PCDReg.nf: Regression analysis of panel count data under the...

In PCDSpline: Semiparametric regression analysis of panel count data using monotone splines

Description

Fits the nonhomogeneous Poisson process model to panel count data using EM algorithm.

Usage

PCDReg.nf(DATA, order, placement, nknot, myknots, binit, ginit, t.seq, tol)

Arguments

DATA	use specified data structure.
order	the order of basis functions.
placement	logical, if TRUE knots are placed evenly across the observed intervals based on the input data set; if FALSE knots should be specified by the user. see myknots.
nknot	the number of knots to be used.
myknots	a sequence of increasing points whose length is nknot.
binit	initial estimate of regression coefficients.
ginit	initial estimate of spline coefficients whose length should be (order+nknot-2) or (order+length(myknots)-2).
t.seq	an increasing sequence of points at which the baseline mean function is evaluated.
tol	the convergence criterion of the EM algorithm.

Details

The above function fits the non-homogeneous Poisson process model to panel count data via EM algorithm.

Value

beta	estimates of regression coefficients.
gamma	estimates of spline coefficients.
var.b	the variance covariance matrix of regression coefficients.
Hess	Hessian matrix.
knots	the knots used in the analysis.
bmf	estimated baseline mean function evaluated at the points t.seq; use pmf to plot the baseline mean fuction.
AIC	the Akaike information criterion.
BIC	the Bayesian information/Schwarz criterion.
flag	the indicator whether the Hessian matrix is non-singular. When flag="TRUE",the variance estimate may not be accurate.

Note

The non-homogeneous Poisson process model involves no Gamma frailty.

References

Yao, B., Wang, L., and He, X. (2014+). Semiparametric regression analysis of panel count data allowing for within-subject correlation.

See Also

PCDReg.wf

Examples

##Simulated Data

n=13; #number of subjects

##generate the number of observations for each subject
k=rpois(n,6)+1; K=max(k);
  
##generate random time gaps for each subject
y=matrix(,n,K);
for (i in 1:n){y[i,1:k[i]]=rexp(k[i],1)} 

##get observation time points for each subject
t=matrix(,n,K);
for (i in 1:n){
  for (j in 2:K){
    t[i,1] = y[i,1]
    t[i,j] = y[i,j]+t[i,j-1]
  }
}

##covariate x1 and x2 generated from Normal(0,0.5^2) and Bernoulli(0.5) respectively
x1=rnorm(n,0,0.5); x2=rbinom(n,1,0.5); x=cbind(x1,x2)

##true regression parameters and frailty variance parameter
beta1=1; beta2=-1; nu=0.5; 
parms=c(beta1,beta2)
phi=rgamma(n,nu,nu) 

##true baseline mean function
mu=function(t){2*t^(0.5)} 

##get the number of events between time intervals
z=matrix(,n,K);
xparms<-c();for (s in 1:nrow(x)){xparms[s]<-sum(x[s,]*parms)}
for (i in 1:n){
 z[i,1]<-rpois(1,mu(t[i,1])*exp(xparms[i])*phi[i]) 
 if (k[i]>1){
 z[i,2:k[i]]<-rpois(k[i]-1,(mu(t[i,2:k[i]])-mu(t[i,1:(k[i]-1)]))*exp(xparms[i])*phi[i])
 }
}

TestD<-list(t=t, x=x, z=z, k=k, K=K)

fit<-PCDReg.nf(DATA = TestD, order = 1, placement = TRUE, nknot=3,
               myknots, binit = c(-0.5,0.5) , ginit = seq(0.1,2),
               t.seq = seq(0,15,0.2), tol=10^(-3))

Example output

There were 26 warnings (use warnings() to see them)

PCDSpline documentation built on May 2, 2019, 5:14 a.m.