# screeplot.MFPCAfit: Screeplot for Multivariate Functional Principal Component... In MFPCA: Multivariate Functional Principal Component Analysis for Data Observed on Different Dimensional Domains

## Description

This function plots the proportion of variance explained by the leading eigenvalues in an MFPCA against the number of the principal component.

## Usage

 ```1 2 3``` ```## S3 method for class 'MFPCAfit' screeplot(x, npcs = min(10, length(x\$values)), type = "lines", ylim = NULL, main = deparse(substitute(x)), ...) ```

## Arguments

 `x` An object of class MFPCAfit, typically returned by a call to `MFPCA`. `npcs` The number of eigenvalued to be plotted. Defaults to all eigenvalues if their number is less or equal to 10, otherwise show only the leading first 10 eigenvalues. `type` The type of screeplot to be plotted. Can be either `"lines"` or `"barplot"`. Defaults to `"lines"`. `ylim` The limits for the y axis. Can be passed either as a vector of length 2 or as `NULL` (default). In the second case, `ylim` is set to `(0,max(pve))`, with `pve` the proportion of variance explained by the principal components to be plotted. `main` The title of the plot. Defaults to the variable name of `x`. `...` Other graphic parameters passed to `plot.default` (for `type = "lines"`) or `barplot` (for `type = "barplot"`).

`MFPCA`, `screeplot`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```# 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"))) # screeplot screeplot(PCA) # default options screeplot(PCA, npcs = 3, type = "barplot", main= "Screeplot") ```