# splineFunction1D: Calculate linear combinations of spline basis functions on... In MFPCA: Multivariate Functional Principal Component Analysis for Data Observed on Different Dimensional Domains

## Description

Given scores (coefficients), this function calculates a linear combination of spline basis functions on one-dimensional domains based on the gam function in the mgcv package.

## Usage

 `1` ```splineFunction1D(scores, argvals, bs, m, k) ```

## Arguments

 `scores` A matrix of dimension `N x K`, representing the `K` scores (coefficients) for each of the `N` observations. `argvals` A list containing a vector of x-values, on which the functions should be defined. `bs` A character string, specifying the type of basis functions to be used. Please refer to `smooth.terms` for a list of possible basis functions. `m` A numeric, the order of the spline basis. See `s` for details. `k` A numeric, the number of basis functions used. See `s` for details.

## Value

An object of class `funData` with `N` observations on `argvals`, corresponding to the linear combination of spline basis functions.

`univExpansion`, `gam`, `splineBasis1D`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ``` set.seed(1234) # simulate coefficients (scores) for 10 observations and 8 basis functions N <- 10 K <- 8 scores <- t(replicate(n = N, rnorm(K, sd = (K:1)/K))) dim(scores) # expand spline basis on [0,1] funs <- MFPCA:::splineFunction1D(scores = scores, argvals = list(seq(0,1,0.01)), bs = "ps", m = 2, k = K) # params for mgcv oldPar <- par(no.readonly = TRUE) par(mfrow = c(1,1)) plot(funs, main = "Spline reconstruction") par(oldPar) ```