# Shapiro-Wilk Test for Exponentiality
# Implementer: Mike Burr
# Uses spline interpolation to to provide estimate of p value between points in the table.
shapiro.wilk.exponentiality.test.simple <- function(W, sample.size, alternative = c("two.sided", "less")) {
validate.htest.alternative(alternative = alternative)
p.value <- .shapiro.wilk.exponentiality.fn[[sample.size]](W)
gt.50.pct <- W > .shapiro.wilk.exponentiality.table[(sample.size-2), 7]
p.value <- ifelse(p.value < 0, 0, p.value)
p.value <- ifelse(p.value > 1, 1, p.value)
if (gt.50.pct) {
p.value <- 1-p.value
}
if (alternative[1] == "two.sided") {
#See pg 359 for method illustration in Shapiro/Wilk 1972.
p.value <- 2* p.value
} else {
}
p.value <- ifelse(p.value < 0, 0, p.value)
p.value <- ifelse(p.value > 1, 1, p.value)
retval<-list(data.name = "input data",
statistic = c(W = W),
estimate = c(W = W, sample.size = sample.size),
parameter = 1 ,
p.value = p.value,
null.value = 1,
alternative = alternative[1],
method = "Shapiro-Wilk Test for Exponentiality"#,
# conf.int = c(NA,NA)
)
names(retval$null.value) <- "W statistic"
names(retval$parameter) <- "null hypothesis W statistic"
class(retval)<-"htest"
retval
}
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