ME.fcLR_IV: Bias correction method of applying linear regression to one...

In MECfda: Scalar-on-Function Regression with Measurement Error Correction

Bias correction method of applying linear regression to one functional covariate with measurement error using instrumental variable.

Description

See detailed model in reference

Usage

ME.fcLR_IV(
  data.Y,
  data.W,
  data.M,
  t_interval = c(0, 1),
  t_points = NULL,
  CI.bootstrap = FALSE
)

Arguments

data.Y	Response variable, can be an atomic vector, a one-column matrix or data frame, recommended form is a one-column data frame with column name.
data.W	A dataframe or matrix, represents W, the measurement of X. Each row represents a subject. Each column represent a measurement (time) point.
data.M	A dataframe or matrix, represents M, the instrumental variable. Each row represents a subject. Each column represent a measurement (time) point.
t_interval	A 2-element vector, represents an interval, means the domain of the functional covariate. Default is c(0,1), represent interval [0,1].
t_points	Sequence of the measurement (time) points, default is NULL.
CI.bootstrap	Whether to return the confidence using bootstrap method. Default is FALSE.

Value

Returns a ME.fcLR_IV class object. It is a list that contains the following elements.

beta_tW	Parameter estimates.
CI	Confidence interval, returnd only when CI.bootstrap is TRUE.

References

Tekwe, Carmen D., et al. "Instrumental variable approach to estimating the scalar‐on‐function regression model w ith measurement error with application to energy expenditure assessment in childhood obesity." Statistics in medicine 38.20 (2019): 3764-3781.

Examples

data(MECfda.data.sim.0.3)
res = ME.fcLR_IV(data.Y = MECfda.data.sim.0.3$Y,
              data.W = MECfda.data.sim.0.3$W,
              data.M = MECfda.data.sim.0.3$M)

MECfda documentation built on April 3, 2025, 10:07 p.m.