On this Picostat.com statistics page, you will find information about the CoalMiners data set which pertains to Breathlessness and Wheeze in Coal Miners. The CoalMiners data set is found in the vcd R package. Try to load the CoalMiners data set in R by issuing the following command at the console data("CoalMiners"). This may load the data into a variable called CoalMiners. If R says the CoalMiners data set is not found, you can try installing the package by issuing this command install.packages("vcd") and then attempt to reload the data with library("vcd") followed by data("CoalMiners"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing CoalMiners at the command-line which should display the entire dataset.
If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the CoalMiners R data set. The size of this file is about 825 bytes.
Breathlessness and Wheeze in Coal Miners
Data from Ashford & Sowden (1970) given by Agresti (1990) on the
association between two pulmonary conditions, breathlessness and
wheeze, in a large sample of coal miners who were smokers with no
radiological evidence of pneumoconlosis, aged between 20–64
This data is frequently used as an example of fitting models for
bivariate, binary responses.
A 3-dimensional table of size 2 x 2 x 9
resulting from cross-tabulating variables for
18,282 coal miners. The variables and their levels are as follows:
No || Name || Levels
1 || Breathlessness || B, NoB
2 || Wheeze || W, NoW
3 || Age || 20-24, 25-29, 30-34, ..., 60-64
In an earlier version of this data set, the first group, aged 20-24, was
inadvertently omitted from this data table and the breathlessness variable
was called wheeze and vice versa.
Michael Friendly (2000),
Visualizing Categorical Data, pages 82–83, 319–322.
A. Agresti (1990),
Categorical Data Analysis.
Wiley-Interscience, New York, Table 7.11, p. 237
J. R. Ashford and R. D. Sowdon (1970),
Multivariate probit analysis,
Biometrics, 26, 535–546.
M. Friendly (2000),
Visualizing Categorical Data.
SAS Institute, Cary, NC.
data("CoalMiners")ftable(CoalMiners, row.vars = 3)## Fourfold display, both margins equated
fourfold(CoalMiners[,,2:9], mfcol = c(2,4))## Fourfold display, strata equated
fourfold(CoalMiners[,,2:9], std = "ind.max", mfcol = c(2,4))
## Log Odds Ratio Plot
lor_CM <- loddsratio(CoalMiners)
lor_CM_df <- as.data.frame(lor_CM)# fit linear models using WLS
age <- seq(20, 60, by = 5)
lmod <- lm(LOR ~ age, weights = 1 / ASE^2, data = lor_CM_df)
grid.lines(age, fitted(lmod), gp = gpar(col = "blue"))
qmod <- lm(LOR ~ poly(age, 2), weights = 1 / ASE^2, data = lor_CM_df)
grid.lines(age, fitted(qmod), gp = gpar(col = "red"))
Dataset imported from https://www.r-project.org.