Displays a correlation circle of a multivariate analysis.
MVA.corplot(x, xax = 1, yax = 2, thresh = 0, fac = NULL, set = c(12, 1, 2), space = 1,
xlab = NULL, ylab = NULL, main = NULL, circle = TRUE, intcircle = 0.5, points = TRUE,
ident = TRUE, arrows = TRUE, labels = NULL, main.pos = c("bottomleft", "topleft",
"bottomright", "topright"), main.cex = 1.3, legend = FALSE, legend.pos = c("topleft",
"topright", "bottomleft", "bottomright"), legend.title = NULL, legend.lab = NULL,
pch = 16, cex = 1, col = 1, lwd = 1, drawintaxes = TRUE, add = FALSE, add.const = 1,
keepmar = FALSE)
a multivariate analysis (see Details).
the horizontal axis.
the vertical axis. This can be set to NULL for a one-dimensional graph, which is a dotchart.
threshold (in absolute value of the correlation coefficient) of variables to be plotted.
an optional factor defining groups of variables.
variables to be displayed, when several sets are available (see Details). 12 (default) for both sets, 1 for X or constraints, 2 for Y or constrained variables.
variables to be displayed, when several spaces are available (see Details). space is the number of the space to be plotted.
legend of the horizontal axis. If NULL (default), automatic labels are used depending on the multivariate analysis.
only used for two-dimensional graphs. Legend of the vertical axis. If NULL (default), automatic labels are used depending on the multivariate analysis.
optional title of the graph.
only used for two-dimensional graphs. Logical indicating if the circle of radius 1 should be plotted.
only used for two-dimensional graphs. Vector of one or several values indicating radii of circles to be plotted inside the main circle. Can be set to NULL.
only used for two-dimensional graphs. If FALSE, arrows or points (see arrows) are replaced with their corresponding label (defined by labels).
only used for two-dimensional graphs when points=TRUE. A logical indicating if variable names should be displayed.
only used if points=TRUE. Logical indicating if arrows should be plotted. If FALSE, points are displayed at the extremity of the arrows.
names of the variables. If NULL (default), labels correspond to variable names found in the data used in the multivariate analysis. For two-dimensional graphs, only used if ident=TRUE.
position of the title, if main is not NULL. Default to "bottomleft".
size of the title, if main is not NULL.
only used for two-dimensional graphs. Logical indicating if a legend should be added to the graph.
position of the legend, if legend is TRUE. Default to "topleft".
optional title of the legend, if legend is TRUE.
legend labels, if legend is TRUE. If NULL, levels of the factor defined by fac are used.
symbol(s) used for points, when points are displayed (see arrows). If fac is not NULL, can be a vector of length one or a vector giving one value per group. Otherwise a vector of any length can be defined, which is recycled if necessary.
size of the points and/or of the variable names. For two-dimensional graphs: if fac is not NULL, can be a vector of length one or a vector giving one value per group; otherwise a vector of any length can be defined, which is recycled if necessary. For dotcharts, gives the size used for points and all labels (see dotchart).
color(s) used for points and/or variable names. If fac is not NULL, can be a vector of length one or a vector giving one value per group. Otherwise a vector of any length can be defined, which is recycled if necessary (not available for density histograms, see dhist).
only used if arrows are displayed. Width of arrows. If fac is not NULL, can be a vector of length one or a vector giving one value per group. Otherwise a vector of any length can be defined, which is recycled if necessary.
logical indicating if internal axes should be drawn.
only used for two-dimensional graphs. Logical indicating if the correlation circle should be added to an existing graph.
only used for two-dimensional graphs and if add is TRUE. Constant by which correlations are multiplied to fit onto the original graph.
only used for two-dimensional graphs. Logical indicating if margins defined by MVA.corplot should be kept after plotting (necessary in some cases when add=TRUE).
Maxime HERVE <maxime.herve@univ-rennes1.fr>
This function should not be use directly. Prefer the general MVA.plot, to which all arguments can be passed.
Many multivariate analyses are supported, from various packages:
- PCA: dudi.pca, rda.
- sPCA: spca.
- IPCA: ipca.
- sIPCA: sipca.
- LDA: lda, discrimin.
- PLS-DA (PLS2 on a dummy-coded factor): plsda. X space only.
- sPLS-DA (sPLS2 on a dummy-coded factor): splsda. X space only.
- CPPLS: mvr. Set 1 is X, set 2 is Y. If set=12 (default), fac is not available and pch,cex, col, lwd can be defined differently for each set. X space only.
- PLSR: mvr, pls, plsR (plsRglm package). Set 1 is X, set 2 is Y. If set=12 (default), fac is not available and pch,cex, col, lwd can be defined differently for each set. X space only.
- sPLSR: pls. Set 1 is X, set 2 is Y. If set=12 (default), fac is not available and pch,cex, col, lwd can be defined differently for each set. X space only.
- PLS-GLR: plsRglm (plsRglm package). Set 1 is X, set 2 is Y. If set=12 (default), fac is not available and pch,cex, col, lwd can be defined differently for each set. Correlations are computed with Y on the link scale.
- PCR: mvr. Set 1 is X, set 2 is Y. If set=12 (default), fac is not available and pch,cex, col, lwd can be defined differently for each set.
- CDA: discrimin, discrimin.coa.
- NSCOA: dudi.nsc. For NSCOA there is no real correlation, but the classical representation of columns is arrows. This is why MVA.corplot was made able to deal with this analysis.
- CCA: cca, pcaiv. Constraints (only quantitative constraints are extracted) in constrained space only.
- Mix analysis: dudi.mix, dudi.hillsmith. Only quantitative variables are displayed.
- RDA (or PCAIV): pcaiv, pcaivortho, rda. With rda, space 1 is constrained space, space 2 is unconstrained space. Only constrained space is available with pcaiv, the opposite for pcaivortho. Set 1 is constraints (only quantitative constraints are extracted), set 2 is dependent variables (only set 2 is available for pcaivortho). If set=12 (default), fac is not available and pch,cex, col, lwd can be defined differently for each set.
- db-RDA: capscale, dbrda. Constraints (only quantitative constraints are extracted) in constrained space only.
- CCorA: CCorA, rcc. Space 1 is X, space 2 is Y. With rcc a third space is available, in which coordinates are means of X and Y coordinates. In this third space, set 1 is X, set 2 is Y. If set=12 (default), fac is not available and pch,cex, col, lwd can be defined differently for each set.
- rCCorA: rcc. Space 1 is X, space 2 is Y, space 3 is a "common" space in which coordinates are means of X and Y coordinates. In space 3, set 1 is X and set 2 is Y. If set=12 (default), fac is not available and pch,cex, col, lwd can be defined differently for each set.
- CIA: coinertia. Space 1 is X, space 2 is Y, space 3 is a "common" space where X and Y scores are normed. In space 3, set 1 is X and set 2 is Y. If set=12 in space 3 (default), fac is not available and pch,cex, col, lws can be defined differently for each set.
- PCIA: procuste. Set 1 is X, set 2 is Y.
- 2B-PLS: pls. Space 1 is X, space 2 is Y, space 3 is a "common" space in which coordinates are means of X and Y coordinates. In space 3, set 1 is X and set 2 is Y. If set=12 (default), fac is not available and pch,cex, col, lwd can be defined differently for each set.
- 2B-sPLS: pls. Space 1 is X, space 2 is Y, space 3 is a "common" space in which coordinates are means of X and Y coordinates. In space 3, set 1 is X and set 2 is Y. If set=12 (default), fac is not available and pch,cex, col, lwd can be defined differently for each set.
- rGCCA: wrapper.rgcca. Space can be 1 to n, the number of blocks (i.e. datasets).
- sGCCA: wrapper.sgcca. Space can be 1 to n, the number of blocks (i.e. datasets).
- DIABLO: block.plsda, block.splsda. Space can be 1 to n, the number of blocks (i.e. datasets).
require(ade4)
data(olympic)
PCA <- dudi.pca(olympic$tab,scannf=FALSE)
MVA.plot(PCA,"corr")
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