screeplot: Scree Plot
In pjpalla/RFmarkerDetector: Multivariate Analysis of Metabolomics Data using Random Forests
Description
Usage
Arguments
Details
Author(s)
Examples
Description
This function creates a graphical display of the variances against the number of principal components.
It is used to determine how many components should be retained in order to explain a high percentage of the variation in the data
Usage
1
screeplot(pca_obj, ncomp)
Arguments
pca_obj
an object of class pca
ncomp
the number of components to plot
Details
screplot generates 2 graphs that represent respectively the relative variances
and the cumulative variances associated with the principal components
Author(s)
Piergiorgio Palla
Examples
1
2
3
## data(cachexiaData)
## pca_obj <- pca(cachexiaData, autoscale = TRUE, exclude = TRUE)
## screeplot(pca_obj, ncomp = 10)
pjpalla/RFmarkerDetector documentation built on May 25, 2019, 8:19 a.m.
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Description Usage Arguments Details Author(s) Examples
Description
This function creates a graphical display of the variances against the number of principal components. It is used to determine how many components should be retained in order to explain a high percentage of the variation in the data
Usage
1 | screeplot(pca_obj, ncomp)
|
Arguments
pca_obj |
an object of class pca |
ncomp |
the number of components to plot |
Details
screplot generates 2 graphs that represent respectively the relative variances and the cumulative variances associated with the principal components
Author(s)
Piergiorgio Palla
Examples
1 2 3 | ## data(cachexiaData)
## pca_obj <- pca(cachexiaData, autoscale = TRUE, exclude = TRUE)
## screeplot(pca_obj, ncomp = 10)
|
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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