longitudinalANAL (version 0.2)

lda: Longitudinal data analysis

Description

This function provide regression analysis of mixed sparse synchronous and asynchronous longitudinal covariates.

Usage

lda(data_res, data_cov, N, bd, omit, method)

Value

a list with the following elements:

est

The estimation for the corresponding parameters.

se

The estimation of standard error for the estimated parameters.

Arguments

data_res

An object of class tibble. The structure of the tibble must be: tibble(id_y=ID, ty=measurement time for response, y=observation for response, x=matrix(observation for synchronous covariates), x_add=matrix(observation for uninterested synchronous covariates)).

data_cov

An object of class tibble. The structure of the tibble must be: tibble(id_z=ID, tz=measurement time for response, z=matrix(observation for asynchronous covariates)).

N

An object of class integer. The sample size.

bd

An object of class vector. If use auto bandwidth selection, the structure of the vector must be: d=c(the maximum bandwidth, the minimum bandwidth, the fold of cross-validation, the number of bandwidth divided). If use fixed bandwidth, bd=c(the chosen bandwidth).

omit

An object of class integer indicating the method used to do estimation for synchronous covariates. If use plm method, omit=1; if use centering method, omit=2; if use additional covariates information, omit=3.

method

An object of class integer indicating the method used to do estimation for asynchronous covariates. If only deal with omit variable, method=0; if use two-stage method, method=1; if use kernel smoothing, method=2.

Examples

library(MASS)
library(tibble)
library(dplyr)
N=100
ty=tz=y=x=z=id_y=id_z=list()
a=b=g=1
ny=rpois(N,5)+1
nz=rpois(N,5)+1
for(i in 1:N){
  ty[[i]]=as.matrix(runif(ny[i]))
  tz[[i]]=as.matrix(runif(nz[i]))
  t.temp=rbind(tz[[i]],ty[[i]])
  n.temp=nz[i]+ny[i]
  corr=exp(-abs(rep(1,n.temp)%*%t(t.temp)-t.temp%*%t(rep(1,n.temp))))
  corr.e=2^(-abs(rep(1,n.temp)%*%t(t.temp)-t.temp%*%t(rep(1,n.temp))))
  MX=t.temp^.5
  MZ=rep(0, n.temp)
  x.temp=mvrnorm(1,MX,corr)
  z.temp=mvrnorm(1,MZ, corr)
  z[[i]]=as.matrix(z.temp[1:nz[i]])
  x[[i]]=as.matrix(x.temp[-(1:nz[i])])
  id_z[[i]]=rep(i,nz[i])
  id_y[[i]]=rep(i,ny[i])
  y.temp=a+g*z.temp+x.temp*b+as.matrix(mvrnorm(1,rep(0,n.temp),corr.e))
  y[[i]]=as.matrix(y.temp[-(1:nz[i])])
}
data_cov=tibble(id_z=unlist(id_z),tz=unlist(tz),z=matrix(unlist(z),length(unlist(z))))
data_res=tibble(id_y=unlist(id_y),ty=unlist(ty),x=matrix(unlist(x),length(unlist(x))),y=unlist(y))
bd=0.1
omit=1
method=1
lda(data_res,data_cov,N,bd,omit,method)