# plot.MFPCAfit: Plot MFPCA results In MFPCA: Multivariate Functional Principal Component Analysis for Data Observed on Different Dimensional Domains

## Description

Plots the eigenfunctions as perturbations of the mean (i.e. the mean function plus/minus a constant factor times each eigenfunction separately). If all elements have a one-dimensional domain, the plots can be combined, otherwise the effects of adding and subtracting are shown in two separate rows for each eigenfunction.

## Usage

 ```1 2 3``` ```## S3 method for class 'MFPCAfit' plot(x, plotPCs = seq_len(nObs(x\$functions)), stretchFactor = NULL, combined = FALSE, ...) ```

## Arguments

 `x` An object of class `MFPCAfit`, typically returned by the MFPCA function. `plotPCs` The principal components to be plotted. Defaults to all components in the `MFPCAfit` object. `stretchFactor` The factor by which the principal components are multiplied before adding / subtracting them from the mean function. If `NULL` (the default), the median absolute value of the scores of each eigenfunction is used. `combined` Logical: Should the plots be combined? (Works only if all dimensions are one-dimensional). Defaults to `FALSE`. `...` Further graphical parameters passed to the plot.funData functions for functional data.

## Value

A plot of the principal components as perturbations of the mean.

`MFPCA`, `plot.funData`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# Simulate multivariate functional data on one-dimensonal domains # and calculate MFPCA (cf. MFPCA help) set.seed(1) # simulate data (one-dimensional domains) sim <- simMultiFunData(type = "split", argvals = list(seq(0,1,0.01), seq(-0.5,0.5,0.02)), M = 5, eFunType = "Poly", eValType = "linear", N = 100) # MFPCA based on univariate FPCA PCA <- MFPCA(sim\$simData, M = 5, uniExpansions = list(list(type = "uFPCA"), list(type = "uFPCA"))) # Plot the results plot(PCA, combined = TRUE) # combine addition and subtraction in one plot ```