humpfit(vegan)
humpfit()所属R语言包：vegan
No-interaction Model for Hump-backed Species Richness vs. Biomass
无相互作用模型驼峰支持的物种丰富度与生物
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function humpfit fits a no-interaction model for species richness vs. biomass data (Oksanen 1996). This is a null model that produces a hump-backed response as an artifact of plant size and density.
函数humpfit符合一个没有交互物种丰富度与生物量数据模型（奥克萨宁1996年），。这是一个空的模型，该模型产生一个驼背的响应，植物的大小和密度的神器。
用法----------Usage----------
humpfit(mass, spno, family = poisson, start)
## S3 method for class 'humpfit'
summary(object, ...)
## S3 method for class 'humpfit'
predict(object, newdata = NULL, ...)
## S3 method for class 'humpfit'
plot(x, xlab = "Biomass", ylab = "Species Richness", lwd = 2,
l.col = "blue", p.col = 1, type = "b", ...)
## S3 method for class 'humpfit'
points(x, ...)
## S3 method for class 'humpfit'
lines(x, segments=101, ...)
## S3 method for class 'humpfit'
profile(fitted, parm = 1:3, alpha = 0.01, maxsteps = 20, del = zmax/5, ...)
参数----------Arguments----------
参数：mass
Biomass.
生物质。
参数：spno
Species richness.
物种丰富度。
参数：start
Vector of starting values for all three parameters.
可用于所有三个参数的初始值的向量。
参数：family
Family of error distribution. Any family can be used, but the link function is always Fisher's diversity model, and other link functions are silently ignored.
家庭的误差分布。任何family可以使用，但始终是费舍尔的多样性模型的纽带作用，和其他link功能被忽略掉。
参数：x, object, fitted
Result object of humpfit
结果对象humpfit
参数：newdata
Values of mass used in predict. The original data values are used if missing.
值mass在predict使用。如果缺少原始数据值。
参数：xlab,ylab
Axis labels in plot
轴标签在plot
参数：lwd
Line width
线宽度
参数：l.col, p.col
Line and point colour in plot
线和点的颜色plot
参数：type
Type of plot: "p" for observed points, "l" for fitted lines, "b" for both, and "n" for only setting axes.
类型plot："p"观测点，"l"拟合线，"b"都"n"只设置轴。
参数：segments
Number of segments used for fitted lines.
用于拟合线的段数。
参数：parm
Profiled parameters.
异形参数。
参数：alpha, maxsteps, del
Parameters for profiling range and density.
参数分析的范围和密度。
参数：...
Other parameters to functions.
其他函数的参数。
Details
详细信息----------Details----------
The no-interaction model assumes that the humped species richness pattern along biomass gradient is an artifact of plant size and density (Oksanen 1996). For low-biomass sites, it assumes that plants have a fixed size, and biomass increases with increasing number of plants. When the sites becomes crowded, the number of plants and species richness reaches the maximum. Higher biomass is reached by increasing the plant size, and then the number of plants and species richness will decrease. At biomasses below the hump, plant number and biomass are linearly related, and above the hump, plant number is proportional to inverse squared biomass. The number of plants is related to the number of species by the relationship (link function) from Fisher's log-series (Fisher et al. 1943).
没有交互模型假设生物质梯度隆起的物种丰富度沿是人造的工厂规模和密度（奥克萨宁1996年）。对于低生物量的网站，它假定植物具有固定的大小，和生物量的增加，越来越多的植物。当网站变得拥挤时，植物的数量和物种丰富度达到最大。达到较高的生物量是通过增加植物的大小，然后将减少的植物的数量和物种丰富度。在下面的驼峰的生物量，植物的数量和生物量是线性相关的，以上的驼峰，厂房反方生物量是成正比的。植物的数量有关的物种的数量从Fisher的log系列（Fisher等人，1943）由上述关系（link函数）。
The parameters of the model are:
该模型的参数是：
hump: the location of the hump on the biomass gradient.
hump：驼背的位置上的生物量梯度。
scale: an arbitrary multiplier to translate the biomass into virtual number of plants.
scale：任意乘数翻译的植物生物质转化为虚拟号码。
alpha: Fisher's alpha to translate the virtual number of plants into number of species.
alpha：费舍尔的alpha翻译的虚拟植物种数。
The parameters scale and alpha are intermingled and this function should not be used for estimating Fisher's alpha. Probably the only meaningful and interesting parameter is the location of the hump.
的参数scale和alpha混合功能，该功能不应该被用于估计费舍尔的alpha。也许唯一有意义和有趣的参数是的位置的hump。
Function may be very difficult to fit and easily gets trapped into local solutions, or fails with non-Poisson families, and function profile should be used to inspect the fitted models. If you have loaded package MASS, you can use functions plot.profile, pairs.profile for graphical inspection of the profiles, and confint.profile.glm for the profile based confidence intervals.
功能可能是非常难以适应，很容易陷入当地的解决方案，或将失败，非泊松家庭的，和功能profile应该被用来检查的拟合模型。如果已装入packageMASS，你可以使用函数plot.profile，pairs.profile图形检验的型材，confint.profile.glm的档案为基础的置信区间。
The original model intended to show that there is no need to speculate about "competition" and "stress" (Al-Mufti et al. 1977), but humped response can be produced as an artifact of using fixed
原来的模式表明，有没有必要猜测的“竞争”和“压力”（铝穆夫提等人，1977年），但可以使用固定的神器双峰响应
值----------Value----------
The function returns an object of class "humpfit" inheriting from class "glm". The result object has specific summary, predict, plot, points and lines methods. In addition, it can be accessed by the following methods for glm objects: AIC, extractAIC, deviance, coef, residuals.glm (except type = "partial"), fitted, and perhaps some others. In addition, function ellipse.glm (package ellipse) can be used to draw approximate confidence ellipses for pairs of parameters, if the normal assumptions look appropriate.
该函数返回一个对象的类"humpfit"继承类"glm"的。结果对象有特定的summary，predict，plot，points和lines方法。此外，它还可以通过以下方法为glm对象访问：AIC，extractAIC，deviance，coef，residuals.glm（除type = "partial"），fitted，也许还有一些其他的。此外，函数ellipse.glm（包ellipse）可以用来绘制对参数的近似置信椭圆的，如果正常的假设看合适。
注意----------Note----------
The function is a replacement for the original GLIM4 function at the archive of Journal of Ecology. There the function was represented as a mixed glm with one non-linear parameter (hump) and a special one-parameter link function from Fisher's log-series. The current function directly applies non-linear maximum likelihood fitting using function nlm. Some expected problems with the current approach are:
该功能是替代原GLIM4功能的归档生态学杂志。函数被表示为一个混合的glm与一个非线形参数（hump）和一个特殊的单参数链接功能从Fisher的log系列。目前的功能直接适用于非线性最大似然拟合的功能nlm。一些预期的问题，目前的做法是：
The function is discontinuous at hump and may be difficult to optimize in some cases (the lines will always join, but the derivative jumps).
hump该函数是不连续的，并可能是困难的，以优化在某些情况下（线将随时加入，但衍生的跳跃）。
The function does not try very hard to find sensible starting values and can fail. The user may supply starting values in argument start if fitting fails.
该功能不尝试很难找到合理的初始值，可能会失败。用户可以在参数中提供的初始值start如果装修失败。
The estimation is unconstrained, but both scale and alpha should always be positive. Perhaps they should be fitted as logarithmic. Fitting Gamma family models might become easier, too.
估计是不受约束的，但都scale和alpha应该始终是积极的。也许他们应该装有数。配件Gammafamily模型可能会变得更加容易了。
（作者）----------Author(s)----------
Jari Oksanen
参考文献----------References----------
& Band, S.R. (1977) A quantitative analysis of shoot phenology and dominance in herbaceous vegetation. Journal of Ecology 65,759–791.
between the number of species and the number of individuals in a random sample of of an animal population. Journal of Animal Ecology 12, 42–58.
and biomass an artefact due to plot size? Journal of Ecology 84, 293–295.
参见----------See Also----------
fisherfit, profile.glm,
fisherfit，profile.glm，
实例----------Examples----------
##[＃]
## Data approximated from Al-Mufti et al. (1977)[数据近似从铝穆夫提等人。 （1977）]
##[＃]
mass <- c(140,230,310,310,400,510,610,670,860,900,1050,1160,1900,2480)
spno <- c(1, 4, 3, 9, 18, 30, 20, 14, 3, 2, 3, 2, 5, 2)
sol <- humpfit(mass, spno)
summary(sol) # Almost infinite alpha...[几乎是无限的阿尔法...]
plot(sol)
# confint is in MASS, and impicitly calls profile.humpfit.[confint是在的质量和impicitly要求profile.humpfit。]
# Parameter 3 (alpha) is too extreme for profile and confint, and we[参数3（α）是太极端了配置文件和confint，我们]
# must use only "hump" and "scale".[只能使用“驼峰航线”和“规模”。]
library(MASS)
plot(profile(sol, parm=1:2))
confint(sol, parm=c(1,2))
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
|
发表于 2012-10-1 15:06:52
