fisherfit(vegan)
fisherfit()所属R语言包：vegan
Fit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
适合费舍尔的Logseries和普雷斯顿的丰度数据的对数正态模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function fisherfit fits Fisher's logseries to abundance data. Function prestonfit groups species frequencies into doubling octave classes and fits Preston's lognormal model, and function prestondistr fits the truncated lognormal model without pooling the data into octaves.
功能fisherfit符合Fisher的logseries的丰度数据。函数prestonfit群体种频率增加一倍八度音类，适合普雷斯顿的对数正态分布模型和功能prestondistr适合的数据汇集到八度音的对数正态分布模型，而无需截断。
用法----------Usage----------
fisherfit(x, ...)
## S3 method for class 'fisherfit'
confint(object, parm, level = 0.95, ...)
## S3 method for class 'fisherfit'
profile(fitted, alpha = 0.01, maxsteps = 20, del = zmax/5,
...)
prestonfit(x, tiesplit = TRUE, ...)
prestondistr(x, truncate = -1, ...)
## S3 method for class 'prestonfit'
plot(x, xlab = "Frequency", ylab = "Species", bar.col = "skyblue",
line.col = "red", lwd = 2, ...)
## S3 method for class 'prestonfit'
lines(x, line.col = "red", lwd = 2, ...)
veiledspec(x, ...)
as.fisher(x, ...)
## S3 method for class 'fisher'
plot(x, xlab = "Frequency", ylab = "Species", bar.col = "skyblue",
kind = c("bar", "hiplot", "points", "lines"), add = FALSE, ...)
as.preston(x, tiesplit = TRUE, ...)
## S3 method for class 'preston'
plot(x, xlab = "Frequency", ylab = "Species", bar.col = "skyblue", ...)
## S3 method for class 'preston'
lines(x, xadjust = 0.5, ...)
参数----------Arguments----------
参数：x
Community data vector for fitting functions or their result object for plot functions.
社区数据矢量拟合函数或对象plot功能的结果。
参数：object, fitted
Fitted model.
拟合模型。
参数：parm
Not used.
未使用。
参数：level
The confidence level required.
所需的信心水平。
参数：alpha
The extend of profiling as significance.
扩展的分析意义。
参数：maxsteps
Maximum number of steps in profiling.
在分析的步骤的最大数量。
参数：del
Step length.
步长。
参数：tiesplit
Split frequencies 1, 2, 4, 8 etc between adjacent octaves.
分割频率1, 2, 4, 8等相邻八度音阶之间的。
参数：truncate
Truncation point for log-Normal model, in log2 units. Default value -1 corresponds to the left border of zero Octave. The choice strongly influences the fitting results.
截断点对数正态模型，在LOG2单位。默认值-1对应于零点八度的左边框。选择强烈影响的拟合结果。
参数：xlab, ylab
Labels for x and y axes.
x和y轴的标签。
参数：bar.col
Colour of data bars.
数据条的颜色。
参数：line.col
Colour of fitted line.
拟合线的颜色。
参数：lwd
Width of fitted line.
拟合线的宽度。
参数：kind
Kind of plot to drawn: "bar" is similar bar plot as in plot.fisherfit, "hiplot" draws vertical lines as with plot(..., type="h"), and "points" and "lines" are obvious.
类的图得出："bar"是类似条形的图，在plot.fisherfit，"hiplot"画垂直线与plot(..., type="h")，"points"和"lines" 是显而易见的。
参数：add
Add to an existing plot.
添加到现有图。
参数：xadjust
Adjustment of horizontal positions in octaves.
八度的水平位置的调整。
参数：...
Other parameters passed to functions. Ignored in prestonfit and tiesplit passed to as.preston in prestondistr.
其他参数传递给函数。忽略的prestonfit和tiesplit传递给as.prestonprestondistr。
Details
详细信息----------Details----------
In Fisher's logarithmic series the expected number of species f with n observed individuals is f_n = α x^n / n (Fisher et al. 1943). The estimation follows Kempton & Taylor (1974) and uses function nlm. The estimation is possible only for genuine counts of individuals. The parameter α is used as a diversity index, and α and its standard error can be estimated with a separate function fisher.alpha. The parameter x is taken as a nuisance parameter which is not estimated separately but taken to be n/(n+α). Helper function as.fisher transforms abundance data into Fisher frequency table.
在Fisher的对数级数预期的物种数量fn观察到的个人f_n = α x^n / n（Fisher等人1943年）。的估计如下肯普顿 - 泰勒（1974），和使用功能nlm。估计是可能的，只有真正的个人的罪名。作为参数α多样性指数，α和标准错误地估计一个单独的函数fisher.alpha。参数x作为一个参数，这是无法估计，但采取的是n/(n+α)滋扰。助手功能as.fisher丰富的数据转换成的费舍尔频率表。
Function fisherfit estimates the standard error of alpha. However, the confidence limits cannot be directly estimated from the standard errors, but you should use function confint based on profile likelihood. Function confint uses function confint.glm of the MASS package, using profile.fisherfit for the profile likelihood. Function profile.fisherfit follows profile.glm and finds the tau parameter or signed square root of two times log-Likelihood profile. The profile can be inspected with a plot function which shows the tau and a dotted line corresponding to the Normal assumption: if standard errors can be directly used in Normal inference these two lines are similar.
函数fisherfit估计的标准误差alpha。然而，置信限不能直接估计的标准误差，但你应该使用功能confint在个人资料的可能性。函数confint使用功能confint.glm的MASS包，使用profile.fisherfit：的档案可能性。功能profile.fisherfitprofile.glm和发现tau参数，或签订两次对数似然分布的平方根。配置文件可以检查一个plot函数tau和虚线对应于正常的假设：如果标准误差可以直接使用在正常推论，这两条线是相似的。
Preston (1948) was not satisfied with Fisher's model which seemed to imply infinite species richness, and postulated that rare species is a diminishing class and most species are in the middle of frequency scale. This was achieved by collapsing higher frequency classes into wider and wider “octaves” of doubling class limits: 1, 2, 3–4, 5–8, 9–16 etc. occurrences. It seems that Preston regarded frequencies 1, 2, 4, etc.. as “tied” between octaves (Williamson & Gaston 2005). This means that only half of the species with frequency 1 are shown in the lowest octave, and the rest are transferred to the second octave. Half of the species from the second octave are transferred to the higher one as well, but this is usually not as large a number of species. This practise makes data look more lognormal by reducing the usually high lowest octaves. This can be achieved by setting argument tiesplit = TRUE. With tiesplit = FALSE the frequencies are not split, but all ones are in the lowest octave, all twos in the second, etc. Williamson & Gaston (2005) discuss alternative definitions in detail, and they should be consulted for a critical review of log-Normal model.
普雷斯顿（1948年），不满意与费舍尔的模型，这似乎意味着无限的物种丰富度，并推测，稀有物种减少类，品种最中间的频率规模。这是实现的崩溃更高的频度等级为更广泛和更广泛的“八度音阶”类限制：1，2，3-4，5-8，9-16等的发生翻一番。似乎，普雷斯顿认为频率1，2，4，等。作为“捆绑”在个八度（2005年威廉姆森和加斯东）之间。这意味着，只有一半的物种与频率1中所示的最低的八度音阶，其余的被转移到所述第二个八度音。半被转移到较高的一个，以及从所述第二个八度音的种类，但通常这是不作为大的许多物种。这种做法使得数据看起来更对数正态分布的减少通常是高的最低八度音。这样就可以实现通过设置参数tiesplit = TRUE。用tiesplit = FALSE的频率不拆，但所有的人都在最低八度，第二，等威廉姆森和加斯顿（2005年）的所有三三两两在讨论替代定义的细节，和他们应谘询的关键审查对数正态模型。
Any logseries data will look like lognormal when plotted in Preston's way. The expected frequency f at abundance octave o is defined by f = S0 exp(-(log2(o)-mu)^2/2/sigma^2), where μ is the location of the mode and σ the width, both in log2 scale, and S0 is the expected number of species at mode. The lognormal model is usually truncated on the left so that some rare species are not observed. Function prestonfit fits the truncated lognormal model as a second degree log-polynomial to the octave pooled data using Poisson (when tiesplit = FALSE) or quasi-Poisson (when tiesplit = TRUE). error. Function prestondistr fits left-truncated Normal distribution to log2 transformed non-pooled observations with direct maximization of log-likelihood. Function prestondistr is modelled after function fitdistr which can be used for alternative distribution models.
任何logseries数据看起来像对数正态分布时在普雷斯顿的方式绘制。预期的频率f丰富倍频程o被定义为f = S0 exp(-(log2(o)-mu)^2/2/sigma^2)，其中μ的位置的模式和σ的宽度，无论是在 X>的规模，和log2是预期的数量种类模式。在左侧，使一些珍稀物种，没有观察到对数正态模型通常被截断。功能S0适合被截断的对数正态分布模型作为第二学位数多项式的八度汇总数据使用泊松（prestonfit）或准泊松（tiesplit = FALSE）。错误。函数tiesplit = TRUE适合左截断正态分布prestondistr转化非池直接观测最大化对数似然。功能log2是仿照功能prestondistr可用于其他分销模式。
The functions have common print, plot and lines methods. The lines function adds the fitted curve to the octave range with line segments showing the location of the mode and the width (sd) of the response. Function as.preston transforms abundance data to octaves. Argument tiesplit will not influence the fit in prestondistr, but it will influence the barplot of the octaves.
的功能有共同的print，plot和lines方法。 lines函数添加的拟合的曲线示出的模式的位置和宽度（标准差）的响应与线段的八度范围。函数as.preston丰富的数据转换到八度音。参数tiesplit不会影响的适合在prestondistr的，但它会影响barplot的八度。
The total extrapolated richness from a fitted Preston model can be found with function veiledspec. The function accepts results both from prestonfit and from prestondistr. If veiledspec is called with a species count vector, it will internally use prestonfit. Function specpool provides alternative ways of estimating the number of unseen species. In fact, Preston's lognormal model seems to be truncated at both ends, and this may be the main reason why its result differ from lognormal models fitted in Rank–Abundance diagrams with functions rad.lognormal.
总可以找到与从一个装有普雷斯顿模型推算丰富功能veiledspec。该函数接受结果，无论是从prestonfit和prestondistr。如果veiledspec被称为一个物种数矢量，它会在内部使用prestonfit。函数specpool的估计看不见物种的数量提供了替代的方法。事实上，普雷斯顿的对数正态分布模型似乎被截断，在两端，这可能是最主要的原因，其结果不同的对数正态分布模型安装在等级丰度图的功能rad.lognormal。
值----------Value----------
The function prestonfit returns an object with fitted coefficients, and with observed (freq) and fitted (fitted) frequencies, and a string describing the fitting method. Function prestondistr omits the entry fitted. The function fisherfit returns the result of nlm, where item estimate is α. The result object is amended with the following items:
函数prestonfit返回一个对象，配有（coefficients）和安装（freq）的频率，和一个字符串来描述装修fitted method，并与观测到的 。函数prestondistr忽略了项目fitted。函数fisherfit返回的结果nlm，是项目estimate α的。结果对象进行修正，下列项目：
参数：df.residuals
Residual degrees of freedom.
剩余自由度。
参数：nuisance
Parameter x. <tr valign="top"><td>fisher</td>
参数x。 <tr valign="top"> <TD> fisher</ TD>
Observed data from as.fisher.
观测数据从as.fisher。
（作者）----------Author(s)----------
Bob O'Hara (<code>fisherfit</code>) and Jari Oksanen.
参考文献----------References----------
between the number of species and the number of individuals in a random sample of animal population. Journal of Animal Ecology 12: 42–58.
parameters as diversity discriminators for Lepidoptera. Journal of Animal Ecology 43: 381–399.
species. Ecology 29, 254–283.
not an appropriate null hypothesis for the species–abundance distribution. Journal of Animal Ecology 74, 409–422.
参见----------See Also----------
diversity, fisher.alpha, radfit, specpool. Function fitdistr of MASS package was used as the model for prestondistr. Function density can be used for smoothed “non-parametric” estimation of responses, and qqplot is an alternative, traditional and more effective way of studying concordance of observed abundances to any distribution model.
diversity，fisher.alpha，radfit，specpool。功能fitdistrMASS包被用作模型prestondistr。功能density可以用于平滑“非参数估计的响应，qqplot是一个另类的，传统的和更有效的方法观测到的丰度的任何分配模型的研究一致。
实例----------Examples----------
data(BCI)
mod <- fisherfit(BCI[5,])
mod
plot(profile(mod))
confint(mod)
# prestonfit seems to need large samples[prestonfit似乎需要大样本]
mod.oct <- prestonfit(colSums(BCI))
mod.ll <- prestondistr(colSums(BCI))
mod.oct
mod.ll
plot(mod.oct)
lines(mod.ll, line.col="blue3") # Different[不同]
## Smoothed density[＃平滑密度]
den <- density(log2(colSums(BCI)))
lines(den$x, ncol(BCI)*den$y, lwd=2) # Fairly similar to mod.oct[相当类似mod.oct的]
## Extrapolated richness[＃推算丰富]
veiledspec(mod.oct)
veiledspec(mod.ll)
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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 2012-10-1 15:06:31
