| Pottery2 | R Documentation |
Results of chemical analyses of 48 specimens of Romano-British pottery
published by Tubb et al. (1980). The numbers are the percentage of various
metal oxides found in each sample for elements of concentrations greater
than 0.01%. This is the original data set from Tubb et al. (1980), in
contrast to Pottery.
A data frame with 48 observations on the following 12 variables.
Regiona factor with levels Gl NF
Wales
Sitea factor with levels AshleyRails
Caldicot Gloucester IsleThorns Llanedryn
Kilna factor with levels 1 2 3 4
5
Alamount of aluminum oxide, Al_2O_3
Feamount of iron oxide, Fe_2O_3
Mgamount of magnesium oxide, MgO
Caamount of calcium oxide, CaO
Naamount of sodium oxide, Na_2O
Kamount of potassium oxide, K_2O
Tiamount of titanium oxide, TiO_2
Mnamount of manganese oxide, MnO
Baamount of BaO
The specimens are identified by their rownames in the data frame.
Kiln indicates at which kiln site the pottery was found; Site
gives the location names of those sites. The kiln sites come from three
Regions, ("Gl"=1, "Wales"=(2, 3), "NF"=(4, 5)), where the full
names are "Gloucester", "Wales", and "New Forrest".
The variable Kiln comes pre-supplied with contrasts to test
interesting hypotheses related to Site and Region.
Originally slightly modified from files by David Carlson, now at
RBPottery.
Baxter, M. J. 2003. Statistics in Archaeology. Arnold, London.
Carlson, David L. 2017. Quantitative Methods in Archaeology Using R. Cambridge University Press, pp 247-255, 335-342.
Tubb, A., A. J. Parker, and G. Nickless. 1980. The Analysis of Romano-British Pottery by Atomic Absorption Spectrophotometry. Archaeometry, 22, 153-171.
Pottery for the related (subset) data set;
RBPottery for a newer version with more variables.
library(car)
data(Pottery2)
# contrasts for Kiln correspond to between Region [,1:2] and within Region [,3:4]
contrasts(Pottery2$Kiln)
pmod <-lm(cbind(Al,Fe,Mg,Ca,Na,K,Ti,Mn,Ba)~Kiln, data=Pottery2)
car::Anova(pmod)
# extract coefficient names for linearHypotheses
coefs <- rownames(coef(pmod))[-1]
# test differences among regions
linearHypothesis(pmod, coefs[1:2])
# test differences within regions B, C
linearHypothesis(pmod, coefs[3:4])
heplot(pmod, fill=c(TRUE,FALSE), hypotheses=list("Region" =coefs[1:2], "WithinBC"=coefs[3:4]))
# all pairwise views; note that Ba shows no effect
pairs(pmod, fill=c(TRUE,FALSE))
# canonical view, via candisc::heplot
if (require(candisc)) {
# canonical analysis: how many dimensions?
(pcan <- candisc(pmod))
heplot(pcan, scale=18, fill=c(TRUE,FALSE), var.col="darkgreen", var.lwd=2, var.cex=1.5)
## Not run:
heplot3d(pcan, scale=8)
## End(Not run)
}
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