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Måge plot

Source: R/maage.R
maage.Rd

Måge plot for SO-PLS (sopls) cross-validation visualisation.

Usage

maage(
  object,
  expl_var = TRUE,
  pure.trace = FALSE,
  pch = 20,
  xlab = "# components",
  ylab = ifelse(expl_var, "Explained variance (%)", "RMSECV"),
  xlim = NULL,
  ylim = NULL,
  cex.text = 0.8,
  ...
)

maageSeq(
  object,
  compSeq = TRUE,
  expl_var = TRUE,
  pch = 20,
  xlab = "# components",
  ylab = ifelse(expl_var, "Explained variance (%)", "RMSECV"),
  xlim = NULL,
  ylim = NULL,
  cex.text = 0.8,
  col = "gray",
  col.block = c("red", "blue", "darkgreen", "purple", "black", "red", "blue",
    "darkgreen"),
  ...
)

Arguments

object

An SO-PLS model (sopls object)

expl_var

Logical indicating if explained variance (default) or RMSECV should be displayed.

pure.trace

Logical indicating if single block solutions should be traced in the plot.

pch

Scalar or symbol giving plot symbol.

xlab

Label for x-axis.

ylab

Label for y-axis.

xlim

Plot limits for x-axis (numeric vector).

ylim

Plot limits for y-axis (numeric vector).

cex.text

Text scaling (scalar) for better readability of plots.

...

Additional arguments to plot.

compSeq

Integer vector giving the sequence of previous components chosen for maageSeq (see example).

col

Line colour in plot.

col.block

Line colours for blocks (default = c('red','blue','darkgreen','purple','black'))

Value

The maage plot has no return.

Details

This function can either be used for global optimisation across blocks or sequential optimisation, using maageSeq. The examples below show typical usage.

See also

Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.

Examples

data(wine)
ncomp <- unlist(lapply(wine, ncol))[-5]
so.wine <- sopls(`Global quality` ~ ., data=wine, ncomp=ncomp, 
            max_comps=10, validation="CV", segments=10)
maage(so.wine)


# Sequential search for optimal number of components per block
old.par <- par(mfrow=c(2,2), mar=c(3,3,0.5,1), mgp=c(2,0.7,0))
maageSeq(so.wine)
maageSeq(so.wine, 2)
maageSeq(so.wine, c(2,1))
maageSeq(so.wine, c(2,1,1))

par(old.par)