R Dataset / Package datasets / infert

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<iframe src="https://embed.picostat.com/r-dataset-package-datasets-infert.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-65073.csv 6.56 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 infert data set which pertains to Infertility after Spontaneous and Induced Abortion. The infert data set is found in the datasets R package. You can load the infert data set in R by issuing the following command at the console data("infert"). This will load the data into a variable called infert. If R says the infert data set is not found, you can try installing the package by issuing this command install.packages("datasets") 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 infert R data set. The size of this file is about 6,715 bytes.

Documentation

Infertility after Spontaneous and Induced Abortion

Description

This is a matched case-control study dating from before the availability of conditional logistic regression.

Usage

infert

Format

1. Education 0 = 0-5 years
1 = 6-11 years
2 = 12+ years
2. age age in years of case
3. parity count
4. number of prior 0 = 0
induced abortions 1 = 1
2 = 2 or more
5. case status 1 = case
0 = control
6. number of prior 0 = 0
spontaneous abortions 1 = 1
2 = 2 or more
7. matched set number 1-83
8. stratum number 1-63

Note

One case with two prior spontaneous abortions and two prior induced abortions is omitted.

Source

Trichopoulos et al (1976) Br. J. of Obst. and Gynaec. 83, 645–650.

Examples

require(stats)
model1 <- glm(case ~ spontaneous+induced, data = infert, family = binomial())
summary(model1)
## adjusted for other potential confounders:
summary(model2 <- glm(case ~ age+parity+education+spontaneous+induced,
                     data = infert, family = binomial()))
## Really should be analysed by conditional logistic regression
## which is in the survival package
if(require(survival)){
  model3 <- clogit(case ~ spontaneous+induced+strata(stratum), data = infert)
  print(summary(model3))
  detach()  # survival (conflicts)
}
--

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

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