Let's use *norm
as an example. From the documentation:
dnorm(x, mean = 0, sd = 1, log = FALSE)
pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)
qnorm(p, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)
rnorm(n, mean = 0, sd = 1)
So if I wanted to know the value of a standard normal distribution at 0, I would do
dnorm(0)
Which gives us 0.3989423
, a reasonable answer.
In the same way pnorm(0)
gives .5
. Again, this makes sense, because half of the distribution is to the left of 0.
qnorm
will essentially do the opposite of pnorm
. qnorm(.5)
gives 0
.
Finally, there's the rnorm
function:
rnorm(10)
Will generate 10 samples from standard normal.
If you want to change the parameters of a given distribution, simply change them like so
rnorm(10, mean=4, sd= 3)