R Dataset / Package DAAG / ACF1
Attachment | Size |
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dataset-74650.csv | 122 bytes |
Documentation |
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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. Try to load the ACF1 data set in R by issuing the following command at the console data("ACF1"). This may 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 with library("DAAG") followed by data("ACF1"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing ACF1 at the command-line which should display the entire dataset. 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. Aberrant Crypt Foci in Rat ColonsDescriptionNumbers 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. UsageACF1 FormatThis data frame contains the following columns:
SourceRanjana P. Bird, Faculty of Human Ecology, University of Manitoba, Winnipeg, Canada. ReferencesE.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. Examplessapply(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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