Description Usage Format Details Source References Examples
The data represent the number of specimens in each of 16 artifact subclasses recovered from 19 localities at the Lower Paleolithic site of Olorgesailie as described in Isaac (1977).
1 |
A data frame with 19 observations showing the stratum, locality and the number of specimens for each of 16 stone artifact types.
Strat
stratum: Lower
, Middle
, Upper
Locality
Locality
HA
Number of handaxes
PHA
Number of pick-like handaxes
CHA
Number of chisel handaxes
CL
Number of cleavers
KN
Number of knives
BLCT
Number of broken large cutting tools
PAT
Number of picks and trièdres
CH
Number of choppers
CS
Number of core scrapers
LFS
Number of large flake scrapers
CB
Number of core bifaces
OLT
Number of other large tools
SSS
Number of small scrapers simple
SSNP
Number of small scrapers nosed point
OST
Number of other small tools
SP
Number of spheroids
The data come from Table E1 in Isaac (1977: 239). The Locality
contains the column headings in the original table. The rownames
are the same as those in Olorgesailie.maj
. The attribute Variables
in the data frame includes the full variable names. Potts (2011) provides updated information on the site complex.
Isaac, Glynn Ll. 1977. Olorgesailie: Archeological Studies of a Middle Pleistocene Lake Basin in Kenya. The University of Chicago Press.
Carlson, David L. 2017. Quantitative Methods in Archaeology Using R. Cambridge University Press, pp 280-293.
Potts, R. 2011. Olorgesailie–Retrospective and current synthesis. In Casting the net wide: papers in honor of Glynn Isaac and his approach to human origins research, edited by J. Sept and D. Pilbeam, pp 1–20. American School of Prehistoric Research Monographs in Archaeology and Paleoanthropology.
1 2 3 4 5 6 7 | data(Olorgesailie.sub)
# Chi square after removing the first two columns and simulating the p
# value since there are a number of very small expected values
chisq.test(Olorgesailie.sub[,3:18], simulate.p.value=TRUE)
# Compute percentages over the localities
Olor.pct <- prop.table(as.matrix(Olorgesailie.sub[,3:18]), 1)*100
boxplot(Olor.pct, cex.axis=.7)
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