Fdist(stats)
Fdist()所属R语言包：stats
The F Distribution
F分布
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Density, distribution function, quantile function and random generation for the F distribution with df1 and df2 degrees of freedom (and optional non-centrality parameter ncp).
密度分布函数，位数df1和df2自由度（和可选的非集中参数ncp）F分布的函数和随机生成的。
用法----------Usage----------
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)
参数----------Arguments----------
参数：x, q
vector of quantiles.
位数的向量。
参数：p
vector of probabilities.
概率向量。
参数：n
number of observations. If length(n) > 1, the length is taken to be the number required.
观测数。如果length(n) > 1，长度是所需的数量。
参数：df1, df2
degrees of freedom. Inf is allowed.
自由度。 Inf是允许的。
参数：ncp
non-centrality parameter. If omitted the central F is assumed.
非核心参数。如果省略中央F是假设。
参数：log, log.p
logical; if TRUE, probabilities p are given as log(p).
逻辑;如果为TRUE，概率P日志（P）。
参数：lower.tail
logical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x].
逻辑;如果是TRUE（默认），概率P[X ≤ x]，否则，“P[X > x]。
Details
详情----------Details----------
The F distribution with df1 = n1 and df2 = n2 degrees of freedom has density
df1 =n1和df2 =n2自由度的F分布密度
for x > 0.
x > 0。
It is the distribution of the ratio of the mean squares of n1 and n2 independent standard normals, and hence of the ratio of two independent chi-squared variates each divided by its degrees of freedom. Since the ratio of a normal and the root mean-square of m independent normals has a Student's t_m distribution, the square of a t_m variate has a F distribution on 1 and m degrees of freedom.
这是的n1和n2独立标准法线的平均广场的比例分配，并因此在两个独立的卡方分布随机数除以它的自由度每比例。因为有一个正常的比例和m独立的法线方均根学生的t_m分布，方t_m变量有1 F分布和<X >自由度。
The non-central F distribution is again the ratio of mean squares of independent normals of unit variance, but those in the numerator are allowed to have non-zero means and ncp is the sum of squares of the means. See Chisquare for further details on non-central distributions.
非中心F分布又是独立法线单位方差的均方比例，但这些分子都不允许有非零的手段和ncp的手段平方的总和。看到卡方非中心分布的进一步细节。
值----------Value----------
df gives the density, pf gives the distribution function qf gives the quantile function, and rf generates random deviates.
df给人的密度，pf给出了分布函数qf给人的分量功能，rf产生随机的偏离。
Invalid arguments will result in return value NaN, with a warning.
无效的参数将导致返回值NaN，一个警告。
注意----------Note----------
Supplying ncp = 0 uses the algorithm for the non-central distribution, which is not the same algorithm used if ncp is omitted. This is to give consistent behaviour in extreme cases with values of ncp very near zero.
供应ncp = 0使用算法的非中心分布，这是不使用相同的算法，如果ncp省略。这是给予一致的行为，在极端情况下ncp非常接近零值。
The code for non-zero ncp is principally intended to be used for moderate values of ncp: it will not be highly accurate, especially in the tails, for large values.
为非零ncp代码主要拟用于适度值ncp：它不会是非常准确的，尤其是在尾巴大值。
源----------Source----------
For the central case of df, computed via a binomial probability, code contributed by Catherine Loader (see dbinom); for the non-central case computed via dbeta, code contributed by Peter Ruckdeschel.
的df中央的情况下，通过二项式概率计算，代码贡献由凯瑟琳装载机（见dbinom）;彼得Ruckdeschel通过dbeta计算的非中央的情况下，代码贡献。
For pf, via pbeta (or for large df2, via pchisq).
pf，通过pbeta（或大df2，通过pchisq）。
For qf, via qchisq for large df2, else via qbeta.
对于qf通过qchisq大df2，通过qbeta别的。
参考文献----------References----------
The New S Language. Wadsworth & Brooks/Cole.
Continuous Univariate Distributions, volume 2, chapters 27 and 30. Wiley, New York.
参见----------See Also----------
Distributions for other standard distributions, including dchisq for chi-squared and dt for Student's t distributions.
其他标准分布，包括dchisq卡方和dt学生的t分布的分布。
举例----------Examples----------
## the density of the square of a t_m is 2*dt(x, m)/(2*x)[＃平方米一个的漩涡密度是2 * DT（X，M）/（2 * X）]
# check this is the same as the density of F_{1,m}[这是F_密度相同{1，M}]
x <- seq(0.001, 5, len=100)
all.equal(df(x^2, 1, 5), dt(x, 5)/x)
## Identity: qf(2*p - 1, 1, df)) == qt(p, df)^2) for p >= 1/2[＃身份：QF（2 * P - 1，1，DF））== QT为P（P DF）^ 2）= 1/2]
p <- seq(1/2, .99, length=50); df <- 10
rel.err <- function(x,y) ifelse(x==y,0, abs(x-y)/mean(abs(c(x,y))))
quantile(rel.err(qf(2*p - 1, df1=1, df2=df), qt(p, df)^2), .90)# ~= 7e-9[〜= 7E-9]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-2-17 09:51:29
