litters: Mouse Litters

In jhmaindonald/DAAG: Data Analysis and Graphics Data and Functions

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 labeling

mice1.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 & bodywt

summary(mice1.lm)$coef # Similarly for other coefficients.
# results are consistent with the biological concept of brain sparing

pause()

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)

jhmaindonald/DAAG documentation built on May 3, 2019, 3:13 p.m.