endometrial: Histology grade and risk factors for 79 cases of endometrial...
In enrichwith: Methods to Enrich R Objects with Extra Components
Description
Usage
Format
Source
Description
Histology grade and risk factors for 79 cases of endometrial cancer
Usage
1
Format
A data frame with 79 rows and 4 variables:
- NV
neovasculization with coding 0 for absent and 1 for present
- PI
pulsality index of arteria uterina
- EH
endometrium heigh
- HG
histology grade with coding 0 for low grade and 1 for high grade
Source
The packaged data set was downloaded in .dat format
from http://www.stat.ufl.edu/~aa/glm/data. The latter
link provides the data sets used in Agresti (2015).
The endometrial data set was first analysed in Heinze and
Schemper (2002), and was originally provided by Dr
E. Asseryanis from the Medical University of Vienna.
Agresti, A. 2015. *Foundations of Linear and Generalized Linear
Models*. Wiley Series in Probability and Statistics. Wiley.
Heinze, G., and M. Schemper. 2002. <e2><80><9c>A Solution to the Problem of
Separation in Logistic Regression.<e2><80><9d> *Statistics in Medicine*
21:2409<e2><80><93>19.
enrichwith documentation built on Jan. 11, 2020, 9:21 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Format Source
Description
Histology grade and risk factors for 79 cases of endometrial cancer
Usage
1 |
Format
A data frame with 79 rows and 4 variables:
- NV
neovasculization with coding 0 for absent and 1 for present
- PI
pulsality index of arteria uterina
- EH
endometrium heigh
- HG
histology grade with coding 0 for low grade and 1 for high grade
Source
The packaged data set was downloaded in .dat format
from http://www.stat.ufl.edu/~aa/glm/data. The latter
link provides the data sets used in Agresti (2015).
The endometrial data set was first analysed in Heinze and Schemper (2002), and was originally provided by Dr E. Asseryanis from the Medical University of Vienna.
Agresti, A. 2015. *Foundations of Linear and Generalized Linear Models*. Wiley Series in Probability and Statistics. Wiley.
Heinze, G., and M. Schemper. 2002. <e2><80><9c>A Solution to the Problem of Separation in Logistic Regression.<e2><80><9d> *Statistics in Medicine* 21:2409<e2><80><93>19.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.