## Not run:
# ilgt <- function (x)
# {
# tem = exp(x)
# res = tem/(1 + tem)
# return(res)
# }
# lgt <- function (p)
# {
# log(p/(1 - p))
# }
# ## the vector of a parameter estimates if log(a),log(theta),logit(delta),beta0.
# tpar <- c(log(2),log(0.5),lgt(0.5),2)
# ypre <- c(0, 1)
# ynew <- c(1, 0, 0)
# ## No covariate
# XM <- NULL
# ## no missing visit
# stp <- c(0,1,1,1,1)
# RE <- "G"
# ## The estimate of the variance covariance matrix
# V <-
# matrix(
# c( 0.17720309, -0.240418504, 0.093562548, 0.009141980,
# -0.24041850, 0.605132808, -0.160454773, -0.003978118,
# 0.09356255, -0.160454773, 0.095101658, 0.005661923,
# 0.00914198, -0.003978118, 0.005661923, 0.007574769),
# nrow=4)
#
# ## the estimate of the conditional probability based on the sum summary statistics and its SE
# CP.ar1.se(tpar = tpar, ypre = ypre, ynew = ynew,
# XM =XM, stp = stp,
# RE = RE, V = V, MC = FALSE, qfun = "sum")
#
# ## the estimate of the conditional probability based on the max summary statistics and its SE
# CP.ar1.se(tpar = tpar, ypre = ypre, ynew = ynew,
# XM =XM, stp = stp,
# RE = RE, V = V, MC = FALSE, qfun = "max")
#
#
# ## CP.ar1.se calls for jCP.ar1 to compute the estimate of the conditional probability
# ## the estimate of the conditional probability based on the sum summary statistics
# jCP.ar1(tpar = tpar, ypre = ypre, ynew = ynew,
# y2m=NULL, XM =XM, stp = stp,
# RE = RE, LG = FALSE, MC = FALSE, N.MC = 40000, qfun = "sum", oth = NULL)
#
# ## jCP.ar1 calls for CP.ar1 to compute the estimate of the conditional probability
# ## via the adaptive quadrature (MC=F)
# ## the estimate of the conditional probability
#
# u <- rep(exp(tpar[4]),length(ypre)+length(ynew))
#
# CP1.ar1(ypre = ypre, ynew =ynew,
# stp =stp, u = u, th = exp(tpar[2]), a = exp(tpar[1]),
# dt= ilgt(tpar[3]), RE = RE, qfun = "sum")
#
#
# ## jCP.ar1 calls for CP.ar1 to compute the estimate of the conditional probability
# ## via the Monte Carlo method (MC=T)
# ## the estimate of the conditional probability
# MCCP.ar1(ypre = ypre, ynew =ynew, stp = stp,
# u = u, th = exp(tpar[2]), a = exp(tpar[1]), dt = ilgt(tpar[3]),
# RE = RE, N.MC = 1000, qfun = "sum")
# ## End(Not run)
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