get.norm.par(rriskDistributions)
get.norm.par()所属R语言包：rriskDistributions
Fitting parameters of normal distribution from two or more quantiles
从两个或多个分位数正态分布拟合参数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
get.norm.par returns the parameters of a normal distribution where the pth percentiles match with the quantiles q.
get.norm.par返回的正常分布的参数，其中p百分位匹配位数q。
用法----------Usage----------
get.norm.par(p=c(0.025,0.5,0.975), q, show.output=TRUE,
plot=TRUE, tol=0.001,
fit.weights=rep(1,length(p)),scaleX=c(0.1,0.9),...)
参数----------Arguments----------
参数：p
numeric, single value or vector of probabilities.
数字，单值或向量的概率。
参数：q
numeric, single value or vector of quantiles corresponding to p.
数字，位数对应于P的单个值或向量。
参数：show.output
logical, if TRUE the optim result will be printed (default value is TRUE).
逻辑，如果TRUEoptim的结果将被打印（默认值是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：tol
numeric, single positive value giving the absolute convergence tolerance for reaching zero (default value is 0.001).
数字，单正的价值绝对收敛公差达到零（默认值是0.001）。
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, the number of quantiles and the number of weightings must be identical and should be at least two. Using the default p, the three corresponding quantiles are the 2.5th percentile, the median and the 97.5th percentile, respectively. get.norm.par uses the R function optim with the method L-BFGS-B. If this method fails the optimization method BFGS will be invoked. <br> <br> If show.output=TRUE the output of the function optim will be shown. The item convergence equal to 0 means the successful completion of the optimization procedure, otherwise it indicates a convergence error. The item value displays the achieved minimal value of the functions that were minimized. <br> <br> The estimated distribution parameters returned by the function optim are accepted if the achieved value of the minimized function (output component value of optim) is smaller than the argument tol. <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 which will be minimized is defined as a sum of squared differences between the given probabilities and the theoretical probabilities of the specified distribution evaluated at the given quantile points (least squares estimation).
数目的概率，分位数和比重的数目的数目必须是相同的，并且应该是至少两个。使用默认的p，相应的三个位数的第2.5个百分位数，中位数和第97.5个百分位数，分别为。 get.norm.par使用R函数optim的方法L-BFGS-B。如果这种方法失败的优化方法BFGS将被调用。参考<br>如果show.output=TRUE输出的功能optim将显示。的资料convergence等于0的装置的优化过程的成功完成，否则表示会聚误差。资料value显示取得极小值的最小化的功能，。参考参考的估计分布参数的功能optim返回接受，如果实现最小化的函数的值（输出组件valueoptim）小于参数的 tol。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>该将被最小化的函数，它被定义为给定的概率之间的平方差的总和，并评估在给定的位数点（最小二乘估计）的指定的分布的理论概率。
值----------Value----------
Returns fitted parameters of a normal distribution or missing values (NA's) if the distribution cannot fit the specified quantiles.
返回拟合参数的正常分布或遗漏值（NA）的分布不符合指定的位数。
注意----------Note----------
it should be noted that there might be deviations between the estimated and the theoretical distribution parameters in certain circumstances. This is because the estimation of the parameters is based on a numerical optimization method and depends strongly on the initial values. In addition, one must always keep in mind that a distribution for different combinations of parameters may look very similar. Therefore, the optimization method cannot always find the "right" distribution, but a "similar" one. <br> <br> If the function terminates with the error massage "convergence error occured or specified tolerance not achieved", one may try to set the convergence tolerance to a higher value. It is yet to be noted, that good till very good fits of parameters could only be obtained for tolerance values that are smaller than 0.001.
应当指出，有可能会对估计和在某些情况下的理论分布参数之间的偏差。这是因为估计的参数的数值优化方法的基础上，并强烈地依赖于初始值。此外，我们必须始终牢记，分配不同的参数组合看起来非常相似。因此，优化方法并不总是可以找到“正确”的分布，但“类似”。参考<br>如果函数终止“的错误按摩收敛发生错误或指定的公差未实现”，一个可以尝试，设置收敛到一个更高的值的耐受性。是尚未应注意，好至很好的适合的参数只能得到公差值小于0.001。
（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=12,sd=34)
X11(width=9,height=6)
par(mfrow=c(2,3))
get.norm.par(q=q)
get.norm.par(q=q,scaleX=c(0.00001,0.99999))
get.norm.par(q=q, fit.weights=c(10,1,10))
get.norm.par(q=q, fit.weights = c(1,10,1))
get.norm.par(q=q, fit.weights=c(100,1,100))
get.norm.par(q=q, fit.weights = c(1,100,1))
q<-qnorm(p=c(0.025,0.5,0.975),mean=0,sd=1)
X11(width=9,height=6)
par(mfrow=c(2,3))
get.norm.par(q=q)
get.norm.par(q=q, fit.weights=c(10,1,10))
get.norm.par(q=q, fit.weights = c(1,10,1))
get.norm.par(q=q, fit.weights=c(100,1,100))
get.norm.par(q=q, fit.weights = c(1,100,1))
q<-qnorm(p=c(0.025,0.5,0.975),mean=0.1,sd=0.1)
X11(width=9,height=6)
par(mfrow=c(2,3))
get.norm.par(q=q)
get.norm.par(q=q, fit.weights=c(10,1,10))
get.norm.par(q=q, fit.weights = c(1,10,1))
get.norm.par(q=q, fit.weights=c(100,1,100))
get.norm.par(q=q, fit.weights = c(1,100,1))
# example with only two quantiles[例如，只有两个位数]
q<-qnorm(p=c(0.025,0.975),mean=12,sd=34)
X11(width=9,height=6)
par(mfrow=c(2,3))
get.norm.par(p=c(0.025,0.975),q=q)
get.norm.par(p=c(0.025,0.975),q=q,fit.weights=c(10,1))
get.norm.par(p=c(0.025,0.975),q=q,fit.weights=c(100,1))
get.norm.par(p=c(0.025,0.975),q=q,fit.weights=c(1,10))
get.norm.par(p=c(0.025,0.975),q=q,fit.weights=c(1,100))
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 2012-9-28 21:11:56
