Normal {base} | R Documentation |
Density, distribution function, quantile function and random
generation for the normal distribution with mean equal to mean
and standard deviation equal to sd
.
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)
x,q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. |
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]. |
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/(sqrt(2 pi) sigma) e^-((x - mu)^2/(2 sigma^2))
where mu is the mean of the distribution and sigma the standard deviation.
dnorm
gives the density,
pnorm
gives the distribution function,
qnorm
gives the quantile function, and
rnorm
generates random deviates.
runif
and .Random.seed
about random number
generation, and dlnorm
for the Lognormal distribution.
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=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)