However, we could elaborate a little longer to get a more detailed summary that will also output the alternative hypothesis and the confidence interval. The following code block demonstrates how such a function can be created. Afterward, we will see how to keep this function close in order to easily call and use it later:
z.test <- function(sample, mu, sigma, conf.lvl = .95, alternative = 'two.sided'){ n <- length(sample) xbar <- mean(sample, na.rm =T) z <- (xbar - mu)/(sigma/sqrt(n)) if(alternative == 'two.sided'){ p.value <- 2*pnorm(-abs(z)) alt <- 'not equal ' err <- -qnorm((1-conf.lvl)/2)*sigma/sqrt(n) a <- xbar - err b <- xbar + err } else if(alternative == 'greater'){ p.value <- pnorm(z, lower.tail = F)
