plot,opls-method {ropls} | R Documentation |
This function plots values based upon a model trained by opls
.
## S4 method for signature 'opls' plot(x, y, typeVc = c("correlation", "outlier", "overview", "permutation", "predict-train", "predict-test", "summary", "x-loading", "x-score", "x-variance", "xy-score", "xy-weight")[7], parAsColFcVn = NA, parCexN = 0.8, parCompVi = c(1, 2), parEllipsesL = NA, parLabVc = NA, parPaletteVc = NA, parTitleL = TRUE, file.pdfC = NULL, .sinkC = NULL, parCexMetricN = NA, fig.pdfC = NA, info.txtC = NA, ...)
x |
An S4 object of class |
y |
Currently not used. |
typeVc |
Character vector: the following plots are available: 'correlation': Variable correlations with the components, 'outlier': Observation diagnostics (score and orthogonal distances), 'overview': Model overview showing R2Ycum and Q2cum (or 'Variance explained' for PCA), 'permutation': Scatterplot of R2Y and Q2Y actual and simulated models after random permutation of response values; 'predict-train' and 'predict-test': Predicted vs Actual Y for reference and test sets (only if Y has a single column), 'summary' [default]: 4-plot summary showing permutation, overview, outlier, and x-score together, 'x-variance': Spread of raw variables corresp. with min, median, and max variances, 'x-loading': X-loadings (the 6 of variables most contributing to loadings are colored in red to facilitate interpretation), 'x-score': X-Scores, 'xy-score': XY-Scores, 'xy-weight': XY-Weights |
parAsColFcVn |
Optional factor character or numeric vector to be converted into colors for the score plot; default is NA [ie colors will be converted from 'y' in case of (O)PLS(-DA) or will be 'black' for PCA] |
parCexN |
Numeric: amount by which plotting text should be magnified relative to the default |
parCompVi |
Integer vector of length 2: indices of the two components to be displayed on the score plot (first two components by default) |
parEllipsesL |
Should the Mahalanobis ellipses be drawn? If 'NA' [default], ellipses are drawn when either a character parAsColVcn is provided (PCA case), or when 'y' is a character factor ((O)PLS-DA cases). |
parLabVc |
Optional character vector for the labels of observations on the plot; default is NA [ie row names of 'x', if available, or indices of 'x', otherwise, will be used] |
parPaletteVc |
Optional character vector of colors to be used in the plots |
parTitleL |
Should the titles of the plots be printed on the graphics (default = TRUE); It may be convenient to set this argument to FALSE when the user wishes to add specific titles a posteriori |
file.pdfC |
Character: deprecated; use the 'fig.pdfC' argument instead |
.sinkC |
Character: deprecated; use the 'info.txtC' argument instead |
parCexMetricN |
Numeric: magnification of the metrics at the bottom of score plot (default -NA- is 1 in 1x1 and 0.7 in 2x2 display) |
fig.pdfC |
Figure filename (e.g. in case of batch mode) ending with '.pdf'; default is NA (no saving; displaying instead) |
info.txtC |
Character: Report filename for R output diversion [default = NA: no diversion] |
... |
Currently not used. |
Etienne Thevenot, etienne.thevenot@cea.fr
data(sacurine) attach(sacurine) for(typeC in c("correlation", "outlier", "overview", "permutation", "predict-train","predict-test", "summary", "x-loading", "x-score", "x-variance", "xy-score", "xy-weight")) { print(typeC) if(grepl("predict", typeC)) subset <- "odd" else subset <- NULL plsModel <- opls(dataMatrix, sampleMetadata[, "gender"], predI = ifelse(typeC != "xy-weight", 1, 2), orthoI = ifelse(typeC != "xy-weight", 1, 0), permI = ifelse(typeC == "permutation", 10, 0), subset = subset, info.txtC = NULL, fig.pdfC = NULL) plot(plsModel, typeVc = typeC) } sacPlsda <- opls(dataMatrix, sampleMetadata[, "gender"]) plot(sacPlsda, parPaletteVc = c("green4", "magenta")) detach(sacurine)