mxfda (version 0.2.2-1)

run_sofr: Run Scalar on Function Regression

Description

Fit a scalar-on-function regression model. Uses refund::pfr under the hood for computations, and stores results in the mxfda object.

Usage

run_sofr(
  mxFDAobject,
  model_name,
  formula,
  family = "gaussian",
  metric = "uni k",
  r = "r",
  value = "fundiff",
  smooth = FALSE,
  filter_cols = NULL,
  ...,
  knots = NULL
)

Value

A list which is a linear or additive functional Cox model fit. See mgcv::gam for more details.

Arguments

mxFDAobject

Dataframe of spatial summary functions from multiplex imaging data, in long format. Can be estimated using the function extract_summary_functions or provided separately.

model_name

character string to give the fit model

formula

Formula to be fed to mgcv in the form of outcome ~ x1 + x2. Does not contain functional predictor. Character valued.

family

Exponential family distribution to be passed to mgcv::gam. Defaults to "gaussian". Select "binomial" for binary outcome.

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".

smooth

Option to smooth data using FPCA. Defaults to FALSE.

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

knots

Number of knots for defining spline basis.

Author

Julia Wrobel

Alex Soupir

Details

Examples

#load ovarian mxFDA object
data('ovarian_FDA')

# run scalar on function regression model with a continuous outcome (age)
ovarian_FDA = run_sofr(ovarian_FDA,
                       model_name = "fit_sofr",
                       formula = age~stage,
                       metric = "uni g", r = "r", value = "fundiff")

# run scalar on function regression model with a binary outcome (stage)
# also known as functional logistic regression
ovarian_FDA = run_sofr(ovarian_FDA,
                       model_name = "fit_sofr",
                       formula = stage~age,
                       family = "binomial",
                       metric = "uni g", r = "r", value = "fundiff")