Description Usage Arguments Details See Also Examples

Produce a cumulative eigenvalue (CE) plot from a full or partial `PCADSC`

object,
as obtained from a call to `PCADSC`

. In either case, this `PCADSC`

object must have a
non-`NULL`

`CEInfo`

slot (see examples). The CE plot compares the eigenvalues obtained
from PCA performed separately and jointly on two datasets that consist of different observations
of the same variables.

1 |

`x` |
x A |

`nDraw` |
A positive integer. The number of simulated cumulative eigenvalue curves that should be added to the plot. |

In the x-coordinates, cumulative differences in eigenvalues are shown, while the y-coordinates are the cumulative sum of the joint eigenvalues. The plot is annotated with Kolmogorov-Smirnov and Cramer-von Mises tests evaluated by permutation tests, testing the null hypothesis of no difference in eigenvalues. The plot also features a number of cumulative simulated cumulative eigenvalue curves as dashed lines. Moreover, a shaded area presents pointwise 95 % confidence bands for the cumulative difference, also obtained using the permutation test.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
#load iris data
data(iris)
#Define grouping variable, grouping the observations by whether their species is
#Setosa or not
iris$group <- "setosa"
iris$group[iris$Species != "setosa"] <- "non-setosa"
iris$Species <- NULL
## Not run:
#make a PCADSC object, splitting the data by "group"
irisPCADSC <- PCADSC(iris, "group")
#make a partial PCADSC object from iris and fill out CEInfo in the next call
irisPCADSC2 <- PCADSC(iris, "group", doCE = FALSE)
irisPCADSC2 <- doCE(irisPCADSC2)
#make a CE plot
CEPlot(irisPCADSC)
CEPlot(irisPCADSC2)
## End(Not run)
#Only do CE information and use less resamplings for a faster runtime
irisPCADSC_fast <- PCADSC(iris, "group", doAngle = FALSE, doChroma = FALSE,
B = 1000)
CEPlot(irisPCADSC_fast)
``` |

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