R Dataset / Package DAAG / science
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| Attachment | Size |
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| dataset-42162.csv | 50.01 KB |
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
| Documentation |
|---|
On this Picostat.com statistics page, you will find information about the science data set which pertains to School Science Survey Data. The science data set is found in the DAAG R package. Try to load the science data set in R by issuing the following command at the console data("science"). This may load the data into a variable called science. If R says the science 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("science"). 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 science 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 science R data set. The size of this file is about 51,215 bytes. School Science Survey DataDescriptionThe The data are on attitudes to science, from a survey where there were results from 20 classes in private schools and 46 classes in public schools. Usagescience FormatThis data frame contains the following columns:
SourceFrancine Adams, Rosemary Martin and Murali Nayadu, Australian National University Examples
classmeans <- with(science, aggregate(like, by=list(PrivPub, Class), mean))
names(classmeans) <- c("PrivPub","Class","like")
dim(classmeans)attach(classmeans)
boxplot(split(like, PrivPub), ylab = "Class average of attitude to science score", boxwex = 0.4)
rug(like[PrivPub == "private"], side = 2)
rug(like[PrivPub == "public"], side = 4)
detach(classmeans)
if(require(lme4, quietly=TRUE)) {
science.lmer <- lmer(like ~ sex + PrivPub + (1 | school) +
(1 | school:class), data = science,
na.action=na.exclude)
summary(science.lmer)
science1.lmer <- lmer(like ~ sex + PrivPub + (1 | school:class),
data = science, na.action=na.exclude)
summary(science1.lmer)
ranf <- ranef(obj = science1.lmer, drop=TRUE)[["school:class"]]
flist <- science1.lmer@flist[["school:class"]]
privpub <- science[match(names(ranf), flist), "PrivPub"]
num <- unclass(table(flist)); numlabs <- pretty(num)
## Plot effect estimates vs numbers
plot(sqrt(num), ranf, xaxt="n", pch=c(1,3)[as.numeric(privpub)],
xlab="# in class (square root scale)",
ylab="Estimate of class effect")
lines(lowess(sqrt(num[privpub=="private"]),
ranf[privpub=="private"], f=1.1), lty=2)
lines(lowess(sqrt(num[privpub=="public"]),
ranf[privpub=="public"], f=1.1), lty=3)
axis(1, at=sqrt(numlabs), labels=paste(numlabs))
}
-- Dataset imported from https://www.r-project.org. |
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