linearrelation: Linear structure of predictors
In icmm: Empirical Bayes Variable Selection via ICM/M Algorithm
Description
Usage
Format
Details
Examples
Description
This data frame is used as an example to store the structure of predictors or the edge set of an undirected graph. For this data frame, the linear chain is assumed for each predictor.
Usage
1
Format
A data frame with 400 observations and 2 variables as follows.
Indexan index of the predictor/node which has at least one edge.
EdgeIndicesa string of all indices having an edge connected to Index separated by semicolon(;).
Details
This structure of predictors assumes a linear chain for each predictor which its immediate neighbors. For example, j-predictor is connected to (j-1)-predictor and (j+1)-predictor. The example for the entry in the data frame is Index="5" and EdgeIndices="4;6".
Examples
1
2
3
data(linearrelation)
# To see the format of linearrelation data frame
head(linearrelation)
icmm documentation built on May 26, 2021, 9:06 a.m.
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Description Usage Format Details Examples
Description
This data frame is used as an example to store the structure of predictors or the edge set of an undirected graph. For this data frame, the linear chain is assumed for each predictor.
Usage
1 |
Format
A data frame with 400 observations and 2 variables as follows.
Indexan index of the predictor/node which has at least one edge.
EdgeIndicesa string of all indices having an edge connected to
Indexseparated by semicolon(;).
Details
This structure of predictors assumes a linear chain for each predictor which its immediate neighbors. For example, j-predictor is connected to (j-1)-predictor and (j+1)-predictor. The example for the entry in the data frame is Index="5" and EdgeIndices="4;6".
Examples
1 2 3 | data(linearrelation)
# To see the format of linearrelation data frame
head(linearrelation)
|
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.
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