pca is used to build and explore a principal component analysis (PCA) model.
pca(
x,
ncomp = min(nrow(x) - 1, ncol(x), 20),
center = TRUE,
scale = FALSE,
exclrows = NULL,
exclcols = NULL,
x.test = NULL,
method = "svd",
rand = NULL,
lim.type = "ddmoments",
alpha = 0.05,
gamma = 0.01,
info = ""
)Returns an object of pca class with following fields:
number of components included to the model.
selected (optimal) number of components.
matrix with loading values (nvar x ncomp).
vector with eigenvalues for all existent components.
vector with explained variance for each component (in percent).
vector with cumulative explained variance for each component (in percent).
statistical limit for T2 distance.
statistical limit for Q residuals.
information about the model, provided by user when build the model.
an object of class pcares with PCA results for a calibration data.
an object of class pcares with PCA results for a test data, if it
was provided.
More details and examples can be found in the Bookdown tutorial.
calibration data (matrix or data frame).
maximum number of components to calculate.
logical, do mean centering of data or not.
logical, do standardization of data or not.
rows to be excluded from calculations (numbers, names or vector with logical values)
columns to be excluded from calculations (numbers, names or vector with logical values)
test data (matrix or data frame).
method to compute principal components ("svd", "nipals").
vector with parameters for randomized PCA methods (if NULL, conventional PCA is used instead)
which method to use for calculation of critical limits for residual distances (see details)
significance level for extreme limits for T2 and Q disances.
significance level for outlier limits for T2 and Q distances.
a short text with model description.
Sergey Kucheryavskiy (svkucheryavski@gmail.com)
Note, that from v. 0.10.0 cross-validation is no more supported in PCA.
If number of components is not specified, a minimum of number of objects - 1 and number of
variables in calibration set is used. One can also specified an optimal number of component,
once model is calibrated (ncomp.selected). The optimal number of components is used to
build a residuals distance plot, as well as for SIMCA classification.
If some of rows of calibration set should be excluded from calculations (e.g. because they are
outliers) you can provide row numbers, names, or logical values as parameter exclrows. In
this case they will be completely ignored we model is calibrated. However, score and residuls
distances will be computed for these rows as well and then hidden. You can show them
on corresponding plots by using parameter show.excluded = TRUE.
It is also possible to exclude selected columns from calculations by provideing parameter
exclcols in form of column numbers, names or logical values. In this case loading matrix
will have zeros for these columns. This allows to compute PCA models for selected variables
without removing them physically from a dataset.
Take into account that if you see other packages to make plots (e.g. ggplot2) you will not be able to distinguish between hidden and normal objects.
By default loadings are computed for the original dataset using either SVD or NIPALS algorithm.
However, for datasets with large number of rows (e.g. hyperspectral images), there is a
possibility to run algorithms based on random permutations [1, 2]. In this case you have
to define parameter rand as a vector with two values: p - oversampling parameter
and k - number of iterations. Usually rand = c(15, 0) or rand = c(5, 1)
are good options, which give quite almost precise solution but much faster.
There are several ways to calculate critical limits for orthogonal (Q, q) and score (T2, h)
distances. In mdatools you can specify one of the following methods via parameter
lim.type: "jm" Jackson-Mudholkar approach [3], "chisq" - method based on
chi-square distribution [4], "ddmoments" and "ddrobust" - related to data
driven method proposed in [5]. The "ddmoments" is based on method of moments for
estimation of distribution parameters (also known as "classical" approach) while
"ddrobust" is based in robust estimation.
If lim.type="chisq" or lim.type="jm" is used, only limits for Q-distances are
computed based on corresponding approach, limits for T2-distances are computed using
Hotelling's T-squared distribution. The methods utilizing the data driven approach calculate
limits for combination of the distances bases on chi-square distribution and parameters
estimated from the calibration data.
The critical limits are calculated for a significance level defined by parameter 'alpha'.
You can also specify another parameter, 'gamma', which is used to calculate acceptance
limit for outliers (shown as dashed line on residual distance plot).
You can also recalculate the limits for existent model by using different values for alpha and
gamme, without recomputing the model itself. In this case use the following code (it is assumed
that you current PCA/SIMCA model is stored in variable m):
m = setDistanceLimits(m, lim.type, alpha, gamma).
In case of PCA the critical limits are just shown on residual plot as lines and can be used for
detection of extreme objects (solid line) and outliers (dashed line). When PCA model is used for
classification in SIMCA (see simca) the limits are also employed for
classification of objects.
1. N. Halko, P.G. Martinsson, J.A. Tropp. Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review, 53 (2010) pp. 217-288.
2. S. Kucheryavskiy, Blessing of randomness against the curse of dimensionality, Journal of Chemometrics, 32 (2018).
3. J.E. Jackson, A User's Guide to Principal Components, John Wiley & Sons, New York, NY (1991).
4. A.L. Pomerantsev, Acceptance areas for multivariate classification derived by projection methods, Journal of Chemometrics, 22 (2008) pp. 601-609.
5. A.L. Pomerantsev, O.Ye. Rodionova, Concept and role of extreme objects in PCA/SIMCA, Journal of Chemometrics, 28 (2014) pp. 429-438.
Methods for pca objects:
plot.pca | makes an overview of PCA model with four plots. |
summary.pca | shows some statistics for the model. |
categorize.pca | categorize data rows as "normal", "extreme" or "outliers". |
selectCompNum.pca | set number of optimal components in the model |
setDistanceLimits.pca | set critical limits for residuals |
predict.pca | applies PCA model to a new data. |
Plotting methods for pca objects:
plotScores.pca | shows scores plot. |
plotLoadings.pca | shows loadings plot. |
plotVariance.pca | shows explained variance plot. |
plotCumVariance.pca | shows cumulative explained variance plot. |
plotResiduals.pca | shows plot for residual distances (Q vs. T2). |
plotBiplot.pca | shows bi-plot. |
plotExtreme.pca | shows extreme plot. |
plotT2DoF | plot with degrees of freedom for score distance. |
plotQDoF | plot with degrees of freedom for orthogonal distance. |
plotDistDoF | plot with degrees of freedom for both distances. |
Most of the methods for plotting data are also available for PCA results (pcares)
objects. Also check pca.mvreplace, which replaces missing values in a data matrix
with approximated using iterative PCA decomposition.
library(mdatools)
### Examples for PCA class
## 1. Make PCA model for People data with autoscaling
data(people)
model = pca(people, scale = TRUE, info = "Simple PCA model")
model = selectCompNum(model, 4)
summary(model)
plot(model, show.labels = TRUE)
## 2. Show scores and loadings plots for the model
par(mfrow = c(2, 2))
plotScores(model, comp = c(1, 3), show.labels = TRUE)
plotScores(model, comp = 2, type = "h", show.labels = TRUE)
plotLoadings(model, comp = c(1, 3), show.labels = TRUE)
plotLoadings(model, comp = c(1, 2), type = "h", show.labels = TRUE)
par(mfrow = c(1, 1))
## 3. Show residual distance and variance plots for the model
par(mfrow = c(2, 2))
plotVariance(model, type = "h")
plotCumVariance(model, show.labels = TRUE, legend.position = "bottomright")
plotResiduals(model, show.labels = TRUE)
plotResiduals(model, ncomp = 2, show.labels = TRUE)
par(mfrow = c(1, 1))
Run the code above in your browser using DataLab