Description Usage Arguments Details Value Author(s) See Also Examples
tune.pca
can be used to quickly visualise the proportion of explained
variance for a large number of principal components in PCA.
1 2 3 4 5 6 7 8 9 10 11 
X 
a numeric matrix (or data frame) which provides the data for the principal components analysis. It can contain missing values. 
ncomp 
integer, the number of components to initially analyse in

center 
a logical value indicating whether the variables should be
shifted to be zero centered. Alternately, a vector of length equal the
number of columns of 
scale 
a logical value indicating whether the variables should be
scaled to have unit variance before the analysis takes place. The default is

max.iter 
integer, the maximum number of iterations for the NIPALS algorithm. 
tol 
a positive real, the tolerance used for the NIPALS algorithm. 
logratio 
one of ('none','CLR','ILR'). Default to 'none' 
V 
Matrix used in the logratio transformation id provided. 
multilevel 
Design matrix for multilevel analysis (for repeated measurements). 
The calculation is done either by a singular value decomposition of the
(possibly centered and scaled) data matrix, if the data is complete or by
using the NIPALS algorithm if there is data missing. Unlike
princomp
, the print method for these objects prints the
results in a nice format and the plot
method produces a bar plot of
the percentage of variance explaned by the principal components (PCs).
When using NIPALS (missing values), we make the assumption that the first
(min(ncol(X),
nrow(X)
) principal components will account for
100 % of the explained variance.
Note that scale= TRUE
cannot be used if there are zero or constant
(for center = TRUE
) variables.
Components are omitted if their standard deviations are less than or equal
to comp.tol
times the standard deviation of the first component. With
the default null setting, no components are omitted. Other settings for
comp.tol
could be comp.tol = sqrt(.Machine$double.eps)
, which
would omit essentially constant components, or comp.tol = 0
.
logratio transform and multilevel analysis are performed sequentially as
internal preprocessing step, through logratio.transfo
and
withinVariation
respectively.
tune.pca
returns a list with class "tune.pca"
containing the following components:
sdev 
the square root of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix). 
explained_variance 
the proportion of explained variance accounted for by each principal component is calculated using the eigenvalues 
cum.var 
the cumulative proportion of explained variance accounted for by the sequential accumulation of principal components is calculated using the sum of the proportion of explained variance 
Ignacio González, Leigh Coonan, KimAnh Le Cao, Fangzhou Yao, Florian Rohart, AL J Abadi
nipals
, biplot
,
plotIndiv
, plotVar
and http://www.mixOmics.org
for more details.
1 2 3  data(liver.toxicity)
tune < tune.pca(liver.toxicity$gene, center = TRUE, scale = TRUE)
tune

Loading required package: MASS
Loading required package: lattice
Loading required package: ggplot2
Loaded mixOmics 6.2.0
Visit http://www.mixOmics.org for more details about our methods.
Any bug reports or comments? Notify us at mixomics at math.univtoulouse.fr or https://bitbucket.org/klecao/packagemixomics/issues
Thank you for using mixOmics!
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
Eigenvalues for the first 10 principal components, see object$sdev^2:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
874.77885 463.10098 248.15205 167.57834 141.95703 122.32390 108.76499 77.74595
PC9 PC10
74.18373 56.45566
Proportion of explained variance for the first 10 principal components, see object$explained_variance:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
0.28073776 0.14862034 0.07963801 0.05377996 0.04555745 0.03925671 0.03490533
PC8 PC9 PC10
0.02495056 0.02380736 0.01811799
Cumulative proportion explained variance for the first 10 principal components, see object$cum.var:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
0.2807378 0.4293581 0.5089961 0.5627761 0.6083335 0.6475902 0.6824956 0.7074461
PC9 PC10
0.7312535 0.7493715
Other available components:

loading vectors: see object$rotation
Call:
tune.pca(X = liver.toxicity$gene, center = TRUE, scale = TRUE)
for all principal components, see object$sdev, object$explained_variance and object$cum.var
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