Distributions in R
?distribution
The beta distribution - dbeta
The binomial (including bernoulli) distribution - dbinom
The cauchy distribution - dcauchy
The chi-squared distribution - dchisq
The exponential distribution - dexp
The f distribution - df
The gamma distribution - dgamma
The geometric distribution - dgeom
The hypergeometric distribution - dhyper
The log-normal distribution - dlnorm
The multinomial distribution - dmultinom
The negative binomial distribution - dnbinom
The normal distribution - dnorm
The poisson distribution - dpois
The student's t distribution - dt
The uniform distribution - dunif
The weibull distribution - dweibull
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)
rnorm() generates random numbers from a normal distribution
> rnorm(1) # generates 1 random number
> rnorm(3) # generates 3 random number
> rnorm(3, mean=10, sd=2) # provide our own mean and standard deviation
temp_norm <- rnorm(200,mean=mean(temp, na.rm=TRUE), sd=sd(temp, na.rm=TRUE))
x <- rnorm(100, mean=.5, sd=.3)
dunif(x, min = 0, max = 1, log = FALSE)
punif(q, min = 0, max = 1, lower.tail = TRUE, log.p = FALSE)
qunif(p, min = 0, max = 1, lower.tail = TRUE, log.p = FALSE)
runif(n, min = 0, max = 1)
runif() generates random numbers from a uniform distribution
> runif(1) # generates 1 random number
> runif(5) # generates 5 random number
> runif(n=3, min=5, max=10) # define the range between 5 and 10
runif(9,3,6)
runif(3,,6)
dunif()
punif()
qunif()
x <- rexp(10)
dlnorm(x, meanlog = 0, sdlog = 1, log = FALSE)
plnorm(q, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE)
qlnorm(p, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE)
rlnorm(n, meanlog = 0, sdlog = 1)
dgamma(x, shape, rate = 1, scale = 1/rate, log = FALSE)
-log(dgamma(1:4, shape = 1))
pgamma(q, shape, rate = 1, scale = 1/rate, lower.tail = TRUE, log.p = FALSE)
pgamma(5e-324, 0.001)
qgamma(p, shape, rate = 1, scale = 1/rate, lower.tail = TRUE, log.p = FALSE)
1 - 1/exp(qgamma(p, shape = 1))
pgamma(qgamma(p, shape = 2), shape = 2)
rgamma(n, shape, rate = 1, scale = 1/rate)
table(rgamma(1e4, 0.001) == 0)/1e4
dpois(x, lambda, log = FALSE)
ppois(q, lambda, lower.tail = TRUE, log.p = FALSE)
qpois(p, lambda, lower.tail = TRUE, log.p = FALSE)
rpois(n, lambda)
dchisq(x, df, ncp = 0, log = FALSE)
dchisq(1, df = 1:3)
pchisq(q, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)
pchisq(1, df = 3, ncp = 0:4)
qchisq(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)
rchisq(n, df, ncp = 0)
Z0 <- rchisq(100, df = 0, ncp = 2.)
dbinom(x, size, prob, log = FALSE)
sum(dbinom(46:54, 100, 0.5))
pbinom(q, size, prob, lower.tail = TRUE, log.p = FALSE)
qbinom(p, size, prob, lower.tail = TRUE, log.p = FALSE)
rbinom(n, size, prob)
dnbinom(x, size, prob, mu, log = FALSE)
pnbinom(q, size, prob, mu, lower.tail = TRUE, log.p = FALSE)
qnbinom(p, size, prob, mu, lower.tail = TRUE, log.p = FALSE)
rnbinom(n, size, prob, mu)
rmultinom(n, size, prob)
rmultinom(10, size = 12, prob = c(0.1,0.2,0.8))
rmultinom(10, 20, prob = pr)
dmultinom(x, size = NULL, prob, log = FALSE)
dgeom(x, prob, log = FALSE)
pgeom(q, prob, lower.tail = TRUE, log.p = FALSE)
qgeom(p, prob, lower.tail = TRUE, log.p = FALSE)
rgeom(n, prob)
dbeta(x, shape1, shape2, ncp = 0, log = FALSE)
dbeta(x, 1, 1)
pbeta(q, shape1, shape2, ncp = 0, lower.tail = TRUE, log.p = FALSE)
pbeta(x, 1, 1)
qbeta(p, shape1, shape2, ncp = 0, lower.tail = TRUE, log.p = FALSE)
rbeta(n, shape1, shape2, ncp = 0)
dcauchy(x, location = 0, scale = 1, log = FALSE)
pcauchy(q, location = 0, scale = 1, lower.tail = TRUE, log.p = FALSE)
qcauchy(p, location = 0, scale = 1, lower.tail = TRUE, log.p = FALSE)
rcauchy(n, location = 0, scale = 1)
dexp(x, rate = 1, log = FALSE)
pexp(q, rate = 1, lower.tail = TRUE, log.p = FALSE)
qexp(p, rate = 1, lower.tail = TRUE, log.p = FALSE)
rexp(n, rate = 1)
df(x, df1, df2, ncp, log = FALSE)
pf(q, df1, df2, ncp, lower.tail = TRUE, log.p = FALSE)
qf(p, df1, df2, ncp, lower.tail = TRUE, log.p = FALSE)
rf(n, df1, df2, ncp)
dt(x, df, ncp, log = FALSE)
pt(q, df, ncp, lower.tail = TRUE, log.p = FALSE)
qt(p, df, ncp, lower.tail = TRUE, log.p = FALSE)
rt(n, df, ncp)
dhyper(x, m, n, k, log = FALSE)
phyper(q, m, n, k, lower.tail = TRUE, log.p = FALSE)
qhyper(p, m, n, k, lower.tail = TRUE, log.p = FALSE)
rhyper(nn, m, n, k)
dweibull(x, shape, scale = 1, log = FALSE)
all.equal(dweibull(x, shape = 1), dexp(x))
pweibull(q, shape, scale = 1, lower.tail = TRUE, log.p = FALSE)
qweibull(p, shape, scale = 1, lower.tail = TRUE, log.p = FALSE)
all.equal(qweibull(x/11, shape = 1, scale = pi), qexp(x/11, rate = 1/pi))
rweibull(n, shape, scale = 1)
qbirthday(prob = 0.5, classes = 365, coincident = 2)
qbirthday(coincident = 10)
pbirthday(n, classes = 365, coincident = 2)
pbirthday(23, coincident = 3)
dsignrank(x, n, log = FALSE)
psignrank(q, n, lower.tail = TRUE, log.p = FALSE)
qsignrank(p, n, lower.tail = TRUE, log.p = FALSE)
rsignrank(nn, n)
ptukey(q, nmeans, df, nranges = 1, lower.tail = TRUE, log.p = FALSE)
summary(abs(.95 - ptukey(qtt, 2, df = 2:11)))
qtukey(p, nmeans, df, nranges = 1, lower.tail = TRUE, log.p = FALSE)
dwilcox(x, m, n, log = FALSE)
fx <- dwilcox(x, 3, 6)
pwilcox(q, m, n, lower.tail = TRUE, log.p = FALSE)
Fx <- pwilcox(x, 2, 6)
qwilcox(p, m, n, lower.tail = TRUE, log.p = FALSE)
rwilcox(nn, m, n)
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