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 Litters

Description

Data on the body and brain weights of 20 mice, together with the size of the litter. Two mice were taken from each litter size.

Usage

litters

Format

This data frame contains the following columns:

lsize

litter size

bodywt

body weight

brainwt

brain weight

Source

Wainright P, Pelkman C and Wahlsten D 1989. The quantitative relationship between nutritional effects on preweaning growth and behavioral development in mice. Developmental Psychobiology 22: 183-193.

Examples

print("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)

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