scoreplot.MFPCAfit function

Plot the Scores of a Multivariate Functional Principal Component Analysis

This function plots two scores of a multivariate functional principal component analysis for each observation.

## S3 method for class 'MFPCAfit'
scoreplot(PCAobject, choices = 1:2, scale = FALSE, ...)

Arguments

• PCAobject: An object of class MFPCAfit, typically returned by the MFPCA function.
• choices: The indices of the scores that should by displayed. Defaults to 1:2, i.e. the scores corresponding to the two leading modes of variability in the data.
• scale: Logical. Should the scores be scaled by the estimated eigenvalues to emphasize the proportions of total variance explained by the components. Defaults to FALSE.
• ...: Further parameters passed to the plot.default function.

Returns

A bivariate plot of scores.

Examples

# 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 first two scores
scoreplot(PCA) # no scaling (default)
scoreplot(PCA, scale = TRUE) # scale the scores by the first two eigenvalues

See Also

MFPCA