[整理] 利用R生成随机分布的…-白红宇的个人博客

[整理] 利用R生成随机分布的…

发布日期：2021-10-16 07:12:17 浏览次数：19 分类：技术文章

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[整理] 利用R生成随机分布的方法

文/周庭锐

夜里复习各种统计分布的模拟、拟合、验证的R编程，顺手整理一下。

（不懂怎么一回事，刚刚贴上了，然后一转眼就消失了。新浪博客里闹鬼？）

d: density

p: distribution function

q: quantile function

r: random deviates

rexp The Exponential Distribution

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)

rf The F Distribution

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)

rgamma The Gamma Distribution

dgamma(x, shape, rate = 1, scale = 1/rate, log = FALSE)

pgamma(q, shape, rate = 1, scale = 1/rate, lower.tail = TRUE, log.p = FALSE)

qgamma(p, shape, rate = 1, scale = 1/rate, lower.tail = TRUE, log.p = FALSE)

rgamma(n, shape, rate = 1, scale = 1/rate)

rgeom The Geometric Distribution

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)

rhyper The Hypergeometric Distribution

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)

rlnorm The Log Normal Distribution

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)

rlogis The Logistic Distribution

dlogis(x, location = 0, scale = 1, log = FALSE)

plogis(q, location = 0, scale = 1, lower.tail = TRUE, log.p = FALSE)

qlogis(p, location = 0, scale = 1, lower.tail = TRUE, log.p = FALSE)

rlogis(n, location = 0, scale = 1)

rmultinom The Multinomial Distribution

rmultinom(n, size, prob)

dmultinom(x, size = NULL, prob, log = FALSE)

rnbinom The Negative Binomial Distribution

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)

rnorm The Normal Distribution

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)

rpois The Poisson Distribution

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)

rsignrank Distribution of the Wilcoxon Signed Rank Statistic

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)

rt The Student t Distribution

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)

runif The Uniform Distribution

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)

rweibull The Weibull Distribution

dweibull(x, shape, scale = 1, log = FALSE)

pweibull(q, shape, scale = 1, lower.tail = TRUE, log.p = FALSE)

qweibull(p, shape, scale = 1, lower.tail = TRUE, log.p = FALSE)

rweibull(n, shape, scale = 1)

rwilcox Distribution of the Wilcoxon Rank Sum Statistic

dwilcox(x, m, n, log = FALSE)

pwilcox(q, m, n, lower.tail = TRUE, log.p = FALSE)

qwilcox(p, m, n, lower.tail = TRUE, log.p = FALSE)

rwilcox(nn, m, n)

sample The Discrete Uniform Distribution

sample(x, size, replace = FALSE, prob = NULL)

sample.int(n, size = n, replace = FALSE, prob = NULL)

拟合：

连续型变量：

大样本：Kolmogorov-Smirnov检验

ks.test(x, y, ...,

alternative = c("two.sided", "less", "greater"),

exact = NULL)

小样本：Shapiro-Wilk检验

shapiro.test(x)

离散型变量：方差齐次性检验

fligner.test(x, ...)

## Default S3 method:

fligner.test(x, g, ...)

## S3 method for class 'formula'

fligner.test(formula, data, subset, na.action, ...)

或

mood.test(x, ...)

## Default S3 method:

mood.test(x, y,

alternative = c("two.sided", "less", "greater"), ...)

## S3 method for class 'formula'

mood.test(formula, data, subset, na.action, ...)

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