litters R Documentation

## 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 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)
```