Nothing
CI.t.test <-
function(x, conf.level=0.95, fpc=1) {
# Generation of a one-sample two-sided confidence interval on the population mean
# based on the t-test, when sampling without replacement.
# 'x': Numeric vector of observations.
# 'conf.level': Confidence level of the interval.
# 'fpc' is the finite population correction, and is used when sampling without replacement.
# Note: \code{fcp} is typically defined as \code{1-n/N},
# where \code{n} is the sample size, and \code{N} is the population size.
# example: Sample 43 observations from a list of 200 numbers, and compute the 95% confidence interval.
# pop = sqrt(1:200) ; x1 = sample( pop, 43 ) ; list(sort(x1))
# fpc = 1 - length(x1) / length(pop) ; CI.t.test( x1, fpc=fpc )
# example: Sample 14 observations from a Normal(mean=50, sd=5) distribution,
# and compute the 90% confidence interval.
# x2 = sample( 14, 50, 5 ) ; list(sort(x2)) ; CI.t.test( x2, 0.9 )
if (!is.numeric(x)) stop("'x' must be numeric.")
if (length(x)<2) stop("'x' must contain at least two numbers.")
if (!is.numeric(conf.level) | length(conf.level)!=1) stop("'conf.level' must be a number between 0 and 1.")
if (conf.level <=0 | conf.level >= 1) stop("'conf.level' must be a number between 0 and 1.")
if (!is.numeric(fpc)) stop("'fpc' must be numeric.")
if (fpc <=0 | fpc > 1) stop("'fpc' must be between 0 and 1.")
if (fpc==1) {
y <- as.vector(t.test(x, conf.level=conf.level)$conf.int) }
else {
y <- rep(NA, 2)
y[1] <- mean(x) + qt( ((1-conf.level)/2), (length(x)-1) ) *
sqrt( var(x)*fpc/length(x) )
y[2] <- mean(x) - qt( ((1-conf.level)/2), (length(x)-1) ) *
sqrt( var(x)*fpc/length(x) )
}
return(y)
}
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