# Normal

##### The Normal Distribution

Density, distribution function, quantile function and random
generation for the normal distribution with mean equal to `mean`

and standard deviation equal to `sd`

.

- Keywords
- distribution

##### Usage

```
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)
```

##### Arguments

- x, q
- vector of quantiles.
- p
- vector of probabilities.
- n
- number of observations. If
`length(n) > 1`

, the length is taken to be the number required. - mean
- vector of means.
- sd
- vector of standard deviations.
- log, log.p
- logical; if TRUE, probabilities p are given as log(p).
- lower.tail
- logical; if TRUE (default), probabilities are \(P[X \le x]\) otherwise, \(P[X > x]\).

##### Details

If `mean`

or `sd`

are not specified they assume the default
values of `0`

and `1`

, respectively. The normal distribution has density
$$
f(x) =
\frac{1}{\sqrt{2\pi}\sigma} e^{-(x-\mu)^2/2\sigma^2}$$
where \(\mu\) is the mean of the distribution and
\(\sigma\) the standard deviation.

##### Value

`dnorm`

gives the density,
`pnorm`

gives the distribution function,
`qnorm`

gives the quantile function, and
`rnorm`

generates random deviates. The length of the result is determined by `n`

for
`rnorm`

, and is the maximum of the lengths of the
numerical arguments for the other functions. The numerical arguments other than `n`

are recycled to the
length of the result. Only the first elements of the logical
arguments are used. For `sd = 0`

this gives the limit as `sd`

decreases to 0, a
point mass at `mu`

.
`sd < 0`

is an error and returns `NaN`

.

##### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
*The New S Language*.
Wadsworth & Brooks/Cole. Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995)
*Continuous Univariate Distributions*, volume 1, chapter 13.
Wiley, New York.

##### See Also

Distributions for other standard distributions, including
`dlnorm`

for the *Log*normal distribution.

##### Examples

`library(stats)`

```
require(graphics)
dnorm(0) == 1/sqrt(2*pi)
dnorm(1) == exp(-1/2)/sqrt(2*pi)
dnorm(1) == 1/sqrt(2*pi*exp(1))
## Using "log = TRUE" for an extended range :
par(mfrow = c(2,1))
plot(function(x) dnorm(x, log = TRUE), -60, 50,
main = "log { Normal density }")
curve(log(dnorm(x)), add = TRUE, col = "red", lwd = 2)
mtext("dnorm(x, log=TRUE)", adj = 0)
mtext("log(dnorm(x))", col = "red", adj = 1)
plot(function(x) pnorm(x, log.p = TRUE), -50, 10,
main = "log { Normal Cumulative }")
curve(log(pnorm(x)), add = TRUE, col = "red", lwd = 2)
mtext("pnorm(x, log=TRUE)", adj = 0)
mtext("log(pnorm(x))", col = "red", adj = 1)
## if you want the so-called 'error function'
erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
## (see Abramowitz and Stegun 29.2.29)
## and the so-called 'complementary error function'
erfc <- function(x) 2 * pnorm(x * sqrt(2), lower = FALSE)
## and the inverses
erfinv <- function (x) qnorm((1 + x)/2)/sqrt(2)
erfcinv <- function (x) qnorm(x/2, lower = FALSE)/sqrt(2)
```

*Documentation reproduced from package stats, version 3.3.3, License: Part of R 3.3.3*