R Dataset / Package HSAUR / respiratory

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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<iframe src="https://embed.picostat.com/r-dataset-package-hsaur-respiratory.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-50717.csv 9.53 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the respiratory data set which pertains to Respiratory Illness Data . The respiratory data set is found in the HSAUR R package. You can load the respiratory data set in R by issuing the following command at the console data("respiratory"). This will load the data into a variable called respiratory. If R says the respiratory data set is not found, you can try installing the package by issuing this command install.packages("HSAUR") and then attempt to reload the data. 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 respiratory R data set. The size of this file is about 9,759 bytes.

Documentation

Respiratory Illness Data

Description

The respiratory status of patients recruited for a randomised clinical multicenter trial.

Usage

data("respiratory")

Format

A data frame with 555 observations on the following 7 variables.

centre

the study center, a factor with levels 1 and 2.

treatment

the treatment arm, a factor with levels placebo and treatment.

sex

a factor with levels female and male.

age

the age of the patient.

status

the respiratory status (response variable), a factor with levels poor and good.

month

the month, each patient was examined at months 0, 1, 2, 3 and 4.

subject

the patient ID, a factor with levels 1 to 111.

Details

In each of two centres, eligible patients were randomly assigned to active treatment or placebo. During the treatment, the respiratory status (categorised poor or good) was determined at each of four, monthly visits. The trial recruited 111 participants (54 in the active group, 57 in the placebo group) and there were no missing data for either the responses or the covariates. The question of interest is to assess whether the treatment is effective and to estimate its effect.

Note that the data are in long form, i.e, repeated measurments are stored as additional rows in the data frame.

Source

C. S. Davis (1991), Semi-parametric and non-parametric methods for the analysis of repeated measurements with applications to clinical trials. Statistics in Medicine, 10, 1959–1980.

Examples

  data("respiratory", package = "HSAUR")
  mosaicplot(xtabs( ~ treatment + month + status, data = respiratory))
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Dataset imported from https://www.r-project.org.

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