pcaFit {mvdalab} | R Documentation |
Principal Component Analysis
Description
Function to perform principal component analysis.
Usage
pcaFit(data, scale = TRUE, ncomp = NULL)
Arguments
data |
an data frame containing the variables in the model. |
scale |
should scaling to unit variance be used. |
ncomp |
the number of components to include in the model (see below). |
Details
The calculation is done via singular value decomposition of the data matrix. Dummy variables are automatically created for categorical variables.
Value
pcaFit
returns a list containing the following components:
loadings |
X loadings |
scores |
X scores |
D |
eigenvalues |
Xdata |
X matrix |
Percent.Explained |
Explained variation in X |
PRESS |
Prediction Error Sum-of-Squares |
ncomp |
number of latent variables |
method |
PLS algorithm used |
Author(s)
Nelson Lee Afanador (nelson.afanador@mvdalab.com)
References
Everitt, Brian S. (2005). An R and S-Plus Companion to Multivariate Analysis. Springer-Verlag.
Edoardo Saccentia, Jos? Camacho, (2015) On the use of the observation-wise k-fold operation in PCA cross-validation, J. Chemometrics 2015; 29: 467-478.
See Also
loadingsplot2D
, T2
, Xresids
, ScoreContrib
Examples
data(iris)
pc1 <- pcaFit(iris, scale = TRUE, ncomp = NULL)
pc1
print(pc1) #Model summary
plot(pc1) #MSEP
PE(pc1) #X-explained variance
T2(pc1, ncomp = 2) #T2 plot
Xresids(pc1, ncomp = 2) #X-residuals plot
scoresplot(pc1) #scoresplot variable importance
(SC <- ScoreContrib(pc1, obs1 = 1:9, obs2 = 10:11)) #score contribution
plot(SC) #score contribution plot
loadingsplot(pc1, ncomp = 1) #loadings plot
loadingsplot(pc1, ncomp = 1:2) #loadings plot
loadingsplot(pc1, ncomp = 1:3) #loadings plot
loadingsplot(pc1, ncomp = 1:7) #loadings plot
loadingsplot2D(pc1, comps = c(1, 2)) #2-D loadings plot
loadingsplot2D(pc1, comps = c(2, 3)) #2-D loadings plot