Running examples in ‘binGroup-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: binGroup-package
> ### Title: Statistical Methods for Group Testing.
> ### Aliases: binGroup-package binGroup
> ### Keywords: package regression models htest
> 
> ### ** Examples
> 
> 
> # 1) One-sample problem
> 
> # 1.1) Confidence intervals for designs with equal group size (pool size),
> # where 
> #  n denotes the number of groups (pools),
> #  s denotes the common group size (number of individuals pooled per group),
> #  y denotes the number of groups tested positive.
> 
> # The following call reproduces the example given 
> # by Tebbs and Bilder (2004) for the two-sided 95-percent 
> # exact (Clopper-Pearson) interval:
> 
> bgtCI(n=24, y=3, s=7, conf.level=0.95,
+  alternative="two.sided", method="CP")

95 percent CP confidence interval:
 [ 0.003838, 0.05432 ]
Point estimate: 0.0189 
> 
> # 1.2) Confidence intervals for designs with unequal group size (pool size):
> # Keeping notation as above but allowing for (a limited number of) different
> # group size s, the examples given in Hepworth (1996), Table 5 can be 
> # reproduced by calling:
> 
>  bgtvs(n=c(2,3), s=c(5,2), y=c(0,0))
Input 
                          [,1] [,2]
number of groups             2    3
group size                   5    2
number of positive groups    0    0

Exact 95 percent confidence interval 

[1] 0.0000 0.2059

Point estimate: 

[1] 0
>  bgtvs(n=c(2,3), s=c(5,2), y=c(0,1))
Input 
                          [,1] [,2]
number of groups             2    3
group size                   5    2
number of positive groups    0    1

Exact 95 percent confidence interval 

[1] 0.001581 0.266743

Point estimate: 

[1] 0.06459
> 
> # The function pooledBin provides different methods for the same problem,
> # where x is the number of positive groups, m is the size of the groups and
> # n is the number of groups with the correesponding sizes:
> 
> pooledBin(x=c(0,1), m=c(5,2), n=c(2,3), ci.method="score")
PointEst    Lower    Upper 
  0.0584   0.0114   0.2410 
> pooledBin(x=c(0,1), m=c(5,2), n=c(2,3), ci.method="lrt")
PointEst    Lower    Upper 
  0.0584   0.0038   0.2553 
> pooledBin(x=c(0,1), m=c(5,2), n=c(2,3), ci.method="bc-skew-score")
PointEst    Lower    Upper 
  0.0584   0.0038   0.2499 
> 
> # 1.3) For experimental design based on the bias of the point estimate,
> # as proposed by Swallow (1985): The values in Table 1 (Swallow, 1985),
> # p.885 can be reproduced by calling:
> 
> estDesign(n=10, smax=100, p.tr=0.001)
 minimal mse(p) is achieved with group size s >= smax 
$varp
[1] 1.198715e-06

$mse
[1] 1.201888e-06

$bias
[1] 5.63273e-05

$exp
[1] 0.001056327

> estDesign(n=10, smax=100, p.tr=0.01)
group size s with minimal mse(p) = 33 
$varp
[1] 4.539192e-05

$mse
[1] 4.580377e-05

$bias
[1] 0.0006417578

$exp
[1] 0.01064176

> 
> # 2) Two-sample comparison
> 
> # Assume a design, where pools 5, 1, 1, 30, and 20 pools of size 10, 4, 1, 25, 50, 
> # respectively, are used to estimate the prevalence in two populations.
> # In population 1, one out of 5 pools with 10 units is positive,
> # while in population 2, two out of five pools with 10 units is positive as well as 
> # the one pool with only 1 unit.
> # The difference of proportions is to be estimated.
> 
> x1 <- c(1,0,0,0,0)
> m1 <- c(10,4,1,25,50)
> n1 <- c(5,1,1,30,20)
> 
> x2 <- c(2,0,1,0,0)
> m2 <- c(10,4,1,25,50)
> n2 <- c(5,1,1,30,20)
> 
> pooledBinDiff(x1=x1, m1=m1,x2=x2, m2=m2, n1=n1, n2=n2, ci.method="lrt")
PointEst    Lower    Upper 
 -0.0011  -0.0039   0.0012 
> 
> 
> # 3) Regression models
> 
> # 3.1) Fitting a regression model
> # A HIV surveillance data (used by Vansteelandt et al. 2000)
> # can be analysed for the dependence of HIV prevalence
> # on covariates AGE and EDUC., with sensitivity and specificity
> # assumed to be 0.9 each.
> 
> data(hivsurv)
> fit1 <- gtreg(formula = groupres ~ AGE + EDUC., data = hivsurv,
+   groupn = gnum, sens = 0.9, spec = 0.9, method = "Xie")
Error in nrow(dd) : object 'dd' not found
Calls: gtreg ... by.data.frame -> structure -> eval -> eval -> tapply -> nrow
Execution halted