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Functional Outcome Regression Prediction with Group-Specific Inference

Source: R/cma.R
mean_response_predict.Rd

This function is an internal function for constructing SCBs for functional data.

Usage

mean_response_predict(
  data_df,
  object,
  fitted = TRUE,
  outcome,
  domain,
  subset = NULL,
  id
)

Arguments

data_df

A functional data frame that contains both name and values for variables including functional outcome, domain (e.g. time) and ID (e.g. subject names) used to fit the model object.

object

A fitted Function-on-Scalar Regression (FoSR) model object (e.g., from mgcv::gam()/mgcv::bam()).

fitted

Logical. Whether to estimate the simultaneous confidence bands for the fitted mean function or the fitted parameter function

  • TRUE - Estimate the simultaneous confidence bands for the fitted mean outcome function.

  • FALSE - estimate the simultaneous confidence bands for the fitted parameter function.

Default is TRUE.

outcome

A character string specifying the name of the outcome variable used in the model.

domain

A character string specifying the name of the domain variable (e.g. time) used in the model.

subset

An atomic character vector (e.g., c("user = 1", "age = 30")) specifying the target function for constructing the SCB. Each element must be of the form <name> = <value>, where <name> is the name of a scalar grouping variable and <value> is the desired value. Whitespace is ignored. Binary or categorical character variables should be transformed into numeric. Factors are not allowed here because if the input data contains factor variables, they will be automatically expanded into dummy (indicator) variables when constructing the design matrix, and the resulting variable names may differ from the original factor names. Default is NULL, which represents the reference group.

id

A character string specifying the name of the ID variable.

Value

A list containing the following elements:

s_pred

Numeric vector of sorted unique domain used for prediction

pred_df

Data frame with prediction results, containing:

  • mean: Predicted mean values

  • se: Standard errors

lpmat

Linear predictor matrix (design matrix) used for confidence interval calculations

mod_coef

Vector of model coefficients for selected group

mod_cov

Variance-covariance matrix corresponding to the selected group coefficients

References

Crainiceanu, C. M., Goldsmith, J., Leroux, A., & Cui, E. (2024). Functional Data Analysis with R. Chapman and Hall/CRC.

Examples

if (requireNamespace("mgcv", quietly = TRUE)) {
data(pupil)

# \donttest{
pupil_fpca <- prepare_pupil_fpca(pupil)

fosr_mod <- mgcv::bam(percent_change ~ s(seconds, k=30, bs="cr") +
  s(seconds, by = use, k=30, bs = "cr") +
  s(id, by = Phi1, bs="re") +
  s(id, by = Phi2, bs="re") +
  s(id, by = Phi3, bs="re") +
  s(id, by = Phi4, bs="re"),
  method = "fREML", data = pupil_fpca, discrete = TRUE)

results <- mean_response_predict(pupil_fpca, fosr_mod, fitted = TRUE,
outcome = "percent_change", domain = "seconds", subset = c("use = 1"), id = "id")# }

mean_mod <- mgcv::gam(percent_change ~ s(seconds, k = 5, bs = "cr") +
s(seconds, by = use, k = 5, bs = "cr"),
data = pupil, method = "REML")

results <- mean_response_predict(pupil, mean_mod, fitted = TRUE,
outcome = "percent_change", domain = "seconds", subset = c("use = 1"), id = "id")
}