plot.perf {mixOmics} | R Documentation |
Function to plot performance criteria, such as MSEP, RMSEP, R^2, Q^2 for s/PLS methods, and classification performance for supervised methods, as a function of the number of components.
## S3 method for class 'perf.pls.mthd' plot( x, criterion = "MSEP", xlab = "number of components", ylab = NULL, LimQ2 = 0.0975, LimQ2.col = "darkgrey", cTicks = NULL, layout = NULL, ... ) ## S3 method for class 'perf.spls.mthd' plot( x, criterion = "MSEP", xlab = "number of components", ylab = NULL, LimQ2 = 0.0975, LimQ2.col = "darkgrey", cTicks = NULL, layout = NULL, ... ) ## S3 method for class 'perf.plsda.mthd' plot( x, dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"), measure = c("all", "overall", "BER"), col, xlab = NULL, ylab = NULL, overlay = c("all", "measure", "dist"), legend.position = c("vertical", "horizontal"), sd = TRUE, ... ) ## S3 method for class 'perf.splsda.mthd' plot( x, dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"), measure = c("all", "overall", "BER"), col, xlab = NULL, ylab = NULL, overlay = c("all", "measure", "dist"), legend.position = c("vertical", "horizontal"), sd = TRUE, ... ) ## S3 method for class 'perf.mint.plsda.mthd' plot( x, dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"), measure = c("all", "overall", "BER"), col, xlab = NULL, ylab = NULL, study = "global", overlay = c("all", "measure", "dist"), legend.position = c("vertical", "horizontal"), ... ) ## S3 method for class 'perf.mint.splsda.mthd' plot( x, dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"), measure = c("all", "overall", "BER"), col, xlab = NULL, ylab = NULL, study = "global", overlay = c("all", "measure", "dist"), legend.position = c("vertical", "horizontal"), ... ) ## S3 method for class 'perf.sgccda.mthd' plot( x, dist = c("all", "max.dist", "centroids.dist", "mahalanobis.dist"), measure = c("all", "overall", "BER"), col, weighted = TRUE, xlab = NULL, ylab = NULL, overlay = c("all", "measure", "dist"), legend.position = c("vertical", "horizontal"), sd = TRUE, ... )
x |
an |
criterion |
character string. What type of validation criterion to plot
for |
xlab, ylab |
titles for x and y axes. Typically character
strings, but can be expressions (e.g., |
LimQ2 |
numeric value. Signification limit for the components in the
model. Default is |
LimQ2.col |
character string specifying the color for the |
cTicks |
integer vector. Axis tickmark locations for the used number of
components. Default is |
layout |
numeric vector of length two giving the number of rows and
columns in a multi panel display. If not specified, |
... |
Further arguments sent to |
dist |
prediction method applied in |
measure |
Two misclassification measure are available: overall
misclassification error |
col |
character (or symbol) color to be used, possibly vector. One
color per distance |
overlay |
parameter to overlay graphs; if 'all', only one graph is shown with all outputs; if 'measure', a graph is shown per distance; if 'dist', a graph is shown per measure. |
legend.position |
position of the legend, one of "vertical" (only one column) or "horizontal" (two columns). |
sd |
If 'nrepeat' was used in the call to 'perf', error bar shows the standard deviation if sd=TRUE |
study |
Indicates which study-specific outputs to plot. A character
vector containing some levels of |
weighted |
plot either the performance of the Majority vote or the Weighted vote. |
plot.perf
creates one plot for each response variable in the model,
laid out in a multi panel display. It uses xyplot
for
performing the actual plotting.
More details about the prediction distances in ?predict
and the
supplemental material of the mixOmics article (Rohart et al. 2017).
none
Ignacio González, Florian Rohart, Francois Bartolo, Kim-Anh Lê Cao, Al J Abadi
Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752
pls
, spls
, plsda
,
splsda
, perf
.
require(lattice) ## validation for objects of class 'pls' or 'spls' data(liver.toxicity) X <- liver.toxicity$gene Y <- liver.toxicity$clinic liver.pls <- pls(X, Y, ncomp = 3) liver.perf <- perf(liver.pls, validation = "Mfold") plot(liver.perf, criterion = "R2", layout = c(2, 2)) ## Not run: ## validation for objects of class 'plsda' or 'splsda' data(breast.tumors) X <- breast.tumors$gene.exp # Y will be transformed as a factor in the function, # but we set it as a factor to set up the colors. Y <- as.factor(breast.tumors$sample$treatment) res <- splsda(X, Y, ncomp = 2, keepX = c(25, 25)) breast.perf <- perf(res, nrepeat = 5) plot(breast.perf) plot(breast.perf, col=1:3) plot(breast.perf, col=1:3, sd=FALSE) ## End(Not run)