.glm0 <- glm(formula = count ~ endtime, family = poisson, data = ACF1)
summary(ACF.glm0)# Is there a quadratic effect?
pause()ACF.glm <- glm(formula = count ~ endtime + I(endtime^2),
  family = poisson, data = ACF1)
summary(ACF.glm)# But is the data really Poisson?  If not, try quasipoisson:
pause()ACF.glm <- glm(formula = count ~ endtime + I(endtime^2),
  family = quasipoisson, data = ACF1)
summary(ACF.glm)
--

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

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