PCD.Corr: Pearson's coefficient of correlation between panel counts...

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

Description

Calculating Pearson's coefficient of correlation between panel counts within two non-overlapping time intervals (t1, t2] and (t3, t4]. The arguments of this function are the output of function PCDReg.wf. For the corresponding formula see Yao, Wang and He (2014+).

Usage

PCD.Corr(x, beta, nu, gamma, t1, t2, t3, t4, order, knots)

Arguments

x	the covariate vector.
beta	estimates of regression coefficients.
nu	estimate of gamma frailty variance parameter.
gamma	estimates of spline coefficients.
t1, t2	interval endpoints. t1 must be less than t2.
t3, t4	interval endpoints. t3 must be less than t4.
order	the order of basis functions.
knots	knots used in the analysis.

Value

corr.rho

Pearson's coefficient of correlation.

Note

The two intervals (t1, t2] and (t3, t4] must not be overlapped.

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

x1=c(1,1);
b1=fit$beta; n1=fit$nu; g1=fit$gamma;
t1=0; t2=6; t3=6; t4=12;
order=1; knots=fit$knots;

PCD.Corr(x1, b1, n1, g1, t1, t2, t3, t4, order, knots)

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