R语言 rriskDistributions包 get.norm.sd()函数中文帮助文档(中英文对照)

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get.norm.sd(rriskDistributions)
get.norm.sd()所属R语言包：rriskDistributions

                                        Fitting standard deviation of a normal distribution from one or more quantiles and known mean
                                         从一个或更多的位数的和公知的平均拟合标准偏差的正态分布

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

get.norm.sd returns the standard deviation of a normal distribution where the pth percentiles match with the quantiles q.
get.norm.sd返回一个正常的分布的标准偏差，其中p百分位匹配位数q。


用法----------Usage----------


  get.norm.sd(p=c(0.025,0.5,0.975), q, show.output=TRUE,
    plot=TRUE,
    fit.weights=rep(1,length(p)),scaleX=c(0.1,0.9),...)



参数----------Arguments----------

参数：p
numeric, vector of probabilities.
数字的概率，向量。


参数：q
numeric, vector of quantiles corresponding to p.
向量的数字，位数对应于P。


参数：show.output
logical, if TRUE the optim result will be printed (default vaule is TRUE).
逻辑，如果TRUE optim的结果将被打印（默认情况下，徐玉梅，是TRUE）。


参数：plot
logical, if TRUE the graphical diagnostics will be plotted (default value is TRUE).
逻辑，如果TRUE将被绘制的图形诊断（默认值是TRUE）。


参数：fit.weights
numerical vector of the same length as a probabilities vector p containing positive values for weighting quantiles. By default all quantiles will be weighted by 1.
相同的长度为一个数值向量概率矢量p含有正的值进行加权分位数。默认情况下，所有的位数将加权1。


参数：scaleX
numerical vector of the length 2 containing values (from the open interval (0,1)) for scaling quantile-axis (relevant only if plot=TRUE). The smaller the left value, the further the graph is extrapolated within the lower percentile, the greater the right value, the further it goes within the upper percentile.
数值向量的长度位数轴缩放2的值（在开区间（0,1））（相关的只有plot=TRUE）。左边的值较小，进一步在图形内的较低百分推断，更大的权值，它进一步去内上部百分。


参数：...
further arguments passed to the functions plot and points (relevant only if plot=TRUE).
进一步的参数传递的功能plot和只有points（相关plot=TRUE）。


Details

详细信息----------Details----------

The number of probabilities and the number of quantiles must be identical and should be at least two. get.norm.sd uses the central limit theorem and the linear regression. <br> <br> If show.output=TRUE the output of the function lm will be shown. <br> <br> The items of the probability vector p should lie between 0 and 1. <br> <br> The items of the weighting vector fit.weights should be positive values. <br> <br> The function will be meaningful only if the quantile comes from a normal distribution.
概率和分位数的数目的数目必须是相同的，并且应该是至少两个。 get.norm.sd使用中心极限定理和线性回归。参考<br>如果show.output=TRUE输出的功能lm将显示。是参考参考本的的概率向量p件应位于0和1之间。参考参考项目的权重向量fit.weights应该是正面的价值观。 <BR> <BR>的功能将是有意义的，只有当位数的正态分布。


值----------Value----------

Returns an estimated standard deviation or missing value
返回一个估计的标准偏差或遗漏值


注意----------Note----------

It should be noted that the data must be normally distributed, or the central limt theorem must hold for large (enough) samples sizes.
应当指出，数据必须正态分布，或中央limt定理必须保持大的（足够的）样品尺寸。


（作者）----------Author(s)----------



Matthias Greiner <a href="mailto:matthias.greiner@bfr.bund.de">matthias.greiner@bfr.bund.de</a>
(BfR), <br> Katharina Schueller
<a href="mailto:schueller@stat-up.de">schueller@stat-up.de</a> (<acronym><span class="acronym">STAT-UP</span></acronym>
Statistical Consulting), <br> Natalia Belgorodski
<a href="mailto:belgorodski@stat-up.de">belgorodski@stat-up.de</a> (<acronym><span class="acronym">STAT-UP</span></acronym>
Statistical Consulting)




参见----------See Also----------

See pnorm for distribution implementation details.
见pnorm分布实施细节。


实例----------Examples----------


q<-qnorm(p=c(0.025,0.5,0.975),mean=0,sd=2)
X11(width=9,height=6)
par(mfrow=c(2,3))
get.norm.sd(q=q)
get.norm.sd(q=q,scaleX=c(0.0001,0.9999))
get.norm.sd(q=q,fit.weights=c(10,1,10))
get.norm.sd(q=q,fit.weights=c(1,10,1))
get.norm.sd(q=q,fit.weights=c(100,1,100))
get.norm.sd(q=q,fit.weights=c(1,100,1))

q<-qnorm(p=c(0.025,0.5,0.975),mean=176,sd=15)
X11(width=9,height=6)
par(mfrow=c(2,3))
get.norm.sd(q=q)
get.norm.sd(q=q, fit.weights=c(10,1,10))
get.norm.sd(q=q, fit.weights=c(1,10,1))
get.norm.sd(q=q, fit.weights=c(100,1,100))
get.norm.sd(q=q, fit.weights=c(1,100,1))

# The estimating model is not suitable for the following quantiles.[估算模型是不适用于下列位数。]
# Because the quantile is unsymmetrical, which could not be from a normally distributed data.[由于分位数是不对称的，不能从正常分布的数据。]
q<-c(-2,30,31)
X11(width=9,height=6)
par(mfrow=c(2,3))
get.norm.sd(q=q)
get.norm.sd(q=q,fit.weights=c(10,1,10))
get.norm.sd(q=q, fit.weights=c(1,10,1),scaleX=c(0.0001,0.9999))
get.norm.sd(q=q,fit.weights=c(100,1,100))
get.norm.sd(q=q, fit.weights=c(1,100,1),scaleX=c(0.0001,0.9999))

# The estimating from an actually exponetial distributed data[估计从一个实际的数abc分布数据]
x.exp<-rexp(n=10,rate=5)
mean(x.exp)
sd(x.exp)
q<-quantile(x.exp,c(0.025,0.5,0.975))
X11(width=9,height=6)
par(mfrow=c(2,3))
get.norm.sd(q=q)
get.norm.sd(q=q,fit.weights=c(1,10,1))
get.norm.sd(q=q,fit.weights=c(10,1,10))
get.norm.sd(q=q,fit.weights=c(1,100,1))
get.norm.sd(q=q,fit.weights=c(100,1,100))

# other exmamples[其他exmamples]
q<-qnorm(p=c(0.025,0.5,0.975),mean=1,sd=1)
get.norm.sd(q=q)

q<-qnorm(p=c(0.025,0.5,0.975),mean=1,sd=0.5)
get.norm.sd(q=q)

q<-qnorm(p=c(0.025,0.5,0.975),mean=0.01,sd=0.1)
get.norm.sd(q=q)

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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