R Dataset / Package DAAG / litters
Attachment  Size 

dataset30790.csv  301 bytes 
Documentation 

On this Picostat.com statistics page, you will find information about the litters data set which pertains to Mouse Litters. The litters data set is found in the DAAG R package. You can load the litters data set in R by issuing the following command at the console data("litters"). This will load the data into a variable called litters. If R says the litters 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 litters R data set. The size of this file is about 301 bytes. Mouse LittersDescriptionData on the body and brain weights of 20 mice, together with the size of the litter. Two mice were taken from each litter size. Usagelitters FormatThis data frame contains the following columns:
SourceWainright P, Pelkman C and Wahlsten D 1989. The quantitative relationship between nutritional effects on preweaning growth and behavioral development in mice. Developmental Psychobiology 22: 183193. Examplesprint("Multiple Regression  Example 6.2")pairs(litters, labels=c("lsize\n\n(litter size)", "bodywt\n\n(Body Weight)", "brainwt\n\n(Brain Weight)")) # pairs(litters) gives a scatterplot matrix with less adequate labelingmice1.lm < lm(brainwt ~ lsize, data = litters) # Regress on lsize mice2.lm < lm(brainwt ~ bodywt, data = litters) #Regress on bodywt mice12.lm < lm(brainwt ~ lsize + bodywt, data = litters) # Regress on lsize & bodywtsummary(mice1.lm)$coef # Similarly for other coefficients. # results are consistent with the biological concept of brain sparingpause()hat(model.matrix(mice12.lm)) # hat diagonal pause()plot(lm.influence(mice12.lm)$hat, residuals(mice12.lm))print("Diagnostics  Example 6.3")mice12.lm < lm(brainwt ~ bodywt+lsize, data=litters) oldpar <par(mfrow = c(1,2)) bx < mice12.lm$coef[2]; bz < mice12.lm$coef[3] res < residuals(mice12.lm) plot(litters$bodywt, bx*litters$bodywt+res, xlab="Body weight", ylab="Component + Residual") panel.smooth(litters$bodywt, bx*litters$bodywt+res) # Overlay plot(litters$lsize, bz*litters$lsize+res, xlab="Litter size", ylab="Component + Residual") panel.smooth(litters$lsize, bz*litters$lsize+res) par(oldpar)  Dataset imported from https://www.rproject.org. 
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