# R/mod_normal_fct.R In predictoR: Predictive Data Analysis System

```########################## modeest ###########################################

fisher.calc <- function (x, na.rm = FALSE, ...) {
if (!is.numeric(x)) {
stop("x must be numeric")
}
if (na.rm)
x <- x[!is.na(x)]
nx <- length(x)

sk <- sum((x - mean(x))^3/stats::sd(x)^3)/nx

return(sk)
}

########################## nortest ###########################################

pearson.test <- function (x, n.classes = ceiling(2 * (n^(2/5))), adjust = TRUE) {
x <- x[complete.cases(x)]
n <- length(x)
dfd <- 2
}
else {
dfd <- 0
}
num <- floor(1 + n.classes * pnorm(x, mean(x), sd(x)))
count <- tabulate(num, n.classes)
prob <- rep(1/n.classes, n.classes)
xpec <- n * prob
h <- ((count - xpec)^2)/xpec
P <- sum(h)
pvalue <- pchisq(P, n.classes - dfd - 1, lower.tail = FALSE)

return(pvalue)
}

lillie.test <- function (x) {
x <- sort(x[complete.cases(x)])
n <- length(x)
if (n < 5)
stop("sample size must be greater than 4")
p <- pnorm((x - mean(x))/sd(x))
Dplus <- max(seq(1:n)/n - p)
Dminus <- max(p - (seq(1:n) - 1)/n)
K <- max(Dplus, Dminus)
if (n <= 100) {
Kd <- K
nd <- n
}
else {
Kd <- K * ((n/100)^0.49)
nd <- 100
}
pvalue <- exp(-7.01256 * Kd^2 * (nd + 2.78019) + 2.99587 *
Kd * sqrt(nd + 2.78019) - 0.122119 + 0.974598/sqrt(nd) +
1.67997/nd)

return(pvalue)
}

cvm.test <- function (x) {
x <- sort(x[complete.cases(x)])
n <- length(x)
if (n < 8)
stop("sample size must be greater than 7")
p <- pnorm((x - mean(x))/sd(x))
W <- (1/(12 * n) + sum((p - (2 * seq(1:n) - 1)/(2 * n))^2))
WW <- (1 + 0.5/n) * W
if (WW < 0.0275) {
pval <- 1 - exp(-13.953 + 775.5 * WW - 12542.61 * WW^2)
}
else if (WW < 0.051) {
pval <- 1 - exp(-5.903 + 179.546 * WW - 1515.29 * WW^2)
}
else if (WW < 0.092) {
pval <- exp(0.886 - 31.62 * WW + 10.897 * WW^2)
}
else if (WW < 1.1) {
pval <- exp(1.111 - 34.242 * WW + 12.832 * WW^2)
}
else {
pval <- 7.37e-10
}
return(pval)
}
```

## Try the predictoR package in your browser

Any scripts or data that you put into this service are public.

predictoR documentation built on April 30, 2022, 1:05 a.m.