SCoRES (version 0.1.2)

cma: Construct CMA Confidence Intervals via Parametric Method

Description

This function computes Correlation and Multiplicity Adjusted (CMA) confidence bands for a specified group in a functional outcome regression model using parameter simulations approach with Gaussian multiplier bootstrap.

Usage

cma(
  data_df,
  object,
  fitted = TRUE,
  alpha = 0.05,
  outcome,
  domain,
  subset = NULL,
  id,
  nboot = NULL
)

Value

A list containing:

mu_hat

Estimated mean function for the group of interest.

domain

The domain used.

se_hat

Standard errors of the estimated means.

scb_low

Lower bound of the simultaneous confidence band.

scb_up

Upper bound of the simultaneous confidence band.

type

A character description of the output type.

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) 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.

alpha

Significance level for SCB. Default is 0.05.

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, representing the reference group.

id

A character string specifying the name of the ID variable.

nboot

An integer specifying the number of bootstrap samples used to construct the confidence bands. Default is 10,000.

References

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

Examples

# example using pupil data
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 <- cma(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 <- cma(pupil, mean_mod, fitted = TRUE, outcome = "percent_change",
               domain = "seconds", subset = c("use = 1"), id = "id", nboot = 100)
}