Olorgesailie.maj {archdata} | R Documentation |
Major stone tool classes, Olorgesailie, Kenya
Description
The data represent the number of specimens in each of 6 major artifact classes recovered from 19 localities at the Lower Paleolithic site of Olorgesailie as described in Isaac (1977).
Usage
data(Olorgesailie.maj)
Format
A data frame with 19 observations showing the number of specimens for each of 6 stone artifact types.
Large.cutting.tools
Number of large cutting tools
Heavy.duty.tools
Number of heavy duty tools
Large.scrapers
Number of large scrapers
Other.large.tools
Number of other large tools
Small.tools
Number of small tools
Spheroids
Number of spheroids
Details
The data come from Table E1 in Isaac (1977: 239). The rownames
identify localities in the lower, middle and upper strata to provide relative chronological placement. They are in the same order as the columns in the table: LS1 (BBB), LS2 (BBA), LS3(FB), LS4(FB-HL), LS5(FB-I3), MS1a(DE/89 A-L), MS1b(DE/89 A-I), MS2a(DE/89 B-L), MS2b(DE/89 B-I), MS3(DE/89 C), MS4(H/6), MS5(H/9 A), MS6(H/9 AM), MS7(Mid), MS8(Meng), MS9(LHS), US1(TRTrM10), US2(Hog), US3(MFS). Potts (2011) provides updated information on the site complex.
Source
Isaac, Glynn Ll. 1977. Olorgesailie: Archeological Studies of a Middle Pleistocene Lake Basin in Kenya. The University of Chicago Press.
References
Carlson, David L. 2017. Quantitative Methods in Archaeology Using R. Cambridge University Press, pp 115-119, 138-142.
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 Resarch Monographs in Archaeology and Paleoanthropology.
Examples
data(Olorgesailie.maj)
# 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.maj, simulate.p.value=TRUE)
# Compute percentages over the localities
Olor.pct <- prop.table(as.matrix(Olorgesailie.maj), 1)*100
boxplot(Olor.pct)