R Dataset / Package COUNT / azpro

On this R-data statistics page, you will find information about the azpro data set which pertains to azpro. The azpro data set is found in the COUNT R package. You can load the azpro data set in R by issuing the following command at the console data("azpro"). This will load the data into a variable called azpro. If R says the azpro data set is not found, you can try installing the package by issuing this command install.packages("COUNT") and then attempt to reload the data with the library() command. 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 azpro R data set. The size of this file is about 82,747 bytes.

azpro

Description

Data come from the 1991 Arizona cardiovascular patient files. A subset of the fields was selected to model the differential length of stay for patients entering the hospital to receive one of two standard cardiovascular procedures: CABG and PTCA. CABG is the standard acronym for Coronary Artery Bypass Graft, where the flow of blood in a diseased or blocked coronary artery or vein has been grafted to bypass the diseased sections. PTCA, or Percutaneous Transluminal Coronary Angioplasty, is a method of placing a balloon in a blocked coronary artery to open it to blood flow. It is a much less severe method of treatment for those having coronary blockage, with a corresponding reduction in risk.

Usage

data(azpro)

Format

A data frame with 3589 observations on the following 6 variables.

los: length of hospital stay
procedure: 1=CABG;0=PTCA
sex: 1=Male; 0=female
admit: 1=Urgent/Emerg; 0=elective (type of admission)
age75: 1= Age>75; 0=Age<=75
hospital: encrypted facility code (string)

Details

azpro is saved as a data frame. Count models use los as response variable. 0 counts are structurally excluded

Source

1991 Arizona Medpar data, cardiovascular patient files, National Health Economics & Research Co.

References

Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC

Examples

data(azpro)
glmazp <- glm(los ~ procedure + sex + admit, family=poisson, data=azpro)
summary(glmazp)
exp(coef(glmazp))
#glmaznb < -glm.nb(los ~ procedure + sex + admit, data=azpro)
#summary(glmaznb)
#exp(coef(glmaznb))

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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