Nothing

```
# Calculate confidence intervals based on a set of bootstrap estimates.
# In bootCIlogit, the corrections are perfomed on the logistic scale.
# t0 is point estimate of parameter of interest
# bt is a vector of bootstrap estimates
# conf is required confidence interval
# Returns: matrix with estimators in rows, lower/upper limits in columns.
bootCI <-
function(t0, bt, conf=0.95) {
bt <- as.vector(bt)
bt <- bt[is.finite(bt)]
out <- matrix(NA, 5, 2)
dimnames(out) <- list(c("norm", "norm0", "basic", "basic0", "perc"),
c("lower","upper"))
bias <- mean(bt) - t0
merr <- sd(bt) * qnorm((1 + conf)/2)
out[1, ] <- c(t0 - bias - merr, t0 - bias + merr) # norm, same as boot.ci
out[2, ] <- c(t0 - merr, t0 + merr) # norm0
out[5, ] <- quantileInter(bt, conf) # perc
out[3, ] <- 2 * t0 - out[5, 2:1] # basic, same as boot.ci
out[4, ] <- out[5, ] - bias # basic0
return(out)
}
bootCIlogit <-
function(t0, bt, conf=0.95)
plogis(bootCI(qlogis(t0), qlogis(bt), conf=conf))
# Normally-interpolated quantile confidence interval.
quantileInter <-
function(bt, conf=0.95)
# Args:
# bt : numeric vector, no missing values
# conf : required confidence interval
# Returns: a vector of length 2, lower and upper confidence limits,
# or NAs if the vector is not long enough.
# Not exported.
{
R <- length(bt)
alpha <- (1 + c(-conf, conf)) / 2
rk <- (R+1) * alpha
if (!all(rk>1 & rk<R) ) {
out <- rep(NA_real_, 2)
} else {
k <- trunc(rk)
ts <- sort(bt, partial = sort(c(k, k+1)))[c(k, k+1)]
if(all(k == rk)) {
out <- ts[1:2]
} else {
wanted <- qnorm(alpha)
tooLo <- qnorm(k/(R+1))
tooHi <- qnorm((k+1)/(R+1))
tLo <- ts[1:2]
tHi <- ts[3:4]
out <- tLo + (wanted-tooLo)/(tooHi - tooLo)*(tHi - tLo) # interpolation
}
}
return(out)
}
```

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