MECfda (version 0.2.1)

ME.fcLR_IV: 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
)

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.

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.

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)