| calcium {GSE} | R Documentation |
Calcium data
Description
The Calcium data is from the article by Holcomb and Spalsbury (2005). The dataset used for class was compiled by Boyd, Delost, and Holcomb (1998) for the use of a study to determine if significant gender differences existed between subjects 65 years of age and older with regard to calcium, inorganic phosphorous, and alkaline phosphatase levels. Although the original data from Boyd, Delost, and Holcomb (1998) had observations needing investigation, Holcomb and Spalsbury (2005) further massaged the original data to include data problems and issues that have arisen in other research projects for pedagogical purposes.
Usage
data(calcium)Format
A data frame with 178 observations on the following 8 variables.
obsno | Patient Observation Number |
age | Age in years |
sex | 1=Male, 2=Female |
alkphos | Alkaline Phosphatase International Units/Liter |
lab | 1=Metpath; 2=Deyor; 3=St. Elizabeth's; 4=CB Rouche; 5=YOH; 6=Horizon |
cammol | Calcium mmol/L |
phosmmol | Inorganic Phosphorus mmol/L |
agegroup | Age group 1=65-69; 2=70-74; 3=75-79; 4=80-84; 5=85-89 Years |
Source
The original data have been taken from the Journal of Statistics Education Databases at
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http://jse.amstat.org/datasets/calcium.dat.txt (originally downloaded from www.amstat.org/publications/jse/datasets/calcium.dat.txt),
the corrected data have been taken from Statlib at
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http://jse.amstat.org/datasets/calciumgood.dat.txt (originally downloaded from www.amstat.org/publications/jse/datasets/calciumgood.dat.txt)
References
Boyd, J., Delost, M., and Holcomb, J., (1998). Calcium, phosphorus, and alkaline phosphatase laboratory values of elderly subjects, Clinical Laboratory Science, 11, 223-227.
Holcomb, J., and Spalsbury, A. (2005), Teaching Students to Use Summary Statistics and Graphics to Clean and Analyze Data. Journal of Statistics Education, 13, Number 3.
Examples
## Not run:
data(calcium)
## remove the categorical variables
calcium.cts <- subset(calcium, select=-c(obsno, sex, lab, agegroup) )
res <- GSE(calcium.cts)
getOutliers(res)
## able to identify majority of the contaminated cases identified
## in the reference
## End(Not run)