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run_fpca

Source: R/run_fpca.R
run_fpca.Rd

This is a wrapper for the function fpca.face from the refund package. EXPAND

Usage

run_fpca(
  mxFDAobject,
  metric = "uni k",
  r = "r",
  value = "fundiff",
  knots = NULL,
  analysis_vars = NULL,
  lightweight = FALSE,
  filter_cols = NULL,
  ...
)

Arguments

mxFDAobject

object of class mxFDA created by make_mxfda with metrics derived with extract_summary_functions

metric

name of calculated spatial metric to use

r

Character string, the name of the variable that identifies the function domain (usually a radius for spatial summary functions). Default is "r".

value

Character string, the name of the variable that identifies the spatial summary function values. Default is "fundiff".

knots

Number of knots for defining spline basis.Defaults to the number of measurements per function divided by 2.

analysis_vars

Optional list of variables to be retained for downstream analysis.

lightweight

Default is FALSE. If TRUE, removes Y and Yhat from returned FPCA object. A good option to select for large datasets.

filter_cols

a named vector of factors to filter summary functions to in c(Derived_Column = "Level_to_Filter") format

...

Optional other arguments to be passed to fpca.face

Value

A mxFDA object with the functional_pca slot filled for the respective spatial summary function containing:

mxfundata

The original dataframe of spatial summary functions, with scores from FPCA appended for downstream modeling

fpc_object

A list of class "fpca" with elements described in the documentation for refund::fpca.face

Details

[Stable]

The filter_cols parameter is useful when the summary function was input by the user using add_summary_function() and the multiple marks were assessed; a column called "Markers" with tumor infiltrating lymphocytes as well as cytotoxic T cells. This parameter allows for filtering down to include only one or the other.

References

Xiao, L., Ruppert, D., Zipunnikov, V., and Crainiceanu, C. (2016). Fast covariance estimation for high-dimensional functional data. Statistics and Computing, 26, 409-421. DOI: 10.1007/s11222-014-9485-x.

Author

Julia Wrobel julia.wrobel@emory.edu

Alex Soupir alex.soupir@moffitt.org

Examples

#load ovarian mxFDA object
data('ovarian_FDA')

#run the FPCA
ovarian_FDA = run_fpca(ovarian_FDA, metric = "uni g", r = "r", value = "fundiff",
                       lightweight = TRUE,
                       pve = .99)
#> 128 sample have >= 4 values for FPCA; removing 0 samples