PVA: A Function to extract principle variables of a data set
In hughesevoanth/moosefun: Functions by Moose
PVA R Documentation
A Function to extract principle variables of a data set
Description
This function estimates the correlation structure among two factors, using a chi-square test and the Crammer's V statsitic
Usage
PVA(responses, data, nvarselect = NULL, p.variance = 1, include = NULL, ...)
Arguments
responses
a vector of column names to run PVA on
data
a matrix of data, variables are in columns
nvarselect
the number of variables to return
p.variance
the proportion of variance you want you principal variable to explain
include
a matrix of data, variables are in columns
Examples
PVA()
hughesevoanth/moosefun documentation built on Aug. 22, 2022, 7:04 a.m.
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| PVA | R Documentation |
A Function to extract principle variables of a data set
Description
This function estimates the correlation structure among two factors, using a chi-square test and the Crammer's V statsitic
Usage
PVA(responses, data, nvarselect = NULL, p.variance = 1, include = NULL, ...)
Arguments
responses |
a vector of column names to run PVA on |
data |
a matrix of data, variables are in columns |
nvarselect |
the number of variables to return |
p.variance |
the proportion of variance you want you principal variable to explain |
include |
a matrix of data, variables are in columns |
Examples
PVA()
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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