library(knitr) library(bmiproj)
$$\mu_n = \mu_{n - 1} + \frac{x_n - \mu_{n-1}}{n}$$
From the formula above, the old mean is $\mu_{n-1}$ and the new data is $x_n$.
An example code from the function new_mean
y <- new_mean(10, 12, 4) y
$$s_n^2 = \frac{n- 2}{n - 1}s_{n - 1}^2 + \frac{(x_n - \mu_{n-1})^2}{n} $$
From the formula above, the old sample variance is $s^2_{n - 1}$, the new data is $x_n$ and the old mean is $\mu_{n - 1}$.
An example code from the function newvar
x <- newvar(5, 4, 6, 5) x
data <- array(c("a", "c", "b", "d"), c(2, 2)) colnames(data) <- c("Risk", "No Risk") rownames(data) <- c("Treatment", "Control") kable(data)
The 2x2 contigency table above shows how the data could be used in function odds.ratio. The CI parameter controls the two-sided confidence interval.
An example of the function odds.ratio
z <- oddsratio(c(50, 40, 40, 60), CI = 0.90) z
The lat1 and lat2 can only take any values between -90 and 90. On the other hand, long1 and long2 can only take values between -180 and 180.
An example of the function crowfly_dist
crowfly_dist <- function (long1, lat1, long2, lat2){ stopifnot(sapply(c(long1, lat1, long2, lat2), is.numeric)) stopifnot(all(c(long1, long2) < 180 & c(long1, long2) > -180)) stopifnot(all(c(lat1, lat2) < 90 & c(lat1, lat2) > -90)) rad <- pi / 180 a1 <- lat1 * rad a2 <- long1 * rad b1 <- lat2 * rad b2 <- long2 * rad dlon <- b2 - a2 dlat <- b1 - a1 a <- (sin(dlat / 2))^2 + cos(a1) * cos(b1) * (sin(dlon / 2))^2 c <- 2 * atan2(sqrt(a), sqrt(1 - a)) R <- 6378.145 d <- R * c return(d / 1.60934) }
distance <- crowfly_dist(-50, 20, 90, -80) distance
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