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Cross validation of degrees of freedom

Source: R/fqpca.R
fqpca_cv_df.Rd

Performs cross validation on degrees of freedom parameter of fqpca

Usage

fqpca_cv_df(
  data,
  colname = NULL,
  npc = 2,
  pve = NULL,
  quantile.value = 0.5,
  lambda.ridge = 0,
  n.folds = 3,
  return.models = TRUE,
  criteria = "points",
  periodic = TRUE,
  splines.df.grid = c(5, 10, 15, 20),
  tol = 0.001,
  max.iters = 20,
  splines.method = "conquer",
  penalized = FALSE,
  verbose.fqpca = FALSE,
  verbose.cv = TRUE,
  seed = NULL
)

Arguments

data

An \((N \times T)\) matrix, a tf object from the tidyfun package or a data.frame containing the functional data as a tf column.

colname

The name of the column containing the functional data. Use only if data is a dataframe and colname is a column in the dataframe.

npc

The number of estimated components.

pve

Float between 0 and 1. Percentage of variability explained by components. This affects the number of components used in the curve reconstruction and error estimation. Set to NULL to avoid this behavior.

quantile.value

The quantile considered.

lambda.ridge

Hyper parameter controlling the penalization on the second derivative of the splines. It has effect only with penalized=TRUE and method='conquer'.

n.folds

Number of folds to be used on cross validation.

return.models

Should the list of all the models built be returned?

criteria

Criteria used to divide the data. Valid values are 'rows', which considers the division based on full rows, or 'points', which considers the division based on points within the matrix.

periodic

Boolean indicating if the data is expected to be periodic (start coincides with end) or not.

splines.df.grid

Grid of possible values for the degrees of freedom.

tol

Tolerance on the convergence of the algorithm.

max.iters

Maximum number of iterations.

splines.method

Method used in the resolution of the splines quantile regression model. It currently accepts the methods c('conquer', 'quantreg').

penalized

Boolean indicating if the smoothness should be controlled using a second derivative penalty. This functionality is experimental.

verbose.fqpca

Boolean indicating verbosity of the fqpca function.

verbose.cv

Boolean indicating verbosity of the cross-validation process.

seed

Seed for the random generator number.

Value

A list containing the matrix of scores, the matrix of loadings, and a secondary list with extra information.

Examples

n.obs = 150
n.time = 144

# Generate scores
c1.vals = rnorm(n.obs)
c2.vals = rnorm(n.obs)

# Generate pc's
pc1 = sin(seq(0, 2*pi, length.out = 144))
pc2 = cos(seq(0, 2*pi, length.out = 144))

# Generate data
Y <- c1.vals * matrix(pc1, nrow = n.obs, ncol=n.time, byrow = TRUE) +
c2.vals * matrix(pc2, nrow = n.obs, ncol=n.time, byrow = TRUE)

# Add noise
Y <- Y + matrix(rnorm(n.obs * n.time, 0, 0.4), nrow = n.obs)

# Add missing observations
Y[sample(n.obs*n.time, as.integer(0.2*n.obs*n.time))] <- NA

cv_result <- fqpca_cv_df(data=Y, splines.df.grid = c(5, 10, 15), n.folds = 2)
#> Degrees of freedom: 5 ---------------------
#> 2026-07-16 19:48:47. Fold: 1
#> 2026-07-16 19:48:47. Fold: 2
#> Degrees of freedom: 5. Execution completed in: 0.397 seconds.
#> Degrees of freedom: 10 ---------------------
#> 2026-07-16 19:48:47. Fold: 1
#> 2026-07-16 19:48:48. Fold: 2
#> Degrees of freedom: 10. Execution completed in: 0.383 seconds.
#> Degrees of freedom: 15 ---------------------
#> 2026-07-16 19:48:48. Fold: 1
#> 2026-07-16 19:48:48. Fold: 2
#> Degrees of freedom: 15. Execution completed in: 0.805 seconds.