pca: principal component analysis
In Swarchal/phenoScreen: Useful Functions For Multivariate Chemical Screening Assays
Description
replace feature columns with principal components
Usage
1
Arguments
data
dataframe
metadata_prefix
string, prefix of metadata columns
n_components
numeric, If an integer greater than or equal to 1, then
this many principal components will be returned.
If a floating point number between 0 and 1, then the number of principal
components will be how many needed to account for that proportion of
variance in the data.
If left blank then will return all principal components which is
equal to the number of feature columns.
...
additional arguments to prcomp
Swarchal/phenoScreen documentation built on May 9, 2019, 3:26 p.m.
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Description
replace feature columns with principal components
Usage
1 |
Arguments
data |
dataframe |
metadata_prefix |
string, prefix of metadata columns |
n_components |
numeric, If an integer greater than or equal to 1, then this many principal components will be returned. If a floating point number between 0 and 1, then the number of principal components will be how many needed to account for that proportion of variance in the data. If left blank then will return all principal components which is equal to the number of feature columns. |
... |
additional arguments to |
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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