R: The Normal Distribution

Normal {stats} | R Documentation

## The Normal Distribution

### Description

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

### 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 ≤ 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) = 1/(√(2 π) σ) e^-((x - μ)^2/(2 σ^2))

where μ is the mean of the distribution and σ the standard deviation.

`qnorm` is based on Wichura's algorithm AS 241 which provides precise results up to about 16 digits.

### Value

`dnorm` gives the density, `pnorm` gives the distribution function, `qnorm` gives the quantile function, and `rnorm` generates random deviates.

### Source

For `pnorm`, based on

Cody, W. D. (1993) Algorithm 715: SPECFUN – A portable FORTRAN package of special function routines and test drivers. ACM Transactions on Mathematical Software 19, 22–32.

For `qnorm`, the code is a C translation of

Wichura, M. J. (1988) Algorithm AS 241: The Percentage Points of the Normal Distribution. Applied Statistics, 37, 477–484.

For `rnorm`, see RNG for how to select the algorithm and for references to the supplied methods.

### 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 Lognormal distribution.

### Examples

    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)

[Package stats version 2.15.1 Index]