Wolf skulls

Skull morphometric data on Rocky Mountain and Arctic wolves (Canis Lupus L.) taken from Morrison (1990), originally from Jolicoeur (1959).

Format

A data frame with 25 observations on the following 11 variables.

group

a factor with levels ar:f ar:m rm:f rm:m, comprising the combinations of location and sex

location

a factor with levels ar=Arctic, rm=Rocky Mountain

sex

a factor with levels f=female, m=male

x1

palatal length, a numeric vector

x2

postpalatal length, a numeric vector

x3

zygomatic width, a numeric vector

x4

palatal width outside first upper molars, a numeric vector

x5

palatal width inside second upper molars, a numeric vector

x6

postglenoid foramina width, a numeric vector

x7

interorbital width, a numeric vector

x8

braincase width, a numeric vector

x9

crown length, a numeric vector

Source

Morrison, D. F. Multivariate Statistical Methods, (3rd ed.), 1990. New York: McGraw-Hill, p. 288-289.

Details

All variables are expressed in millimeters.

The goal was to determine how geographic and sex differences among the wolf populations are determined by these skull measurements. For MANOVA or (canonical) discriminant analysis, the factors group or location and sex provide alternative parameterizations.

References

Jolicoeur, P. “Multivariate geographical variation in the wolf Canis lupis L.”, Evolution, XIII, 283–299.

Examples

data(Wolves)

# using group
wolf.mod <-lm(cbind(x1,x2,x3,x4,x5,x6,x7,x8,x9) ~ group, data=Wolves)
car::Anova(wolf.mod)
#> 
#> Type II MANOVA Tests: Pillai test statistic
#>       Df test stat approx F num Df den Df    Pr(>F)    
#> group  3    2.2454   4.9592     27     45 1.191e-06 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

wolf.can <-candisc(wolf.mod)
plot(wolf.can)
#> Vector scale factor set to 4.885

heplot(wolf.can)

#> Vector scale factor set to  12.04378 

# using location, sex
wolf.mod2 <-lm(cbind(x1,x2,x3,x4,x5,x6,x7,x8,x9) ~ location*sex, data=Wolves)
car::Anova(wolf.mod2)
#> 
#> Type II MANOVA Tests: Pillai test statistic
#>              Df test stat approx F num Df den Df    Pr(>F)    
#> location      1   0.95246   28.938      9     13 3.624e-07 ***
#> sex           1   0.84633    7.955      9     13 0.0005229 ***
#> location:sex  1   0.64944    2.676      9     13 0.0523865 .  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

wolf.can2 <-candiscList(wolf.mod2)
plot(wolf.can2)